Coal Combustion Research [1 ed.] 9781616686468, 9781616684235

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ENERGY SCIENCE, ENGINEERING AND TECHNOLOGY

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COAL COMBUSTION RESEARCH

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ENERGY SCIENCE, ENGINEERING AND TECHNOLOGY

COAL COMBUSTION RESEARCH

CHRISTOPHER T. GRACE

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EDITOR

Nova Science Publishers, Inc. New York

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Copyright © 2010 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com

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NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Coal combustion research / [edited by] Christopher T. Grace. p. cm. Includes index. ISBN  (HERRN) 1. Coal--Combustion. I. Grace, Christopher T. TP325.C514856 2009 662.6'22--dc22 2010004371

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CONTENTS Preface Chapter 1

Advances in Coal Combustion Research Juwei Zhang

Chapter 2

Fundamental Research on Oxy-Fuel Combustion: The Nox and Coal Ignition Reactions, Part I Masayuki Taniguchi and Kenji Yamamoto

39

Fundamental Research for Oxyfuel Combustion: NOx Reaction and Coal Ignition, Part II Masayuki Taniguchi

75

Developments in Nox Emission Control by ‘Reburning’ in Pulverised Coal Combustion Bill Nimmo and Hao Liu

85

Chapter 3

Chapter 4

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vii

Chapter 5

Chapter 6

Chapter 7

Mineral Transformation and Ash Deposit during Coal Combustion Yongchun Zhao, Junying Zhang and Chuguang Zheng Possible Involvement of Ethnicity and Clan Consanguinity in the Modulation of Arseniasis Risk in a Multiethnic, Hyper-Endemic Village Exposed to Indoor Combustion of High Arsenic-Content Coal Jian-hua Shen, Guo-fang Lin, Hui Du, Hong-chao Lu, Klaus Golka and Ji-gang Chen Pyrogenic Metamorphism of the Carbonaceous Rocks in the South of the Siberian Platform N.I. Akulov, V.V. Akulova and E.V. Khudonogova

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1

171

205

219

vi Chapter 8

Contents Short Communication: Influence of a Coal-Fired Power Plant on Terrestrial Biota at Candiota, South of Brazil A. M. Divan Jr., P. L. Oliveira,V. Schmidt, J. S. Bernardo-Silva, R. Hentschel, B. Darski-Silva, M. T. Raya-Rodriguez and S. M. Hartz

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Index

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PREFACE Coal is widely known as being most abundant fossil fuel, as well as being the most prevalent method to obtain energy in the world. Though coal combustion has been studied extensively for more than a century, there are still a number of unknown issues to be explored. Generally, coal combustion includes three main processes: devolatiliztion, combustion of volatiles, and coal char combustion. This book presents and discusses topical data on coal combustion, such as: the mechanisms of coal devolatilization and the influences of several heating conditions; combustion models for the NOx and coal ignition reactions as well as an oxy-fuel combustion system; and mineral transformation and ash deposit during coal combustion. Chapter 1 - This chapter is concerned with the current development of the study on coal combustion, which mainly involves devolatilization, volatiles combustion, char combustion and the formation of pollutants. This paper focuses on devolatilization, char combustion and the formation of pollutants. Regarding the devolatilization of coal, it is well-known that devolatilization has a significant impact on the other processes during coal combustion. This paper firstly discusses the mechanism of coal devolatilization, then, the influences of several heating conditions such as temperature, heating rate, pressure and particle size on the yield of volatiles and the structure development of char structure during devolatilization, and finally, the introduction and comparison of different devolatilization models. The process of char combustion is of central importance in practical pulverized applications. However, the mechanism of char combustion has not yet been completely understood due to the existence of many factors such as the thermal annealing of char, the pore structure, inherent mineral content in char and the fragmentation of the char particle. This chapter presents a detailed discussion on the intrinsic reactivity and thermal annealing of char, as well as the comparison of different char combustion models. The pollutants derived from coal combustion process are the primary source of environmental pollution. Therefore, the study on the formation of these pollutants can not be ignored. The discussion in this part focuses on the formation of NOx which is the most important pollutants during coal combustion, and some up-to-date research results from our laboratory are also included. Chapter 2 - At COP13 held in Bali in December-2007, the framework for cubing-global warming were taken after 2013. The development of the technologies that are necessary for cubing-global warming must be accelerated through worldwide cooperation.

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Christopher T. Grace

Chapter 3 - Carbon dioxide capture and permanent underground storage (CCS) will be necessary. Oxyfuel combustion (CO2/O2 combustion) is one of the promising CCS technologies for avoidance of global warning of coal power plant. For oxyfuel combustion, composition and specific heat of the combustion supporting gas are different from those for air combustion. The combustion performance varies with the differences. In the present investigation, we focused NOx reaction and coal ignition. Chapter 4 - The combustion of pulverised coal for power generation will continue to be a major source of energy for the foreseeable future but with advanced designs for improvements in efficiency and emission control. There are several techniques which may be employed for NOx emission control including low NOx burner design, air-staging, selective catalytic reduction (SCR) and selective non-catalytic reduction (SNCR). Combustion modification by fuel staging can also reduce NOx by a reburning chemical process whereby fuel-rich conditions are generated within the coal flame inducing the formation of N2 from NOx and suppressing the oxidation of fuel-nitrogen. The secondary fuel that is used in the process may be any fuel, solid, liquid or gas, that can release significant quantities of hydrocarbon radicals, CHi, under oxygen deficient combustion conditions which then undergo a series of reactions reducing NOx to N2. High levels of NOx reduction may be achieved with this process but care must be taken to maintain high char burnout efficiencies if high carbon in ash levels are to be avoided. In this regards some fuels perform better than others and therefore have found more favour within the power generation industry. In addition to coal itself, interest in utilising waste fuels has inspired investigation into the use of pyrolysis gas, waste plastics, tyre rubber, biomass and oils as reburn fuels. The object of this chapter is to document the latest advances in NOx Reburning and Advanced Reburning technologies and perform a critical review on relative performances for use as a guide to fuel selection and process optimisation. Data from studies around the world will be digested along with pioneering work at The University of Leeds. Chapter 5 - Inorganic components and minerals in coals were recognized as being the main source of ash deposit. In this paper, the mineral transformation during coal combustion was summarized. Five coal samples were collected from typical coal mines and power plants in China to investigate mineral transformation during coal combustion. The thermal-gravity curves of the low temperature ashes of coals were analyzed to study the thermal behavior of minerals. The results indicate that boehmite, kaolinite, and rutile are refractory minerals. Most of the iron-bearing mineral transformed into iron oxide. Ca-Mg silicate was detected in the ash when it closes to be melt. To understand the physico-chemical characteristics of ash deposit, six samples of ash deposits were collected from a typical coal-fired power plant (Zhuzhou Power Plant) in Hunan, China. X-ray diffraction (XRD), field emission scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy (FSEM-EDX), optical microscopy, and X-ray fluorescence (XRF) were used to analyze mineralogy, chemical composition, and microstructure of ash deposits, which would provide valuable information for the elucidation of ash deposit formation mechanism. The results show that, the ash deposit is mainly composed of silica amorphous phases, the chemical compositions of different layers vary from each other. The identified minerals include mullite, cristobalite, hematite, quartz, hercynite, and anorthite. The silica-rich glass phases are derived from volatilization-recondensation of SiO and the interaction between aluminosilicates and other minerals. Several types of crystals were identified in the ash deposits, including iron-oxides crystals, Fe-Ca-bearing phases, Si-riched phases, and aluminosilicate phases. The deposition

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of particles as well as the following melting is the main reason for the porosity structure of deposit. The interaction and eutectic of minerals in coals during combustion result in the serious deposition in the coal-fired power plant. Chapter 6 - Inorganic arsenic compounds are listed by IARC as an evident human carcinogen (Group 1) (IARC 1987). Most of the environmental arsenic poisoning (other than occupational exposure) cases reported so far were associated with the daily exposure via drinking water naturally contaminated from arsenic-rich geological formations. Arseniasis due to drinking water naturally contaminated from arsenic-rich geological formations is a serious problem for about 2,343,000 rural residents living in 583 villages, 40 counties, in 7 different provinces and regions in Chinese mainland (Xinjiang, Inner Mongolia, Shanxi, Jilin, Ningxia, Qinghai, Anhui) (Jin et al. 2003) and some other countries (Bangladesh, India, Argentina, Mexico, Chile, Thailand， etc.) as well (WHO 2001; Chen et al. 2005; Ferreccio et al. 2000; Hopenhayn-Rich et al. 1996a,1996b,1998; Tondel et al. 1999). Chapter 7 - Coal combustion is rather common natural phenomenon. Pyrogenic metamorphism-related events occurred many times during the Earth’s long history (Bentor & Kastner, 1976; Bustin & Mathews, 1982; Church et al., 1979; Chesnokov & Sherbakova, 1991; Cosca et al., 1989; Essene et al., 1984; Fermor, 1918; McLintock, 1932; Rattigan, 1967; Sokol et al., 1998; Stracher & Taylor, 2004; Tilley, 1924; Venkatesh, 1952; and others). The products of oxidation and combustion within the burning coal-waste heaps (terricones) are a serious problem because they exert an adverse effect on the atmosphere, soils, and surface and ground water resources. However, they are of considerable practical importance, and the coal-waste heaps alone are compact natural laboratories to study a whole variety of previous and current geological and geochemical processes of pyrogenic metamorphism. The burnt rocks have a wide use in road filling and in building industry for building material production, whereas coal ash is used for the extraction of rare metals: germanium, gallium, titanium, and vanadium. The burnt rocks have been also used in gemology. Pyrogenic metamorphism has recently been the subject of some interesting investigations resulted in discovery of new minerals and solutions for complex problems of pyrogenic mineralogy (Chesnokov, 2001; Heffern & Coates, 2004; Panov, 2004; Potapov & Maksimovich, 2006; Sharygin et al., 2009; Sokol et al., 2000; 2005; Stracher & Taylor, 2004; Stracher, 2007; Zborshik & Osokin, 2000, 2004; and others). Nevertheless, in the context of geology pyrogenesis is poorly known. The lack of lithologic basis for the analysis of trends in natural types of pyrogenesis does not allow comparison between the processes of lithogenesis and pyrogenesis. The purpose of this work has been to study the most important products of pyrogenic metamorphism of the carbonaceous deposits of the Siberian platform and their formation peculiarities making comparison with the basic stages of lithogenesis. Chapter 8 - A study was carried out to examine and compare the floristic composition of grassland sites exposed to fly ash emitted by a coal-fired power plant in Southern Brazil. The impact of the thermal plant on vicinity environment was evaluated for two years (2007– 2009), by means of studying heavy metal content in wild plant species, richness of plant species, amphibians species and reptiles species and oral pathologies of sheep. In addition, the

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foliose lichen species, Heterodermia cf. obscurata, transplanted from an unpolluted site was exposed at the same sampling areas and their heavy metal content evaluated. The vegetation is composed primarily of grasslands with prominence of species Poaceae and Asteraceae plant families. In all, 165 plant species, mainly herbaceous ones, were found. Besides changes of floristic composition associated with seasonal changes, we observed a significant reduction in plant species richness in a site situated around 6 km from the power plant in the prevailing winds direction. Among the herbaceous plant species evaluated, Elephantopus mollis (Asteraceae) presented the highest content of cadmium and zinc and Paspalum notatum (Poaceae) the highest content of lead. The sampling areas, located in the prevailing wind direction, presented the highest level of metal accumulation. Moreover, statistically significant negative correlations were found between zinc content of E. mollis and cadmium and zinc content of foliose lichen and the plant species richness of sampling areas around the thermal plant. A total of 17 amphibians composed mainly of Hylidae and Lepdodactylidae families and 12 reptiles species with prominence of Colubridae family were found. A direct association was not found between fly ash emitted by coal-fired on herpetofauna richness. Among the oral pathologies in sheep herds, there was a high prevalence of excessive tooth wear, both in young animals as well as the old. There was a positive association between the intense occurrence of tooth wear and the bioaccumulation factor of cadmium in E. mollis. These results put in evidence the influence of coal-fired power plant emissions on terrestrial biota.

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Chapter 1

ADVANCES IN COAL COMBUSTION RESEARCH Juwei Zhang State key Laboratory of Multi-Phase Complex System, Institute of Process Engineering, Chinese Academy of Sciences, P.O. Box 353, Beijing 100190, P.R. China.

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ABSTRACT This chapter is concerned with the current development of the study on coal combustion, which mainly involves devolatilization, volatiles combustion, char combustion and the formation of pollutants. This paper focuses on devolatilization, char combustion and the formation of pollutants. Regarding the devolatilization of coal, it is well-known that devolatilization has a significant impact on the other processes during coal combustion. This paper firstly discusses the mechanism of coal devolatilization, then, the influences of several heating conditions such as temperature, heating rate, pressure and particle size on the yield of volatiles and the structure development of char structure during devolatilization, and finally, the introduction and comparison of different devolatilization models. The process of char combustion is of central importance in practical pulverized applications. However, the mechanism of char combustion has not yet been completely understood due to the existence of many factors such as the thermal annealing of char, the pore structure, inherent mineral content in char and the fragmentation of the char particle. This chapter presents a detailed discussion on the intrinsic reactivity and thermal annealing of char, as well as the comparison of different char combustion models. The pollutants derived from coal combustion process are the primary source of environmental pollution. Therefore, the study on the formation of these pollutants can not be ignored. The discussion in this part focuses on the formation of NOx which is the most important pollutants during coal combustion, and some up-to-date research results from our laboratory are also included.

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INTRODUCTION As we all know, coal is the most abundant fossil fuel and coal combustion is the most prevalent method to obtain the energy all over the word. Though coal combustion has been studied extensively for more than a century, there are still a number of unknown issues to be explored. Generally, coal combustion includes three main processes, devolatilization, combustion of volatiles, and coal char combustion. The devolatilization and char combustion are the vital aspects discussed in this chapter, while the homogeneous combustion of volatiles is not involved due to its relative simplicity.

1. DEVOLATILIZATION OF COAL Devolatilization is the first step in coal combustion. Although coal devolatilization occurs on a time scale (up to several hundreds milliseconds), much shorter than the subsequent char oxidation process (0.5 to 2 seconds for pulverized coal), it has a significant impact on the overall combustion efficiency and pollutant production in industrial coal combustion furnaces. Therefore, it is very important to understand the mechanism of devolatilization. Coal devolatilization is different from pyrolysis occurring under inert gas. However, coal devolatilization is usually not separated from pyrolysis due to the similar behaviours of coal in the two processes in terms of char chemistry and the composition of volatile matter. Therefore, “pyrolysis” will be used to substitute “devolatilization” in the following discussion of this chapter.

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1.1. Basic Mechanism of Coal Pyrolysis Coal pyrolysis has been studied extensively for a long time. However, no general mechanism is accepted universally. The observed phenomena during coal pyrolysis are not only determined by the chemical structure of the coal, but also influenced by physical properties and operating conditions. Solomon [1] divided this complicated pyrolysis process into the following nine steps, disruption of hydrogen bonds (step 1), vaporization and transport of noncovalently bonded “guest” molecules (molecular phase) (step 2), low temperature crosslinking in coals with more than 10% oxygen content(step 3), bridge breaking to fragment with macro-molecular network (step 4), hydrogen utilization to stabilize free radicals (step 5), vaporization and gas phase transport of light fragments (step 6), moderate temperature crosslinking to resolidify the macromolecular network (step 7), decomposition of functional groups to produce light species (step 8), and high temperature condensation of the macro-molecular network by hydrogen elimination (step 9). During these pyrolysis steps, some typical reactions are involved as the increase of temperature [1]: R－CH2－R＇→ R－R＇+ －CH2

(R1)

－CH2 + 2H＇→ CH4

(R2)

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－OH + H＇→ H2O

(R3)

－R－CH2 + H＇→ R－CH3

(R4)

R－OH + H－R → R－R + H2O

(R5)

R－H + H－R＇→ R－R＇(coke)+ H2

(R6)

R－COOH → R－H + CO2

(R7)

In these reactions, R radical is obtained from benze, naphthalene, and phenantherene, etc. R1 is the cracking of weak aliphatic bridge between the ring systems, resulting in the formation of free radical group such as－CH2, H＇, R2 and R3 are the combination of these free radicals, resulting in the formation of the methane and water, R4 is the formation of tar, R5 and R6 are the condensation (crossing-linking) reaction, and R7 is the oxide of carbon. The reactions corresponding to different pyrolysis steps are listed in Table 1. Nevertheless, the nine pyrolysis steps can also be divided into three main processes [2]. (1) Upon heating, coal undergoes mild changes, including the disruption of hydrogen bonds, vaporization and release of certain noncovalently bonded molecules, and low temperature crosslinking (large aromatic fragments attaching together) in low rank coals with more than 10% oxygen [1]. (2) During primary pyrolysis at higher temperatures (500–1000 K), the weak aliphatic bridges connecting large aromatic clusters in the coal matrix are cleaved to produce molecular fragments. Those fragments containing one to several aromatic ring structures will be released as tar if their vapor pressure is sufficiently high to escape the coal matrix. The larger fragments, too large to vaporize, will eventually undergo moderate temperature “crosslinking” reactions to attach to the char. At the same time, the release of some functional groups attached to the aromatic clusters and some labile bridges leads to the formation of light gases, including CO, CO2 and light hydrocarbons. (3) Secondary pyrolysis initiates when the tar and certain light gases (such as benzene and acetylene) begin to undergo further reactions in the gas phase.

1.2. Research Apparatus of Pyrolysis Experiments Various experimental apparatus have been used to study coal pyrolysis. Each kind of apparatus has its advantages and disadvantages. This section will introduce several apparatus which is widely used in coal pyrolysis experiments. As a simple and convenient device, thermogrametric analyzer (TGA) is used most frequently in pyrolysis experiments. The mass, temperature, and residence time of coal sample can be accurately determined through TGA so that the reliable reaction kinetics can be obtained. However, the data obtained under slow heating rate (3–200 K/min) condition in TGA can not be simply extrapolated to high heating rate condition such as pulverized coal combustion (>104 K/min). Therefore, the applicability of TGA data should be further validated. In addition, the bulk diffusion can not be eliminated easily due to the pile state of coal sample in TGA.

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Juwei Zhang Table 1. The reactions corresponding to different pyrolysis steps

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Step number 1 2 3 4 5 6 7 8 9

Main products free radicals free radicals Coke, CO2, H2O, free radicals tar tar Coke, CH4,, H2O CO2, CH4, H2O H2

Reactions R1 R1 R5 R1 R2,R3 － R5 R7 R6

In order to simulate the suspension combustion of coal sample, drop tube furnace (DTF) is widely used. In DTF, coal particles are entrained by carrier gas (inert gas in pyrolysis experiments) into a high-temperature zone created by a heated tube. The residence time can be regulated by the velocity variations of carrier gas and the position of sampling probe. It is obvious that DTF can provide high heating rate (~105 K/s) and high temperature (~1900 K). Nevertheless, it is very difficulty to measure or calculate the temperature history of coal particles which is affected by various factors such as the heat transfer limitations and specific heat variation of coal particles, etc. Another disadvantage of DTF is the occurrence of secondary reaction of volatiles. Some pyrolysis experiments have been done on electrically heated wire mesh (HWM) which can provide a high heating rate as high as 103 K/s. Although the heating rate of HWM is evidently lower than that of DTF, HWM is more convenient and efficient to operate. The advantage of this reactor is that the pyrolysis time of coal particles can be accurately controlled and good mass balance can be guaranteed. Being similar to DTF, the temperature history of coal particles are also not be well known. Flat flame reactor (FFR) is also applied in several pyrolysis studies. In FFR, coal particles are injected into a high-temperature post-flame gas environment which is formed by the fuelrich combustion of some combustible gas such as carbon monoxide, methane, and hydrogen etc. Utilizing the combustion of these combustible gas in a flat flame burner, FFR can provide very high temperature (~ 2000 ) and high heating rate (~ 105 /s). This method can simulate the most actual temperature and gas environment of industrial pulverized coal combustion. The disadvantage of this method is that the pyrolysis products are very difficulty to be distinguished from the post-flame gas. In addition, the temperature history and residence time of coal particles are also very difficulty to determine. In addition to the four experimental apparatus mentioned above, there are also several experimental apparatus designed to study coal pyrolysis such as heated tube reactor, transparent wall reactor, well-stirred reactor, radiative heating reactor, fluidized bed, fixed bed, and curie-point heating reactor etc. The comprehensive description of the operating principle, advantages and disadvantages of these experimental apparatus can be referred to the excellent review of Solomon [1].

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1.3. Factors Influencing Pyrolysis Behavior With respect to coal pyrolysis, there are always some traditional hot topics in literature such as the products distribution, char structure, and pyrolysis model etc. This section will review the advances in these aspects in the past fifteen years. It is well-known that the products distribution and char structure are closely associated with heating conditions such as temperature, heating rate, pressure etc.. Therefore, this section will discuss the effect of these heating conditions on coal pyrolysis one by one. 16

Char Tar HCG CO CO2

90

12

H2O

80

Char yield (%)

14

10

70

8

60

6

Tar and gas yield (%)

100

4 50 2 40 200

300

400

500

600

700

800

900

0 1000

Temperature (℃ )

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Figure 1. Products yield as a function of coal pyrolysis temperature. Pyrolysis experiments were carried out at 3000 K/s at different temperatures in a Curie-point reactor with an Australian brown coal [3].

1.3.1. Effect of temperature Figure 1 shows the effect of temperature on pyrolysis products yield. Pyrolysis products mainly consist of char, tar, hydrocarbon gases (HCG: C1–C6 gaseous compounds, benzene, toluene and xylene) and inorganic gases (H2, CO, CO2 and H2O). It can be seen that the yields of almost all products increase with the increase of temperature except char. It is no doubt that the yields of total volatiles (sum of hydrocarbon gases, inorganic gases and tar) increase monotonously with the increase of temperature, while the yields of tar, CO2 level off between 800 and 900 . Large amount of studies [4] observed that temperature has a significant influence on char physical structure. Earlier works [5,6] have found that char morphology changed to structures with larger central pore and networks voids at increasing temperature then the char microporosity and surface area of char decreased with increasing temperature. In order to find the intrinsic cause of these observations, some more basic research was carried out. Lu et al. [8,11] used quantitative X-ray diffraction (XRD), high-resolution transmission electron microscopy (TEM) and high-resolution field emission scanning electron microscopy (FESEM) to characterize the structure of chars prepared at different temperatures, and found that char structure became more ordered with increasing pyrolysis temperature. Figure 2 shows the effect of temperature on aromaticity and crystallite size of char. The aromaticity denotes the ratio of carbon atom in aromatic rings to aliphatic side chains. As shown in Figure

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Juwei Zhang

2, the aromaticity and average crystallite size of char both increases with pyrolysis temperature. This phenomenon means that increasing pyrolysis temperature can promote the growth of crystallites and the increase of aromatic carbon in chars, then resulting in the decrease of char reactivity.

1.3.2. Effect of heating rate 86

10

Acromaticity Crystallite size

82

8

Acromaticity (%)

80 6 78 76 4 74 72

Crystallite size (A)

84

2

70 68 1100

0 1200

1300

1400

1500

1600

1700

1800

Temperature (K)

Figure 2. Char structure as a functional of pyrolysis temperature. The data were obtained from XRD profiles of chars prepared at different temperatures in DTF [11]. 55

Total volatiles Tar

50

40

Yield (%)

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45

35 30 25 20 15 1

10

100

1000

Heating rate (K/s)

Figure 3. Products yield as a function of heating rate in coal pyrolysis. Experiments were carried out in a wire-mesh reactor for Illinois No. 6 (SBN) coal at 1273K and 2s hold in He [11].

Heating rate also has a great impact on pyrolysis products yield, char structure and char reactivity. Figure 3 shows the effect of heating rate on coal pyrolysis products yield. It can be seen that increasing the heating rate from 2 to 5000 K/s results in the increase of the volatiles yield from 43.4 to 53.3 % while the tar yield increases from 16.6 to 28.1 %. The fact that the variation trend line of the volatiles is parallel with that of tar indicates that the increased yield of volatiles obtained by increasing the heating rate is primarily ascribed to a higher yield of

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tar. These phenomena can show that a high heating rate results in a more extensive thermal fragmentation of coal molecule structure and suppresses secondary reaction. In most cases, the amount of volatiles obtained at a high heating rate often exceeds that obtained with standard proximate analysis. 60

Char Tar Gas Liquid

50

Yield (%)

40

30

20

10

0

1

2

3

Pressure (MPa)

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Figure 4. Products yield as a function of pyrolysis pressure. Experiments were carried out in a DTF for a subbituminous coal at 1073 K hold in He [12].

Some earlier studies [13,14,15] have found that chars prepared at high heating rate contained more miro-pores and meso-pores and had a greater internal surface area resulting in a higher reactivity. Matsuoka et al. [16] found that high porous char particle was found at 104 K/s while no porous char was observed at 25 K/s irrespective of pressure. At low heating rates (e. g. in TGA, or ~1 K/s), volatiles can diffuse through the pores without causing an internal pressure high enough to cause the particle to swell. At moderate heating rates (e. g. in a DTF, or 104 K/s), the volatiles formed in the particle interior are formed faster than those escaping through the pores, and swelling occurs if the particles has softened. At high heating rates (such as FFR, up to 105 K/s), the volatiles are formed faster than what the swelling process can accommodate, and the bubbles burst. However, Gale et al. [17] found that, a further increase in the heating rate leads to a drastically decrease of porosity and swelling, if the heating rate is higher than 2×104 K/s. Thus, there is an effect value window for heating rate which can result in a higher reactivity, and the different coal rank and maceral should also be taken into account.

1.3.3. Effect of pressure It is well recognized that the volatiles release decreases with pressure. Figure 4 shows the effect of pressure on pyrolysis products yield. With the increase of pressure, char yield increased, and tar yield decreased in opposite, while the gas and liquid yields barely changed. This can be attributed to the fact that the vapor pressure of some tar and liquid were gradually lower than experimental pressure and more and more tar and liquid were remained in the char with the increase of experimental pressure. In a word, the variation of product yields with pyrolysis is not obvious under the experimental conditions involved in Figure 4.

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Figure 5 shows the effect of pressure on the volatiles yield and char porosity. Both the volatiles yield and char porosity decrease evidently in the pressure range of 0.1–1.5 MPa, then level off at the pressure above 1.5 MPa. From Figure 5, the conclusion that the volatiles yield, char porosity and pyrolysis pressure are associated closely with each other can be drawn.

1.4. Pyrolysis Model 1.4.1. Simple kinetic model Various pyrolysis models were proposed to predict the coal pyrolysis process under different conditions, among which the empirical global kinetic model is the simplest. This type of model simply associates the weight loss with temperature through Arrhenius expressions, and can be divided into single-rate and multiple-step models. Such models are only strictly applicable to homogeneous systems in which the decomposition from the source m0 is via a single chemical process with a single activation. The single-rate model can be described as follows: Coal

k1

X Volatiles + (1-X)Char

(1)

where X is the volatile fraction and k1 is the rate constant which can be expressed in Arrhenius form: (2)

where A1 is the pre-exponential (s-1), E1 is the activation energy (kJmol-1), R is the gas constant (kJmol-1k-1) and T is the absolute temperature of coal particle (K). In this model, the rate of total volatile or species evolved dm / dt is proportional to the instantaneous values of the source: 50 49

Volatiles yield

34

Char porosity

33

48 32 47 46 30 45 29 44

Porosity (%)

31

Yield (%)

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k1 = A1 exp(− E1 / RT ) .

28 43 27 42 26 41 0.0

0.5

1.0

1.5

2.0

2.5

3.0

Pressure (MPa)

Figure 5. Volatiles yield and char porosity as a function of pyrolysis pressure. Experiments were carried out in a fixed bed reactor for a sub-bituminous coal at 1373 K and 90 s hold in He [18]. Coal Combustion Research, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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dm / dt = k1 (m0 − m)

9 (3)

The two-competing-rate model (one kind of multiple-step models) can be described as follows: k1

Volatile1 + Residue1

k2

Volatile2 + Residue2

Coal

(4)

where k1 and k2 are rate constants which can also be expressed in Arrhenius form: k1 = A1 exp(− E1 / RT ) and k2 = A2 exp(− E2 / RT ) . The relative magnitude of A1 and A2 and E1 and E2 are determined by the extent of overall volatile formation as a function of the heating rate. Therefore, the influence of heating rate on volatiles fraction is taken into account in the two-competing-rate model. In order to take the inhomogeneity of coal particle into account, the more complicated distributed-activation energy models were proposed. This type of models assumes that the coal particle is inhomogeneous, and the pyrolysis occurs through several first order reactions which have a distribution of activation energies. The distribution is often described by the Gaussian function: m( E ) =

⎛ − ( E − E0 ) 2 ⎞ m0 exp ⎜ ⎟ 1/2 2σ 2 σ (2π ) ⎝ ⎠

where m ( E ) is the amount of the ultimate yield,

σ

(5)

is the width of the distribution, and E0

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is the mean activation energy.

1.4.2. Network model The above mentioned pyrolysis model have some fatal disadvantages. Firstly, these models have no generality, since the kinetic parameters are associated closely with the coal rank. Secondly, they can only predict the overall amount of volatiles, rather than the specific composition of volatiles. However, the compositions of volatiles must be known in the prediction of coal combustion and gasification. Up to now, several models which are based on the fundamental understanding of coal structure are proposed. These models are called network models which can be used to predict the composition of key species and the residual char structure, and these results can be applicable to different rank of coals in theory. Three commercial network models, such as fuctional group-depolymerization, vaporization and crosslinking (FG-DVC) model, chemical percolation model for devolatilization (CPD) model, and FLASHCHAIN were used most extensively. The physical and chemical phenomena during coal pyrolysis were both taken into consideration in the three networks models. FG-DVC is proposed by Solomon et al. [19,20,21]. In FG-DVC model, coal is assumed to consist of functional groups including carboxyl, hydroxyl, aether, aliphatics, aromatic C, and aromatic H etc., and different functional groups are pyrolyzed form different products.

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The FG-DVC is a general model which combines a functional group (FG) model for gas evolution, and a statistical depolymerization, vaporization and crosslinking (DVC) model for tar and char formation. The FG model describes the gas evolution as well as the elemental and functional group compositions of the tar and char, while the DVC model is employed to determine the yield of tar and molecular weight distribution of the tar and char [20]. Therefore, the FG and DVC model can be combined to describe the combined effects of the following seven processes occurring in pyrolysis, depolymerization and hydrogen consumption, crosslinking, external transport, internal transport, gas formation, tar formation and char formation. In the FG-DVC model, coal is represented as a two-dimensional Bethe lattice of aromatic clusters linked by aliphatic bridges [20]. TG-FTIR must be employed to provide the needed input parameters for the FG-DVC which describes the coal structure and evolution kinetics. In addition, the oxygen/carbon and hydrogen/carbon molar ratios are used as the indicators of coal rank in FG-DVC model. The important equations of FG-DVC model are listed as follows:

Yi = Yi 0 exp(−kit )

(6)

ki = Ai0 exp(−( Ei0 ± σ i ) / RTp )

(7)

X = X 0 exp(− ktar t )

(8)

0 ktar = Atar exp(−( Etar ± σ tar ) / RTp )

(9)

Vi,char = (1 − X 0 + X )Yi

(10)

Vi,tar = ( X 0Yi 0 − XYi )ktar / (ki + ktar )

(11)

Vi,gas = (1 − X 0 )(Yi 0 − Yi ) + Vi,tar ki / ktar

(12)

Vi,char + Vi,tar + Vi,gas = 1

(13) 0

where Yi is the mass fraction of a released functional group, Yi is the initial mass fraction of 0

a functional group, ki is the rate constant, t is the reaction time, Ai is the pre-exponential,

Ei0 and σ i are the mean activation energy and standard deviation which are employed to describe the distribution of activation energy of a functional group release, X and X

0

are

the mass fraction of the initial and residual tar precursors, ktar is the rate constant of tar 0

release, Atar is the pre-exponential of tar evolution, Etar and

σ tar are the mean activation

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70

Experimental char yield Predicted char yield Experimental char N Predicted char N

65

65

60

60

55

55

50

50

45

45

N in char (daf, %)

70

Char yield (%)

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are the mass fractions of a species in char, tar and gas respectively. The transient evolution rates and yields of tar and light gases can be determined with the equations (6)–(13), providing a set of kinetic parameters of about 20 reactions, the content of functional group in coal, and the temperature history of the pyrolyzed particles are obtained. The FG-DVC model was applied successfully in predicting the main pyrolysis species and nitrogen-containing precursors [22,23,24]. Figure 6 shows the comparison between the experimental and predicted results (char yield and nitrogen partitioning in char) with FGDVC model. As can be seen from the figure, the predicted results are in good agreement with experimental data, which indicates that FG-DVC can be available in a pre-processing step to provide initial data for char combustion sub-models in coal combustion modeling. In the CPD model proposed by Grant et al. [25], coal is visualized as macromolecular arrays of fused aromatic rings of various sizes and types, including hetero-aromatic systems with both nitrogen and oxygen atoms. These molecular groupings or clusters are connected with a variety of chemical bridges containing both labile bonds and stable bridges at a given temperature. The CPD model distributes devolatilization products into char, tar, and light gas other than individual components such as CO2, CO, H2O, H2, and light hydrocarbons. Percolation statistics are used to describe the network decomposition. The CPD model uses chemically dependent input parameters determined in part from nuclear magnetic resonance (NMR) data to reflect the chemical diversity found in coals of different rank and type. Four parameters obtained from NMR analysis that describes the structure of the parent coal are used directly as the input parameters to the CPD model. These include Mcl (the average molecular weight per aromatic cluster), Mδ (the average side-chain molecular weight), σ+1 (the average number of attachments per cluster), and p0 (the fraction of intact bridges). The CPD model is unique because the majority of the model input parameters are taken directly from NMR data, while, other models use these parameters as empirical fitting coefficients. This means that the CPD model is based on a mechanistic rather than on an empirical basis.

40

40 74

76

78

80

82

84

86

Carbon content of coal (daf, %)

Figure 6. Comparison between experimental and predicted char yield and nitrogen partitioning in char with FG-DVC. (Pyrolysis conditions: the heating rate is 105 K/s, the pyrolysis temperature is 1673 K, the residence time is 90 s, the pyrolysis atmosphere is nitrogen.) [23]. Coal Combustion Research, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

12

Juwei Zhang The following chemical scheme is proposed to describe the pyrolysis process: (1) formation of a reactive bridge intermediate from a labile bridge

ka

ζ

ζ*

(R8)

(2) formation of a char bridge and gas from the reactive intermediate

kb

ζ*

c + 2 g2

(R9)

(3) formation of a side-chain from the reactive intermediate

ζ*

kc

2δ

(R10)

(4) conversion of side chains into light gases

δ

kd

g1

(R11)

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According to this chemical scheme, some governing equations are given:

dζ / dt = −kaζ

(14)

dc / dt = kbζ * = ka kbζ / (kb + kc )

(15)

d δ / dt = 2kcζ * − kdδ = [2kc kaζ / (kc + kb )] − kdδ

(16)

p =ζ +c

(17)

f = 1− p

(18)

g = g1 + g 2

(19)

g1 = 2 f − δ

(20)

g2 = 2(c − c0 )

(21)

The Flashchain model proposed by Niksa [26] uses a distributed-energy chain model to represent coal structure. In FLASHCHAIN model, some fundamental structure parameters such as the fraction of intact bridges are correlated with the carbon content. Therefore, the ultimate analysis results of coal are the only input data needed in FLASHCHAIN model,

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which is the most evident difference with the FG-DVC model and the CPD model. It seems that the FLASHCHAIN model is more empirical, but it can indeed capture the main features of coal pyrolysis. In conclusion, the three network models are all based on a structure description of the parent coal, that is, the coal macromolecule is considered to consist of aromatic ring clusters, which are connected by bridges of varying reactivity, and the network statistics are all employed to represent the breakup and formation of bridges. Comparing the three network models with each other, it should be emphasized that the FG-DVC model should be recommended, since it associates most closely with the characteristics of the real coal pyrolysis process, though considerable input parameters are needed in this model.

2. CHAR COMBUSTION In the three main processes of coal combustion, char combustion plays a significant role in the burnout of coal and the subsequent formation of pollutants. Therefore, char combustion has been studied extensively all over the world due to its central importance in the combustion of carbonaceous solid fuels including coal and biomass.

2.1. Description of Char Combustion Process

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2.1.1. Macro-description The products of char combustion mainly consist of carbon monoxide and carbon dioxide. Naturally, the one-step global reaction can be written as: C + O2

k

CO/CO2

(R12)

This reaction mechanism is the simplest and the most frequently used [27,28,29,30,31,32,33,34], but it can not describe the behavior of char combustion accurately, such as the reaction order [35,36]. Some authors [37,38] used three familiar heterogeneous reactions and one homogeneous reaction to describe char combustion: C + O2 → CO2

(R13)

C + 1/2 O2 → CO

(R14)

C + CO2 → 2CO

(R15)

2CO + O2 → 2CO2

(R16)

Using these three reactions, it is explicit to analyze the gas distribution around the burning char particle. Figure 7 shows the gas distribution around a burning char surface in a stagnant atmosphere. When the gas temperature far from the char surface is higher than 1200 , the gasification reaction (R15) is accelerated markedly. When char combustion

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begins, reactions R13–R16 take place together on the carbon surface of char, and CO and CO2 are both products. Therefore, the gas distribution is determined by R13–R15 jointly. When CO formed on the char surface diffuse outward, CO consumes O2 from far away and forms CO2 through the CO combustion reaction R16, then a flame front can be formed. The concentration of CO decreases monotonously as the increase of distance from the char surface, while the concentration of O2 has a reverse variation trend. The flame front has the highest CO2 concentration, and CO2 diffuses to the char surface and far place simultaneously. The portion to the char surface is consumed by carbon through gasification reaction R15 due to the absence of O2 on the char surface. In this case, there is no O2 transportation between the char surface and flame front, but CO2 transports the oxygen compound to char surface and gasifies the char. As we known, the gasification reaction R15 is endothermic, and the heat generated at flame front supplies the heat needed by R15 so that the high temperature condition on the char surface can be maintained. O2

CO Char CO2

CO2

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Concentration

Flame front CO2

O2

CO

Distance from the char surface Figure 7. Gas distribution around the burning char surface in a stagnant atmosphere ( T∞ > 1200 [39].

)

It is no doubt that reactions R13-R16 are more precise to describe the char combustion in comparison with one-step global reaction R12. However, we can find that reactions R13–R16 also belong to a type of macro-description and they can be easily transformed into the form of R12. Therefore, reactions R13–R16 do not improve substantially. In order to accurately describe the char combustion, the micro-description mechanism must be developed.

2.1.2. Micro-description Although the mechanism of char-oxygen reaction has not been fully understood, up to now, several advanced micro-mechanism of char combustion has been proposed. The semiglobal intrinsic mechanism of char combustion suggested by Hurt and Calo [40] is the most representative and some adsorption/desorption process are introduced in this mechanism:

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k1 k2 k3

2C(O) CO2+ 2C(O) CO

15 (R17) (R18) (R19)

where Cf is a free carbon active site, C(O) can be either a stable or unstable surface oxygen complex. Reactions R17–R19 are called semi-global mechanism, because any of the steps may be a synthetic description of several elemental steps. In this mechanism, oxygen attacks the carbon surface leading to the formation of C(O) (R17), oxygen attacks C(O) resulting the desorption of CO2 (R18), and CO is desorbed from C(O). CO2 is usually considered to be formed by: 2C(O) → CO2

(R20)

but a number of isotope tracer (C18O16O) experiments results [41,42,43] support R18 as the primary pathway to CO2 rather than R20. If only the reactions R17 and r19 are taken into account, the simplest rate expression of Langmuir-Hinshelwood form is given by [41]:

rc =

k1k3 PO2 k1 PO2 + k3

(22)

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2.2. Intrinsic Reactivity Being the same as other gas-solid reactions, char combustion is controlled by chemical reactions and/or gaseous diffusion to the particles, thus, three zones can be defined in char combustion, zone I where char combustion is controlled by the chemical reactions, zone II where char combustion is controlled by both chemical reactions and gas diffusion, and zone III where char combustion is controlled by the bulk gas diffusion. Generally, the intrinsic reactivity of char R (gm-2s-1) can be expressed as [44]:

R = Rc (Cs )n = ηγρ Ag Ri [Cg (1 − x)]m

(23)

where Rc is the apparent rate constant (kgm-2s-1(kgm-3)-n), Cs is the oxygen concentration at the outer surface of the particle (kgm-3), n is the apparent reaction order, η is the

effectiveness factor (the ratio of the actual combustion rate to the rate attainable if no porediffusion resistance exited), γ is the characteristic size of the particle (m, ratio of the volume to external surface area), Ag is the specific surface area of the particle (m2m-3), Ri is the rate

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constant of intrinsic chemical reactivity (kgm-2s-1(kgm-3)-m), Cg is the oxygen concentration in the bulk gas (kgm-3), x is defined as:

x = ρ / ρm

(24)

In zone I, the reactions in pores surface are slow, and the oxygen concentration in the pores is equal to that in the bulk gas, so the char combustion is kinetics limited and there are no pore diffusion limitation, i.e. η = 1 , and then equation 23 becomes:

R = γρ Ag Ri [Cg (1 − x)]m

(25)

In this case, the obtained apparent kinetic parameters (activation energy, pre-exponential factor and reaction order) in experiments are the true intrinsic kinetic parameters. In zone II, the rate of char combustion is so fast that the oxygen concentration in the pores is lower than the bulk gas concentration, and it is zero in the centre of the particle. Thus, char combustion is controlled by both chemical reactions and gas diffusion, and then equation 23 becomes:

R = 2{2 ρ Ag Ri [Cg (1 − x)]m+1 / (m + 1)}0.5

(26)

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In this case, intrinsic reactivity of char R is independent of particle size, and the obtained apparent activation energy in experiments is approximately half the true intrinsic value, while the obtained apparent reaction order n in experiments is given by:

n = ( m + 1) / 2

(27)

As char combustion proceeds, the large particle shrinks as it burn at a constant density, however, for pulverized coal, the density and particle size both reduce with burn-off. The intrinsic rate constant Ri can be expressed as Arrhenius form:

Ri = A exp(− E / RT )

(28)

where A is the frequency factor (kgm-2s-1(kgm-3)-m), E is the intrinsic activation (kJmol-1). (1)

0

(2)

800

(3)

1600

(4)

2100

Temperature (℃)

Figure 8. Four stages of structure transformation with increasing temperature [45]. Coal Combustion Research, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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2.3. Thermal Annealing The significant effect of thermal annealing on char reactivity has been long recognized [46,47,48,49,50,51,52,53]. Generally, thermal annealing leads to the reducing of char reactivity (deactivation) which can be attributed to loss of carbon active sites, loss of surface area, loss of inorganic catalytic activity, and structure ordering within the carbon matrix. Thermal annealing destroys carbon active sites and micro-porous surface area through transformations of the carbonaceous matrix, involving hydrogen loss, edge coalescence, and defect elimination [47]. The early stages of this process are referred to collectively as pregraphitization or char maturation. Thermal annealing destroys catalytic active sites through a variety of inorganic reactions and phase transformations, including sintering, vaporization, changes in the oxidation state (e.g. for Fe catalysis), and the incorporation of well-dispersed catalytic elements into glassy phases [47]. Figure 8 shows the four stages of structure ordering within the carbon matrix with the increasing temperature. At the temperatures below 500 , the basic structure units in the carbon matrix has no changes (stage 1). At the temperatures between 800 and 1500 , the basic structure units associate face-to-face in distorted column (stage 2). At the temperatures between 1600 and 2000, adjacent columns coalesce into distorted wrinkled layers (stage 3). At the temperatures above 2100, these layers stiffen, become flat and perfect (stage 4). Generally, at lower prepared temperatures, chars from the higher rank coals were relatively less reactive than chars from the lower rank coal. While, at higher prepared temperatures (up to 1800 ) which is typical of full-scale pulverized coal combustion, the trend is reverse [50]. In practical coal combustion process, chars were formed in the oxidation atmosphere other than inert atmosphere, so the mutual interactive effect of char surface oxidation and thermal annealing should be taken into account. Carbon oxides formed in char surface can prevent or retard the development of structural anisotropy, then reduce the loss of char reactivity. At temperatures exceeding 1200, the effect of oxidation vanishes [52,53]. Thermal annealing is a complex process, and several models have been proposed to incorporate the thermal history of chars [46–49], among which the model of Hurt et al. [47] is the most suitable and simple one. In this model, the active sites were proposed to be deactivated by a first-order thermal process which was assumed to share a common preexponential factor for annealing with distributed activation energies for annealing. The ratio of the total number of sites to the number of sites at the initial time (N/N0) can be calculated from

dFE = − Ad FE exp(− Ed / RT ) dt

(29)

∞ N = ∫ FE ( Ed , t )dEd 0 N0

(30)

where FE is the normalized frequency distribution function for active sites (kJ-1), Ad is the pre-exponential factor for annealing (s-1), Ed is the activation energy for annealing (kJ/mol). Coal Combustion Research, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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The value of N/N0 can be obtained by a numerical integration of equation 29 and 30 with a 30-bin discretization in Ed .

2.4. Char Burnout Model 2.4.1. Char combustion model The rate of char combustion R can be given by [54]

R = f (Tf , Pc , Cgn Ri )

(31)

where Tf is the temperature of bulk gas around char particle, Pc is some physical and chemical properties of chars including the specific area, porosity, inherent mineral content etc. Up to now, many char combustion models have been proposed to predict char combustion process. In this section, these models are divided into such two classes as global char combustion model and detailed char combustion model.

2.4.1.1. Global char combustion model Among various char combustion models, the simple global models are used most frequently. The simple global model proposed by Baum and Street [55] is given by

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X O2 dmc −1 = −π D 2 ρ RTp ( Rdiff + Rc-1 ) −1 dt M O2

(32)

where mc is the mass of the coal particle (kg), D is the diameter (m), ρ is the density (kgm3

), R is the universal gas constant (J mol-1 K-1), Tp is the temperature (K), X is the mol fraction of species, M is the molecular weight of species (gmol-1), Rdiff is the diffusion reaction coefficient (kgm-2s-1Pa-1), and Rc is the chemical rate coefficient (kgm-2s-1Pa-1). This model is based on the external area of the char particle and does not account for the pore structure changes of char particle (e.g. surface area, porosity, and pore distribution etc.), Stefan flow, particle fragmentation, and the impurities in char. The diffusion reaction coefficient Rdiff and chemical rate coefficient Rc can be expressed by Rdiff = C1

[(TP + T∞ ) / 2]0.75 D

Rc = Ae

− ( E / RTp )

(33)

(34)

where C1 is mass diffusion-limited rate constant (m3 s-1 K-0.75), T∞ is the temperature of bulk

gas far from the char particle (K). The disadvantage of this model is that D and ρ are difficult to obtain due to the swelling of the char particle, and A and E vary with coal rank. Coal Combustion Research, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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Another prevalent char combustion model is the intrinsic model proposed by Smith [45]. The detailed description can be referred to section 2.2. This model is perfect in principle, however, it is very difficult to determine several parameters in equation 23 such as effectiveness factor η and reactive surface area Ag. The kinetic parameters in global char combustion model include activation energy, preexponential factor and reaction order. Table 2 lists some activation energy values of char combustion obtained in past studies. In principle, the activation energies of different char reactions should be uniformed, however, the scatters of activation energy values (105–182 kJ/mol) can be observed in Table 2. Table 2. Summary of activation energy values in literature Activation energy (kJ/mol) 180

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130

Samples Series of chars and pure carbons Micro-porous carbon

109-134

Series of chars

133

Variety of droptube chars

147

Lignite char

155 ± 10

Series of chars

105–180 (many: 130–150)

Series of chars

133

Inertinite-rich high-ash coal char

143

Inertinite-rich high-ash coal char

180

Illinois No. 6 coal chars

Conditions – Isothermal oxidation at 400– 550K Isothermal oxidation at 573– 973K Isothermal oxidation at 573– 885 K Isothermal oxidation at 623– 848K High temperatures High temperatures (1200–1700K) Non-isothermal oxidation at a heating rate of 4 K/min Non-isothermal oxidation at a heating rate of 4 K/min Isothermal oxidation at 673– 773K

Author (reference) Smith [45] Floess et al. [56]

Gopalakrishnan and Bartholemew [57] Sorensen et al. [58] Coda and Tognotti [59] Williams [55] Hurt and Calo[60] Everson et al. [61]

Everson et al. [62]

Kulaots et al. [62]

This can be primarily attributed to the catalytic effect of inherent metal in chars, since the existence of catalytic metal can obviously decrease the values of apparent activation energy.

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It is meaningless to compare various pre-exponential factors obtained from different studies, because the value of pre-exponential factor can be changed with many factors such as impurities in chars, difference in area bases, char annealing, parent coal petrography, particles size distribution, variations of physical structure and properties of chars etc.. Even for the preexponential factor in intrinsic model, its value is influenced greatly by the choice of area bases. Therefore, the values of pre-exponential factor are not given. Table 3. Summary of char combustion models based on different reaction mechanisms Model types Model 1. Global power-law Model 2. Two-step semi-global ( LangmuirHinshelwood) Model 3. Threestep semi-global

Mechanisms C + O2 → CO/CO2 (k1)

Rate laws

R = kPOn2

Cf + O2 → C(O) + CO (k1) C(O) → CO (k2)

R=

Cf + O2 → C(O) + CO (k1) C(O) → CO (k2) C(O) + O2 → C(O) + CO/CO2 (k3)

R=

k1k2 PO2 k1 PO2 + k2

k1k2 PO22 + k1k3 PO2 k1 PO2 + k3 / 2

CO/CO 2 =

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Model 4. Six-step semi-global

Cf + O2 → C(O) + CO Cf + O2 → C2(O2) Cb + Cf + C(O) + O2 → CO2 + C(O) + Cf Cb + Cf + C(O) + O2 → CO + C(O) Cb + C(O) → CO + Cf Cb + C2(O2) → CO2 + Cf

k3 k 2 PO2

–

For the global intrinsic reaction order m of char combustion at near atmospheric O2 partial pressures, Hurt and Calo [61,63] reviewed the past reliable studies and concluded that at low temperatures (600–800 K), m had high fractional order values (0.6–1), while at high temperatures (1200–1700 K), m were zero with some reports of 1st order. The global intrinsic reaction orders in the high-temperature area were calculated from the Thiele scaling law ( m = 2n − 1 ). As a matter of fact, the variation of global intrinsic reaction orders with temperatures contradicts with simple global power-law kinetics and Langmuir-Hinshelwood semi-global kinetics. Therefore, more detailed char combustion model must be developed.

2.4.1.2. Semi-global char combustion model So-called semi-global char combustion model denotes that the rate expressions in the models are deduced from more than one mechanism reactions. Table 3 lists the mechanisms of one global model and three semi-global models and corresponding rate laws. It is wellknown that global char combustion model is based on R12, and only one pair of kinetic parameters is needed. The simplest semi-global char combustion model is the LangmuirHinshelwood model which is based on the theories of adsorption and desorption on homogeneous (i.e. single surface site type) surface. Both zero order power-law and Langmuir-Hinshelwood models can describe some restricted conditions (e.g. hightemperature pulverized coal combustion), however, neither power-law kinetics nor Langmuir-

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Hinshelwood models can satisfactorily describe char combustion over the entire range of temperature (600–700 K) and oxygen pressure (0–30 bar) in practical industry applications. Compared with Langmuir-Hinshelwood model, three-step semi-global model incorporates an O2-complex reaction and be capable of describing the basic trends of global order, global activation energy, and CO/CO2 ratio over a wide range of combustion conditions, however, it is not capable of describing many behaviors of char combustions including the production of CO2 as a second product of thermal desorption, site heterogeneity, constant nth-order behavior over two orders of magnitude oxygen partial pressure, the increases in oxide surface density with increasing temperature or with the onset of gasification etc. [61]. According to the analysis of Hurt and Haynes [64], site heterogeneity should be taken into account. The regular method of the model with site heterogeneity is assuming that some surface reactions in semi-global model have a distribution of activation energies and the rate of a reaction with distributed activation energies can be expressed as ∞

R = ∫ R( E ) f ( E )dE 0

(35)

where f ( E ) is the site density distribution, if the Gauss distribution is applied, f ( E ) can be given as f (E) =

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where

σ

1 ⎛ 1 E − E0 2 ⎞ exp ⎜ − ( ) ⎟ σ σ 2π ⎝ 2 ⎠

(36)

is the width of the distribution, and E0 is the mean activation energy. In fact, the

model with distributed activation energies is indistinguishable from power law model, when the distribution width is large enough. The detailed analysis can be referred to literature [64]. In order to make the heterogeneous surface models applicable to practical conditions, much work is still needed to be done. The important work mainly includes extensive surface characterization of each carbon material of technological interest, further characterization on carbon oxide oxidation step (C(O) + O2 → products), and the more accurate carbon surface heterogeneity in the high-temperature (>1000 K) regime etc..

2.4.2. Carbon burnout kinetic (CBK) model So far, the CBK model proposed by Hurt et al. [47] is the most popular and comprehensive carbon burnout model. CBK model mainly includes three sub-models, (1) the single film char oxidation sub-model, with rank-dependent kinetic correlations and internal reaction/diffusion formulation; (2) the thermal char deactivation (or thermal annealing) submodel; (3) the physical property sub-model describing swelling , diameter/density changes during oxidation, and ash inhibition effects in the later stage of combustion. 2.4.2.1. Single film char oxidation sub-model For the char combustion at high temperature (>1000 K), reactions R14–R16 are the dominant heterogeneous reactions at the char particle surface. According to the above analysis in section 2.1.1, the two surface reactions and CO volumetric reaction proceed

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Juwei Zhang

simultaneously. Nevertheless, the single film model assumes either CO or CO2 is the product formed at the particle surface, and any CO formed is oxidized only in the free stream. Therefore, this simplified single film model is widely used in the area of char combustion kinetics just due to its simple equations resolving process. The energy balance of a single char particle can be described by the following equations:

mpCp dTp / dt = Qrxn − Orad − Qconv

(37)

Orxn = R(−ΔH rxn ) / MWC

(38)

Orad = Aσε (Tp4 − Tw4 )

(39)

Oconv = AU (Tp − Tg )

(40)

where mp is the particle mass (g), Cp is the particle heat capacity (J/gK), Tp is the particle temperature (K), Orad , Qconv , Qrxn are the heat loss rate due to radiation, heat loss rate due to convection, and rate of heat generation by the reactions (J/s) respectively, A is the carbon surface area (m2), σ is the Stefan-Boltzmann constant (Wm-2K-4), ε is the particle emissivity, Tw and Tg are the wall temperature or the environment temperature and the gas temperature (K) respectively, U is the overall heat transfer coefficient (Wm-2K-1), R is the single particle combustion rate (g/s), ΔH rxn is the heat of reaction (J/mol), and MWC is the

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molecular weight of carbon (g/mol). If an ash film is present, U is given by: U=

(d p / d C ) 2

(41)

1 / hext + 1 / hint

hext =

hint =

λg Nu

(42)

dp

2λa d C δ dp

(43)

λa = (1 − θ )λa,true + θλg

(44)

where d p is the whole particle diameter (m), and dC is the carbon core diameter (m), hext is the external convective heat transfer coefficient (Wm-2k-1),

λg is the gas phase conductivity

(Wm-1k-1), hint is the internal heat transfer coefficient (Wm-2k-1),

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λa is the thermal

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23

δ is the thickness of the ash film (cm), θ is the

λa,true is the conductivity of the fully dense mineral phase

-1 -1

(Wm k ). The single particle combustion rate is given by:

R = η k0 S exp(− E / RTp ) Psn mp 1

1

(45)

η = [coth(3φ ) − ] 3φ φ

(46)

d p (n + 1)k0 S ρ ( Ps / RT ) n-1 1/2 φ= [ ] 6 2 Deff

(47)

where k0 is the pre-exponential factor for the surface rate constant (gs-1m-2atm-n), S is the total specific surface area (m2/g), E is the intrinsic activation energy (cal/mol), Ps is the oxygen partial pressure at the outer particle surface (atm), n is the empirical apparent reaction order, η is the effectiveness factor, φ is the Thiele modulus, T is the gas temperature at the particle surface (K), ρ is the char particle density (gcm-3), Ps is the oxygen partial pressure

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at the particle surface, and Deff is the effective diffusivity (m2/s). In the equation 45, k0 S can be directly measured by TGA, therefore, it is usually used to represent char reactivity. According to the discussion in section 2.5.1, the single film char oxidation sub-model is a kind of global char combustion model, therefore, the accuracy of the sub-model can be improved if more detailed semi-global mechanisms are used.

2.4.2.2. Thermal annealing sub-model The thermal annealing of char can decrease the char reactivity, which has been discussed in section 2.3 in detail. According to the equation 29 and 30, active site ratio N/N0 can be obtained. In zone I, the reactivity change (k0 S ) / (k0 S )0 is proportional to N/N0. In zone II, the observed deactivation factor is (N/N0)0.5 if the effects of annealing are limited to the changes in surface area and changes in intrinsic surface area [64]. Then the reactivity at any time is given by:

k0 S = (k0 S ) 0 (

∞ N 0.5 ) = (k0 S )0 ( ∫ FE ( Ed , t )dEd )0.5 0 N0

(48)

The input parameters of thermal annealing sub-model are the temperature history of char particle, annealing pre-exponential factor ( Ad ), mean value of Ed distribution, and standard

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Juwei Zhang

deviation of Ed distribution. At the initial time, the frequency distribution function

FE ( Ed , 0) is assumed to be a normalized log-normal distribution in Ed [47]. FE ( Ed , 0) =

where μ and

σ

⎛ −(ln Ed − μ ) 2 ⎞ exp ⎜ ⎟ 2σ 2 Edσ 2π ⎝ ⎠ 1

(49)

are the mean value and standard deviation of the ln Ed distribution.

Sometimes FE ( Ed , 0) is assumed to be a shifted Γ -distribution [49,51]: FE ( Ed , 0) =

( Ed − δ ) (α-1) ⎛ E −δ ⎞ exp ⎜ − d Γ (α ) β α β ⎟⎠ ⎝

(50)

δ is a shifted parameter taking into account a positive activation energy at the 2 beginning of deactivation (kJ/mol). The mean activation energy ( Ed , m ) and deviation ( δ d ) where

α

δ d2 = αβ 2 ). Zolin et al. [49] compared the two distributions and found that the shifted Γ -distribution fitted their data are simple function of

and β ( Ed , m = δ + αβ and

better.

2.4.2.3. Physical property sub-model In physical property sub-model, the swelling, diameter and density changes, and ash inhibition in the late stages of combustion are taken into consideration. Firstly, the density ρ

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(g/m3) and diameter d p (m) of char particle are calculated by:

1

=

ρ

1− Xa

ρC

+

Xa

ρa

ρC m = ( C )α ρC,0 mC,0 dp d p,0

= [(

m ρ 0 1/3 )( )] m0 ρ

(51)

(52)

(53)

ρC is the carbon density (g/m3), ρa is the ash density (g/m3), X a is the instantaneous ash mass fraction, ρ C,0 is the initial carbon density (g/m3), α is the empirical mode of where

burning parameter, mC is the mass of carbon (g), subscript 0 denotes the initial state, m is

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the mass of char particle (g). Then, the carbonaceous core diameter d c (m) and the ash film thickness

δ af (m) can be obtained by: dC = dp [

( X a ρ − ρ a,n (1 − θ af )) X a,0 ρ0 − ρ a,n (1 − θ af )

]1/3

δ af = (d p − d c ) / 2 where

(54)

(55)

ρ a,n is the density of nonporous ash (m), θaf is the porosity of the ash film. Then, the

molar flow of oxygen N O2 (mol/s) can be obtained by:

NO2 = π dC2 K p ( Pox − Pox , s ) Kp =

ShDd pθ af2.5 RTm ( Shδ d C + θ af2.5 d C2 )

Tm = (Tg + Tp ) / 2

(56)

(57)

(58)

where K p is the overall mass transfer coefficient (molms-1J-1), Pox is the oxygen partial

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pressure in the bulk gas (Pa), Pox,s is the oxygen partial pressure at the outer surface of the reacting carbon-rich core (Pa), Sh is the Sherwood number, Tm is the mean film temperature (K), Tg is the gas temperature in the bulk gas (K), and Tp is the temperature at the outer surface of the carbon-rich core (K). In this physical property sub-model, the parameter of burning mode α is considered as constant and the change of specific surface area is not taken into account. However, the changes in the apparent density, diameter and specific surface area depend on the regime in which the char particle burns. Therefore, the above physical property sub-model is obviously inaccurate. Unfortunately, the information of individual particle is difficult to obtain directly. Mitchell et al. [65] recently used a direct numerical simulation of a single char particle burning in an oxidation environment to quantify the variations in apparent density, diameter and specific surface area. They drew the conclusion that if the power-law sub-model of density and diameter changes were to be modified to capture the characteristics of char combustion, α would have to vary during the course of burning and the evolution of specific surface area of a char particle in zone II burning regime could be determined by:

S = S0 1 −ψ ln( ρ / ρ 0 )

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(59)

26

Juwei Zhang

where ψ is a char structure parameter. Equation 59 is obtained by modifying the random pore model (RPM) of Bhatia and Perlmutter [66] to char combustion in zone II. In one word, the changes of the physical property of char particles are still a big challenge for building an accurate char combustion model.

3. NOX FORMATION DURING COAL COMBUSTION As is well-known, many pollutants are released during the coal combustion process. In this section, the transformation of nitrogen which is the most important pollutant element in coal will be discussed, and some quantitative results can be very valuable in building a precise model of NOx formation which can be integrated into the CFD simulation of coal combustion.

3.1. NOx Formation Mechanism

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Generally, NOx can be formed in three ways during coal combustion, thermal-NO mechanism, prompt-NOx mechanism, and fuel-NOx mechanism. The former two are both formed from N2, and fuel-NOx is formed from raw coal. Thermal-NOx mechanism proceeds by the following three reactions [67]: O + N2

NO + N

(R21)

N + O2

NO + O

(R22)

N + OH

NO + H

(R23)

The formation of thermal-NO strongly depends on temperature with exponential rules. When the temperature exceeds 1800 K, the formation of thermal-NOx is very important. However, the temperature in combustion systems is generally lower than 1800 K, so the formation of thermal-NOx is less important and the amount of thermal- NOx accounts for around 15–25 % in a pulverized coal boiler. The formation of prompt-NOx is initiated by the attack of CH radical on the N2 triple bond [68]. CH + N2 O + N2 + M

NCN + H N2O + M

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(R24) (R25)

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+CHi R4 Ash

Coa

Pyrolysis R0

+Ox (OH) NOx + R1 Volatile N Char (HCN, NH3) +NOx N R5 R2 Char N

η 1-η

+O2 R3

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Figure 9. Schematic of NOx formation during coal combustion [69]

NCN and N2O may subsequently be oxidized into NO or reduced into N2. In coal-fired boilers, the amount of prompt-NO formed is generally less than 5 %. The fuel-NOx which is formed from organically bound nitrogen in coal has been recognized as the most important source of NOx in coal combustion. The fuel-NOx includes volatile NO and char NO. Under typical conditions of coal combustion, about 80 % of total NOx amount comes from fuel-NOx in which volatiles NO accounts for 60–80 %. Figure 9 shows the fuel-NOx formation pathways in coal combustion. Nitrogen element in coal is distributed in volatile and residual char after the pyrolysis of coal. The N intermediates (NOx precursor) in volatiles (mainly HCN and NH3) are oxidized into NOx (R1) or reduced into N2 (R2), while the char N is be also partially oxidized into NOx (R3). When the formed NOx encounters the hydrocarbon radical (CHi) and char surface, it can be reduced into N intermediates (R4) and N2 (R5) respectively. To summarize these reactions, three main processes can be identified in the fuel-NOx formation, one is N partition during coal pyrolysis between volatile and char (R0), the other is N conversion during volatile oxidation (R1, R2, and R4), and N conversion during char oxidation (R3 and R5). These processes will be discussed in the following three sections respectively. At high temperatures encountered in pulverized coal combustion environments, N2O is produced in the initial stages of the flame, but it is subsequently be converted into other N forms in the flame and has quite low mission levels (typically 1–5 ppmv). On the contrary, in the lower temperature combustion environments such as fluidized bed systems, N2O emission levels are much higher (20–150 ppmv). The discussion in the following sections will be mainly confined to the processes involving NO, since the pulverized coal combustion is the most prevalent coal combustion technology.

3.2. Nitrogen Partition during Coal Pyrolysis Coal pyrolysis, as the initial step of combustion, determines the transformation and distribution of nitrogen element. Nitrogen in coals exists in the forms of organically bound nitrogen. There are three types of mainly organically bound nitrogen, pyrrolic-N, pyridinic-N and quaternary-N. X-ray photoelectron spectroscopy (XPS) is the most effective instrument to study the nitrogen functional forms present in the coals, chars, and tars. Wojtowicz et al. [70]

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Juwei Zhang

used XPS to investigate the transformations of nitrogen functionality occurring during coal pyrolysis, and found that the fresh coal contained typically pyrrolic-N of up to 80 %, pyridinic-N of up to 40 %, and quaternary-N of up to 20 %, and the pyrrolic-N was converted into the pyridinic-N upon heat treatment. Kelemen et al. [71,72] used XPS to further identify and quantify the changes in organically bound nitrogen forms in the tars and chars of after coal pyrolysis. They found that nearly all of the nitrogen initially present was retained in the chars under mild conditions ( T ≤ 400 , 300 s) and implied that some of the quaternary nitrogen species were transformed into pyridinic forms. Figure 10 shows the typical relative distribution of nitrogen at various temperatures in the residual char. It can be seen that the three types of organically bound nitrogen vary intensely as the pyrolysis temperature exceeds 600 , which implies that the pyrrolic-N can be converted into pyridinic-N and quaternary-N at high temperature. Different ranks of coal have the similar variation trends of nitrogen functional groups as shown in Figure 10. As the transformation of organically bound nitrogen, the gaseous NOx precursors such as NH3 and HCN are released. NH3 and HCN are formed during the primary pyrolysis of coal and/or during the secondary thermal cracking of volatiles and char. The formations of NH3 and HCN have been studied extensively in the past ten years. The yields of NH3 and HCN are influenced significantly by the pyrolysis conditions such as temperature, heating rate, pressure and coal rank, and this is similar with other volatile compositions. The formation of HCN and NH3 are both controlled by the process of the in situ generation of radical, particularly the H radicals, which can be used to explain why the yields of HCN and NH3 are so sensitive to the changes of pyrolysis conditions [73,74]. Therefore, the yields of HCN and NH3 can be increased if more radicals are available. Quaternary-N Pyridinic-N Pyrrolic-N

70

Nitrogen fraction (%)

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60 50 40 30 20 10 0 0

200

400

600

800

Temperature (℃ )

Figure 10. Typical relative distribution of nitrogen in char as a function of pyrolysis temperature [22].

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60

(a)

HCN NH3

40

HCN NH3

(b)

35

40

Yields of coal-N (%)

Yields of coal-N (%)

50

30

20

30 25 20 15 10

10

5 0 0 40

50

60

70

80

90

500

600

Carbon content in parent coal (%)

40

HCN NH3

(c) 35

700

800

900

1000

Pyrolysis temperature (℃ ) Fast heating rate Slow heating rate

(d) 20

Yields of coal-N (%)

Yields of coal-N (%)

30

25

20

15

15

10

5 10

0

5 0

2

4

6

8

10

Pyrolysis pressure (atm)

HCN

NH3

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Figure 11. Effects of coal rank (a), temperature (b), pressure (c), and heating rate (d) on the yields of HCN and NH3. Experiments were conducted in a quartz reactor with Victorian brown coal [74,76,81].

As for the factors influencing the yields of HCN and NH3, Li and coworkers recently did a series of excellent studies [74]. Figure 11 shows the effects of coal rank, temperature, pressure, and heating rate on the yields of HCN and NH3. As can be seen from Figure 11 (a), the yields of both HCN and NH3 decrease monotonically with increasing rank. It is recognized that with increasing temperature, the yield of HCN increases monotonously, and the yield of NH3 firstly increases with increasing temperature up to 800 then decreases (see Figure 11 (b)). It should be emphasized that the decrease of NH3 yield at the temperature higher than 800 may be an artifact due to the interaction between NH3 and some reactor materials such as quartz and stainless steel [82]. As for the effect of pressure, elevating pressure drastically increased the yield of NH3, but affected the yield of HCN little (see Figure 11 (c)). The changes in the yield of NH3 were mainly observed during the feeding periods during pyrolysis, at least partly related to the intra-particle cracking of volatiles and the interactions between the nascent char and the evolved volatiles. From the Figure 11 (d), it can be seen that the yields of HCN and NH3 are very sensitive to changes in heating rates, as the increasing heating rate can lead to the increase of more available H radicals then the yields of HCN and NH3 (see Figure 11 (d)). Besides coal rank, temperature, pressure, and heating rate, the gas atmosphere and inherent minerals in coal can also greatly affect the yields of HCN and NH3. For example, CO2 and H2O can promote the formation of NH3, O2 can promote the formation of both the HCN and NH3, and inherent minerals can catalyze HCN to NH3. The detailed description can be referred to the literature 79 and 82. In a word, these phenomena can all be partly or totally interpreted by the availability of H radicals which is the fundamental cause of the formation of HCN and NH3.

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Juwei Zhang

3.3. Nitrogen Conversion during Volatile Combustion The two important N intermediates, HCN and NH3, are oxidized into NO during the oxidation of volatiles. Figure 12 shows the elementary reaction pathways of the formation and reduction of NO. It can be observed that HCN and NH3 are both oxidized into amine radical (NH), and then have the same steps (the dashed box in Figure 12) that determine the selectivity towards NO and N2. For this reason, the specific nitrogen partitioning in volatiles is often of little importance for the NO formation. In addition, we should note that R2 and R4 in Figure 9 are also shown in Figure 12. NO can be reduced by N and be recycled into N2 (R2), which is the principle reaction of selective non-catalytic reduction (SCN) technology. In the systems with stoichiometric ratios less than 0.7, hydrocarbon radicals (CHi) can recycle NO back to HCN (R4) [83], which is the principle reaction of reburning technology. CHi needed in R4 is not only created during pyrolysis of volatiles, but also during oxidation and gasification of char [70]. +CHi R4 +OH, H HCN

CN

+OH, O2

NCO +H NH

NH3

+H, OH

NH2

+H

+OH, O2

NO

N

+N R2

+NO

+H

N2

Figure 12. Schematic of the NO formation pathways in the oxidation of HCN and NH3 modified from literature [84], and based on modeling prediction with a detailed reaction mechanism.

NO concentration (ppm)

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500

450

HCN NH3

400

HNCO CH3CN Pyridine

350

300

250

200 1300

1400

1500

1600

1700

1800

Temperature (K)

Figure 13. Modeling prediction of the NO formation as a function of temperature in oxidation of a CH4/CO/H2 mixture doped with 1000 ppm of a fixed nitrogen compound under fuel-rich conditions and a residence time of 0.3 s [84].

Figure 13 shows the modeling prediction of the NO formation with a detailed chemical kinetic model under fuel-rich conditions as a function of temperature. Besides HCN and NH3, the results of other N compounds are also given in the Figure 13. It can be seen that, almost

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all the N compounds have the equivalent amount of NO productions, especially at higher temperature except CH3CN. This means that they have the similar selectivity towards NO and N2.

3.4. Nitrogen Conversion during Char Combustion The overall reaction mechanism of char-N conversion during char combustion can be represented by: C(N)＇+ O2 → NO + C(O)

(R26)

→ C(N) + C(O)

(R27)

2Cac + NO

C(N) + NO → N2 + C(O)

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C(O)

→ CO

(R28) (R29)

where C(N)＇is the nitrogen surface complex, C(O) is the oxygen surface complexes, Cac is a freshly formed site that is created after the desorption of carbon-oxygen complex by means of R29. It can be seen that R26 is the oxidation of char-N, and R27 and R28 are the reduction of NO on char surface. Therefore, the amount of NO formation in char combustion is a competitive result between the oxidation of char-N and reduction of NO. Generally, the conversion of char-N to NOx is about half the conversion expected from coal combustion [84], but the percent conversion of char-N to NOx is much lower than that of volatile-N. It seems that the reduction of NO should occur in a reducing atmosphere. However, some experimental results demonstrated that the reduction of NO on char could not be ignored during char combustion, particularly under fuel-rich conditions. The convincing evidence is that the NO yield from char is decreased with the increase of char reactivity [85,86,87]. Despite the considerable previous work in the literature about the reduction of NO by char, the mechanism of this reaction is still not clearly understood, and there are large variations in reaction data [88]. These variations can be attributed to the existence of many factors, such as the rank of the parent coal, the inherent catalytic mineral matter, mass transfer limitations at high temperatures and the effects of other gases on char reactivity. The surface area basis which is used to describe NO-char reaction is still in dispute. However, the recent research results have found that the Hg surface area is a better basis for normalizing the reactivity of different coal chars due to its less scatter in the measured values [89,90,91]. According to previous studies, the reaction of NO with coal char is found to be of first order with respect to NO [89,92,93]. Figure 14 and Table 4 summarize some first-order rate constants for NO-char reaction. The data of Aarna and Suuberg [89] were obtained by averaging a great deal of reported kinetic parameters, and any others were obtained in DTF. There are more than two orders of magnitude spread in all rate constants. But the values calculated on Hg surface area are in good agreement with each other.

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Juwei Zhang

-1

0.01

-2

0.1

-1

Rate constant (molNO m s atmNO )

1

1E-3

1E-4

1E-5

1E-6 0.5

0.6

0.7

0.8

0.9

1.0

1.1

1

Figure 14. Summary of first-order rate constants for the NO-char reaction.

Table 4. Key to Figure 14 Symbol

■

○

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▲

tilted titled ▼

Char type

Reactor

Temperature range (K)

Surface area basis

Referenc e

973-1573

Activation energy (kJ/mol) 125

High-volatile bituminous coal char (SH char) Lignite coal char (YB char)

DTF and TGA

Hg

DTF and TGA

973-1573

86

Hg

DTF

1273-1573

68

Hg

DTF

1273-1573

129

Hg

Montana lignite char Montana lignite char Subbituminous coal char Several kinds of carbons

DTF

1250-1750

147

DTF

1250-1750

137

DTF

1273-1573

120

BET (external) BET (external) BET

DTF and fixed bed

1073-1750

133

–

Low-volatile bituminous coal char

DTF

1073-1273

131

Hg

Sun et al. [91] and Zhang et al. [92] Sun et al. [91] and Zhang et al. [92] Sun et al. [91] and Zhang et al. [92] Sun et al. [91] and Zhang et al. [92] Levy et al. [94] Song et al. [93] Schonenb eck et al. [94] Aarna and Suuberg [89] Comman dre et al. [90]

Low-volatile bituminous coal char (SJ char) Anthracite coal char (YQ char)

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However, there are some factors not explicitly represented in Figure 14, for example, the presence of some catalytic metal can increase the reactivity of char and decrease the activation energies of NO-char reaction. As shown in Table 4, the activation energies of YB char and SJ char were apparently lower than others, which is attributed to their higher ash content (i.e. higher catalytic metal) than other chars. In fact, it might be more valid to express the char reactivity on a catalystsurface-area basis. But such a conversion is almost impossible, since the catalytic surface area is rarely determined.

3.5. NOx Formation Model

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A complete NOx model in coal combustion should include the three main processes (i. e. reactions R0–R5 in Figure 9). The nitrogen partition between volatiles and char during coal pyrolysis must be firstly determined before predicting the NOx formation. According to the above discussion of several coal pyrolysis models in section 1.4, the FG-DVC model should be recommended, because it associates most closely with the actual coal pyrolysis. Moreover, this model was used satisfactorily in some NOx modeling [95,96]. As for the nitrogen partitioning between HCN and NH3 in volatiles, according to the above analyses of Figure 12 and Figure 13, it is often of little importance for the NO formation. Therefore, it is not so important to know which N intermediate (HCN or NH3) should be used for modeling the NOx formation in the subsequent volatiles combustion. However, according to the analysis of influence factors of the yields of HCN or NH3 in Figure 11, HCN is the major product during the coal pyrolysis for at high temperature under most conditions, while NH3 is the major product at low temperatures and elevated pressures. In many NOx modeling studies [85,96,97], HCN was used as the only N intermediate for NOx formation during volatiles combustion and good predicted results could be obtained. Table 5. Rate constants of reactions in fuel-N conversion mechanism Reaction HCN + O2

→ NO + … HCN + NO → N2 + … NO + CHi → N2 + … NO + C

→ N2 + ……

Coal-N

Rate constant k 1.0×1010 exp(-280/RT) s-1

Reference DeSoete [98]

3.0×1012 exp(-251×105/RT) s-1

DeSoete [99]

8

3

-1 -1

1.1×10 m mol s 5.5×106 exp(-1.33×105/RT)

Glarborg [99] Aarna and Suuberg [89]

g NO m -2 h -1atm -1NO

Pyrolysis

+O2 HCN +NO

NO

+Char

CO + N2 + ……

+CHi N2

Figure 15. Simplified NOx formation mechanism in coal combustion.

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Actually, the N conversion in homogenous combustion of the volatiles has been relatively well understood, but the N conversion in heterogeneous combustion of char has proved difficult to solve. To sum up, two methods were usually used in modeling char N conversion. The first method assumes that all N in char is directly released as NO in char combustion. Then this NO can diffuse back into the particle and be reduced into N2 on carbon surface. The second method assumes that all N is released as HCN into bulk gas, This HCN then reacts homogeneously by the same way as the HCN formed by coal pyrolysis. The second method is usually considered to be more applicable if the accurate kinetic parameters of associated reactions can be obtained. Based on the above discussions of NOx formation model, the mechanism depicted in Figure 9 is simplified and the new simplified mechanism is shown in Figure 15, while the rate constants of the reactions involved in Figure 15 are listed in Table 5.

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CONCLUSION Three important processes (coal pyrolysis, char combustion and NOx formation) in coal combustion have been discussed in this chapter. The coal pyrolysis, the first process during coal combustion, can be represented by the nine steps mechanism. The yield of volatiles and the structure of residual char are influenced significantly by such heating conditions as temperature, heating rate, and pressure etc.. During the past decades, both simple kinetic pyrolysis models and detailed network pyrolysis models have been developed, however, three network pyrolysis models (FG-DVC, CPD and FLASHCHAIN model) are recognized to be more accurate and FG-DVC is even recommended due to its more fundamental description of coal pyrolysis. Char combustion has been studied extensively due to its central importance in the burnout of coal and the subsequent formation of pollutant. Both macro- and microdescriptions of char combustion are introduced in this chapter, and they are applied in global and semi-global char combustion model respectively. In order to accurately predict the burnout of coal, the CBK model which consists of char combustion, thermal annealing and physical property sub-model should be used, and the detailed semi-global char combustion model must be applied. However, there is still much work to be done before the application of semi-global model in practical conditions. The studies of NOx formation mainly focused on the nitrogen conversion during the three major processes of coal combustion, pyrolysis, volatile combustion, and char combustion. The formation pathway of NOx during coal combustion has been well understood based on these studies, and some NOx formation models were also built. Some simplification is usually conducted before using the NOx formation model. In this chapter, a simplified NOx formation pathway is introduced, and HCN is considered as the only precursor of NOx formation in this pathway. However, the heterogeneous NO-char reaction, as a very important step in NOx formation, is still not clearly understood, and there are large variations in reaction data. Therefore, future work on NOx formation during coal combustion may focus on the mechanism of NO-char reaction.

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[31] Di, BC; Buonanno, F; Branca, C. Carbon, 1999, 37, 1227-1238. [32] Zolin, A; Jensen, A; Jensen, PA; Frandsen, F; Dam-Johansen, K. Energy Fuels, 2001, 15, 1110-1122. [33] Liakos, H; Theologos, KN; Boudouvis, AG; Markatos, NC. Appl. Therm. Eng., 2001, 21, 917-928. [34] Branca, C; Di, BC. Energy Fuels, 2003, 17, 1609-1615. [35] Branca, C; Iannace, A; Di, Blasi. C. Energy Fuels, 2007, 21, 1078-1084. [36] Hurt, RH; Calo, JM. Combust. Flame, 2001, 125, 1138-1149. [37] Hurt, RH; Haynes, BS. Proc. Combust. Inst., 2005, 30, 2161-2168. [38] Fu, WB; Zhang, BL; Zheng, SM. Combust. Flame, 1997, 109, 587-598. [39] Hayhurst, AN. Combust. Flame, 2000, 121, 679-688. [40] Xu, JY; Xu, TM. Combustion theory. China Machine Press. 1990, 155-156. [41] Hurt, RH; Calo, JM. Combust. Flame, 2001, 125, 1138-1149. [42] Zhang, Q; Kyotani, T; Tomita, A. Energy Fuels, 1995, 9, 630-634. [43] Zhang, Q; Takashi, K; Tomita, A. Energy Fuels, 1996, 10, 169-172. [44] Haynes, BS; Newbury, TG. Proc. Combust. Inst., 2000, 28, 2197-2203. [45] Smith, IW. Nineteenth symposium (International) on Combustion. The combustion Institute. 1982, 1045-1065. [46] Senneca, O; Russo, P; Salatino, P; Masi, S. Carbon, 1997, 35, 141-151. [47] Hurt, RH; Sun, JK; Lunden, M. Combust. Flame, 1998, 113, 181-197. [48] Salatino, P; Senneca, O; Masi, S. Energy Fuels, 1999, 13, 1154-1159. [49] Zolin, A; Jensen, A; Johansen, KD. Proc. Combust. Inst., 2000, 28, 2181-2188. [50] Russell, NV; Gibbins, JR; Man, CK; Williamson, J. Energy Fuels, 2000, 14, 883-888. [51] Zolin, A; Jensen, A; Johansen, KD. Combust. Flame, 2001, 125, 1341-1360. [52] Senneca, O; Salatino, P; Masi, S. Proc. Combust. Inst., 2005, 30, 2223-2230. [53] Senneca, O; Salatino, P; Menghini, D. Proc. Combust. Inst., 2007, 31, 1889-1895. [54] Oberlin, A. Carbon, 1984, 22, 521-. [55] Williams, A; Pourkashanian, M; Jones, JM. Proc. Combust. Inst., 2000, 28, 2141-2162. [56] Baum, MM; Street, P. J. Combust. Sci. Technol., 1971, 3, 231-. [57] Floess, JK; Lee, SA; Oleksy, SA. Energy Fuels, 1991, 5, 133-138. [58] Gopalakrishnan, R. Bartholemew, CH. Energy Fuels, 1996, 10, 689-695. [59] Sorensen, LH; Gjernes, E; Jessen, T; Fjellerup, J. Fuel, 1996, 75, 31-. [60] Coda, B. Tognotti, L. Exp. Therm Fluid Sci., 2000, 21, 79-86. [61] Hurt, RH; Calo, JM. Combust. Flame, 2001, 125, 1138-1149. [62] Everson, RC; Neomagus, HWJP; Kasaini, H; Njapha, D. Fuel, 2006, 85, 1067-1075. [63] Kulaots, I; Hsu, A; Suuberg, EM. Proc. Combust. Inst., 2007, 31, 1897-1903. [64] Hurt, RH; Haynes, BS. Proc. Combust. Inst., 2005, 30, 2161-2168. [65] Sun, JK. Ph.D. Thesis, Brown University, USA, 2000, 32-61. [66] Mitchell, RE; Ma, LQ; Kim, B. J. Combust. Flame, 2007, 151, 426-436. [67] Bhatia, SK; Perlmutter, DD. AICHE J, 1980, 26, 379-386. [68] Miller, JA; Bowman, CT. Pro. Energy Combust. Sci., 1989, 15, 287-338. [69] Glarborg, p; Miller, JA; Kee, RJ. Combust. Flame, 1986, 65, 177-202. [70] Taniguchi, M; Yamamoto, K; Kobayashi, H; Kiyama, K. Fuel, 2002, 81, 363-371. [71] Wojtowicz, MA; Pels, JR; Moulijn, JA. Fuel, 1995, 74, 507-516. [72] Kelemen, SR; Gorbaty, ML; Kwiatek, P. J. Energy Fuels, 1994, 8, 896-906. [73] Kelemen, SR; Gorbaty, ML; Kwiatek, PJ; Fletcher, TH; Watt, M; Solum, MS; Pugmire,

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Chapter 2

FUNDAMENTAL RESEARCH ON OXY-FUEL COMBUSTION: THE NOX AND COAL IGNITION REACTIONS, PART I Masayuki Taniguchi* and Kenji Yamamoto** Combustion Systems Group Coal Science Project Energy and Environmental Systems Laboratory, Power Systems Company, Hitachi, Ltd.Position: Senior researcher

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1. INTRODUCTION At COP13 held in Bali in December-2007, the framework for cubing-global warming were taken after 2013. The development of the technologies that are necessary for cubingglobal warming must be accelerated through worldwide cooperation.

Figure 1. The prediction of worldwide energy demand (from IEA “World Energy Outlook: 2007” and cited in [ref.1]).

*

Corresponding author: 7-2-1 Omika-cho, Hitachi-shi, Ibaraki-ken, 319-1292, [email protected], Phone: * +81-29-276-5889 Fax: * +81-29-276-5784. ** [email protected] Coal Combustion Research, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

Japan.

(E-mail)

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Masayuki Taniguchi and Kenji Yamamoto

The IEA (International Energy Agency) has predicted that by 2030 the world’s population will be about 1.6 times that of 2004. The energy consumption will increase by 60% by 2030. Figure 1 shows predicted energy consumption in 2030 for various uses (cited in [1]). The percentage consumed in power generation is expected to be the largest and will be 45%. Wind turbine generation and photovoltaic power generation are expected to increase tenfold by 2030, but their combined percentage of the generation will only total 7% [1]. The expectation for nuclear power generation is large, but under present conditions, the amount of natural uranium resources will have a share for only 80 years [1]. Development of the fast breeder reactor is being pushed forward, but it is not expected to be commercial before about 2050 [1]. Therefore, it will continue to be necessary to depend on fossil fuel. Carbon dioxide capture and storage (CCS) techniques for thermal power plants will surely become important. Oxy-fuel combustion (CO2/O2 combustion) is one of the promising technologies for CCS in coal power plants. Nitrogen is separated beforehand from air to be used for combustion and coal is burnt using the oxygen-containing fraction. The major components of the exhaust gas are H2O and CO2. CO2 can be collected without having to separate it from the nitrogen and H2O of the exhaust gas. A part of the exhaust gas is circulated in a system to control combustion conditions such as flame temperature. An example of the oxy-fuel combustion system [2] is shown in Figure 2. Oxy-fuel combustion technology is applied for pulverized coal combustion. Nitrogen is separated by the ASU (air separation unit) from air to be used for combustion. Only oxygen is supplied to a boiler. Steam is formed by heat exchange with the flame in the boiler. The generated steam drives a steam turbine and generates electricity. The harmful species to atmosphere environment in the combustion exhaust gas are removed by De-NOx, ESP, and De-SOx. The major components of the flue gas are CO2, H2O and a little oxygen. Liquid CO2 is provided after H2O is removed by compressing the exhaust gas, using a dryer. This system looks like a conventional pulverized coal firing boiler. However, the point that the combustion supporting gas becomes O2-CO2-H2O differs from air combustion systems greatly. CO2 and H2O are not always inert towards chemical reactions. It is necessary to determine any influence they may have on the combustion reaction. In addition, specific heats of CO2 and H2O are larger than that of nitrogen It is necessary to determine the influence on heat transfer performance. The emissivities of CO2 and H2O are also different from that of nitrogen [3]. In the present investigation, we examined influences on chemical reactions. We focused on the NOx and coal ignition reactions. In the oxy-fuel combustion system, many plants will be developed by using numerical computations, because there are no actual plants yet. As computers become even more sophisticated, their use is increasing for making the numerical analyses needed in designing pulverized coal firing boilers [4-10]. Numerical analyses were first applied to design heat exchanger such as predictions of the steam temperature [4]. Recently, they have been applied to environmental performance factors, such as NOx emission [5-9] and to control of furnace wall corrosion [10]. In this paper, we introduce combustion models for the NOx and coal ignition reactions. We also show examples of case studies for an oxy-fuel combustion system by using the proposed models.

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Figure 2. Schematic of a pulverized coal fired oxy-fuel power plant system [ref.2].

2. NOX REACTION MODEL

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2.1. A Key Index for Nox Reduction in Fuel-Rich Conditions NOx reduction in flames has been extensively investigated. However, for combustion of coal, NOx performance changes easily with the burning conditions, such as coal properties and coal particle diameter. Boiler design and development cannot be done efficiently if it is necessary to change the method of NOx reduction for each coal property. Then, we proposed a key index to estimate NOx reduction performance. NOx is reduced both gas phase and solid phase reactions for pulverized coal combustion [5]. We tried to analyze the experimental data by dividing them into the associated gas phase and solid phase reactions. Especially, we focused on the gas phase reaction. In the present study, we proposed a gas phase stoichiometric ratio (SRgas) as the key index. Figure 3 is a schematic representation of SRgas. Before combustion, all fuel components of pulverized coal are in the solid phase (Figure 3a). Pulverized coal particles are surrounded by combustion supporting gas such as air. Inlet stoichiometric ratio (SRin) is often used as an index showing burning conditions. The inlet stoichiometric ratio is generally defined by equation (1) and all the fuel is in solid, liquid and gas phases.

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Figure 3. Definition of gas phase stoichiometric ratio (SRgas).

SRin ≡ amount of fuel required for stoichiometric combustion /amount of fuel actually supplied

(1)

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After coal is ignited, part of the fuel components move from the solid phase to the gas phase by pyrolysis, oxidation, and gasification reactions. The remaining fuel components stay in the solid phase. An image is shown in Figure 3b. The gas phase stoichiometric ratio is the index which focuses on the amount of fuel components which moved from the solid phase to the gas phase. The gas phase stoichiometric ratio (SRgas) is defined by equation (2). SRgas ≡ amount of fuel required for stoichiometric combustion / amount of gasified fuel (2) Here, amount of gasified fuel means both the amount of fuel that has moved from the solid phase to the gas phase by pyrolysis, oxidation, and gasification reactions, and, the amounts of gas and liquid fuel supplied to the combustible mixture. We do not consider the fuel components which are left in the solid phase. The gas phase reaction rate exceeds the solid phase reaction rate. The inlet stoichiometric ratio is a good index which shows the difference in the burning conditions. We thought a numerical analysis might become easier when solid is removed from the burning mixture and a stoichiometric ratio is defined to consider the gas phase reaction. The SRgas can be obtained by analyzing the mass balance of H, C and O in the burning gas. Sometimes, it is difficult to analyze mass balance correctly, because the amount of water is difficult to measure. At this time, SRgas can be obtained approximately as equation (3), too.

(3)

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Here, [O2], [CO], [H2], and, [CH4] are O2, CO, H2, and, CH4 concentrations in the burning gas. [O2]0 is the average concentration of O2 in the combustion supporting gas. [TR] is the concentration of tracer. N2, Ar and He are examples of suitable tracers because their amounts will hardly be changed by chemical reactions. In the present study, we used N2. [TR]0 is the concentration of tracer in the combustion supporting gas. The relation between the gas phase stoichiometric ratio (SRgas) and NOx concentration was investigated using a drop-tube furnace [11]. A schematic drawing of the drop-tube furnace is shown in Figure 4. Pulverized coal and all of the combustion gas were pre-mixed, and then injected to the furnace through a nozzle (inner diameter: 6mm). The nozzle was cooled by water to prevent pyrolysis of coal particles before injection. The nozzle was covered with firebrick and an SiC tube to prevent too much cooling of the injected gas. Flow rate of combustion supporting gas was 0.96Nm3/h. Coal feed rate was varied for each experiment from 0.02-0.5 kg/h. The reaction tube was made of alumina and had an inner diameter and length of 50mm and 1200mm, respectively. Four electric heaters were arranged around the reaction tube. Temperature of each heater could be controlled independently in order to keep the temperature distribution of the tube wall constant. The axial temperature distribution of the heated gas was measured along the center axis of the reaction tube. The difference between the wall and gas temperatures was ±50K. Heating rate of the combustion gas was around 10000 - 15000K/s. The heating rate was estimated by CFD calculations [7,11]. A sampling probe collected all of the burned gas and char. Usually, this probe was set 800mm downstream from the nozzle. The reaction time was around 1.0s. The axial sampling position was varied for some experimental conditions. Cooling water was injected into the probe, and the combustion reactions were quenched.

Figure 4. Schematic of the drop-tube furnace [ref.11].

Char was collected by filtration through a 7μm pore paper filter. C, H, N and ash contents in the char were analyzed. Coal burnout was obtained by assuming that the amount of ash

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remained constant during combustion. Concentrations of HCN and NH3 were obtained from the concentrations of NH4+ and CN- in the water in a trap and total gas flow rate. Figure 5 plots NOx concentration characteristics obtained by the drop-tube furnace experiments. The results were obtained for a variety of coal properties, burning temperatures, and compositions of combustion supporting gas. Relationships between both SRin and NOx, and SRgas and NOx are shown in the Figure When the data were analyzed by SRin, NOx concentration varied with experimental conditions. However, when the data were analyzed by SRgas, the difference of NOx concentration became small and NOx was hardly influenced by the burning conditions when SRgas was less than 1.0. We judged that the SRgas was a good index which estimated NOx concentration in fuel-rich conditions. We further thought that the gas-phase NOx reduction was the key reaction when SRgas was smaller than 1.0; that is, the mechanism of the gas reaction did not depend so much on coal properties and there were many common points.

Figure 5. Relationships between NOx concentration for the drop-tube furnace experiments and a) inlet stoichiometric ratio and b) gas phase stoichiometric ratio.

The NOx was also fixed by the gas phase stoichiometric ratio (SRgas) for oxy-fuel combustion. The NOx could be decreased by both air and oxy-fuel combustion, if SRgas could be lowered. Rate of Gasification reactions influences how much SRgas can be reduced in the fuel rich flame. If the rates of gasification reactions (equation (4) and (5)) are large, SRgas in the fuel rich flame can be easily decreased. Char + CO2 → 2CO

(4)

Char + H2O → H2 + CO

(5)

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Char in coal is oxidised by gasification reactions in the fuel rich flame, because there is little oxygen in a flue gas of fuel Rich flame. Gasification reaction of the (4) is accelerated for oxy-fuel combustion, because CO2 concentration is very high. Figure 6 shows the relationship between SRin and coal burnout and the relationship between SRin and SRgas. The experimental results were compared when the same coal burnt with CO2/O2 atmosphere and with in the air. Figure 6a shows the relation between SRin and coal burnout. At first, we compared the results when coal was burned at the same oxygen concentration (21vol%) and burning temperature (1673K). Under these experimental conditions, the contribution of oxidation reaction was the same both for air combustion and oxy-fuel combustion. When SRin was small, coal burnout of oxy-fuel combustion was larger than that of air combustion. This was because the contribution of the gasification reaction increased for oxy-fuel combustion, so the amount of gasified fuel increased. When the amount of gasified fuel increased, SRgas decreased easily. If SRgas decreased, NOx concentration decreased easily. Effect of inlet oxygen concentration was also examined for oxy-fuel combustion (O2=21, 30vol%). The oxidation reaction was also accelerated by increasing the inlet oxygen concentration. When the inlet oxygen concentration was larger, coal burnouts in the fuel-lean condition (SRgas>1.0) also became larger. When coal burnout increased in the fuel-rich condition (SRgas1)

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Equation (14) is Qrad = QradW +β (b-1) QradW.

(16)

When equation (14) is substituted for equation (16), Qin can be expressed by equations (17) and (18). Qin = Q - QradW + β(1-b)QradW =αQ

(17)

α = 1- QradW/Q+β(1-b)QradW/Q

(18)

When equation (13) is substituted for equation (18), equation (19) is obtained. 1/Cmin = (a/Qmin) Qvm - (QradW/Q) (a/Qmin) Qvm +β(1-b)(QradW/Q) (a/Qmin) Qvm

(19)

If the same burner and furnace are used and only the area fraction covered with a caster is varied, then only β in equation (19) changes. A straight line relation is obtained between the reciprocal of the minimum fuel supply (1/Cmin) and the fraction of the area covered with the caster (β). The effect of heat loss on the condition of flame stabilization (such as blow-off limit) can be estimated from the relation between the measured 1/Cmin and β. When small-sized equipment is used, the effect of heat loss also can be estimated by heating the flame from surroundings by using devices such as lasers. When heating rate from the surroundings is defined as QradL for the case of a laser use, total heat loss (Qrad) can be expressed by equation (20).

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Qrad = QradW – QradL

(20)

When equation (17) is substituted for equation (20), equations (21) and (22) are obtained. Qin = Q - QradW + QradL =αQ

(21)

α= 1 - QradW/Q + QradL/Q

(22)

When equation (13) is substituted for equation (22), equation (23) is obtained. 1/Cmin = (a/Qmin)*Qvm - (QradW/Q) (a/Qmin)*Qvm + QradL(1/Q) (a/Qmin)*Qvm

(23)

A straight line relationship is obtained between the reciprocal of the minimum fuel supply (1/Cmin) and heating rate from the surroundings (QradL).

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Figure 24. Effect of radiant heat flux from surroundings on the lean flammability limit for the laser ignition experiments.

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We used a continuous wave laser for an energy source. Figure 24 shows the relation between laser energy and lean flammability limit. A straight line relation was observed between the laser energy (radiant heat flux from the continuous wave laser) and the reciprocals of the lean flammability limit.

Figure 25. Structure of the furnace for blow-off experiment of large scale equipment [ref.29].

Kiga et al. [29] measured the blow-off limit for pulverized coal flames by using largescale equipment. We examined the effect of heat loss on blow-off limit more by analyzing their experimental results. Structures of the experimental equipment are shown in Figure 25 [29]. Part of the pilot-scale furnaces was covered with caster. Blow-off limits were measured in three pilot-scale furnaces and an actual boiler by using the same design of burners. Coal feed rate of the pilot-scale furnaces was 3.2t/h. Changing the area of the caster varied the heat loss rate. For the experiment with the actual boiler, blow-off limit was measured when two sets of burners were operated. All the wall of the actual boiler was covered with a water wall. Judging from one flame, there was one part surrounded by the water wall directly, and there

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was another part that between one flame and the water wall. We assumed that heat loss rate was reduced to the same level as the caster wall if there was another flame between one flame and the water wall. We assumed that this area was covered with caster. The value of β became 0.0625. The relation between β and the blow-off condition of the minimum fuel supply for stable combustion, 1/Cmin, shown in Figure 26, was a straight line. The effect of the heat loss rate on blow-off limit could be estimated by using the proposed method. Blow-off limit for actual boilers could be predicted from experimental results for pilot-scale furnaces. Plural burners are usually operated for actual boilers. Blow-off limit and heat loss rate change depending on which burners are operated. In this case, the effect of heat loss rate can be estimated with the proposed method shown in Figure 25. According to the proposed model, the effect of coal properties on blow-off limit is expressed by equation (13) in which the reciprocal of the minimum fuel supply to stabilized flame (Cmin) is proportional to volatile content of coals as the calorific value (Qvm). The volatile content of the mass standard is roughly proportional to the volatile content of coals as the calorific value. Figure 27 shows the relation between volatile content of coal and Cmin. A straight line relation was observed between volatile content of coal and Cmin (or lean flammability limit). The relation shown by equation (13) was almost realized.

Figure 26. Effect of the β value (the ratio of an area covered with the caster judged from the viewpoint of the burner nozzle) on minimum fuel supply for stable combustion. The experimental data were obtained from ref.30.

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Figure 27. Effect of volatile content of coal on minimum fuel supply for stable combustion.

Using the proposed model and an experimental database, we calculated the lean flammability limit in a large-scale burner [28]. Figure 28 shows the relation between calculated and experimental blow-off limits. The calculation included the effect of heat loss, volatile content of coal, and particle diameter distribution. The calculated results agreed well with the experimental results.

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4. A CASE STUDY FOR OXY-FUEL COMBUSTION SYSTEMS In this section, we introduce examples of numerical analyses for oxy-fuel combustion systems obtained by using the proposed models shown in Sections 2 and 3 and the CFD program shown in Section 2. In particular, we examined how to operate an oxygen combustion system when low-rank coal is used as fuel. Low rank coals, such as anthracite, are very hard to ignite when they are used for air combustion. Coal properties used for analyses are shown in table 1. Coal A is an example of an anthracite coal. Coal A is not used in pulverized coal filing boilers so much, because it is inferior in its firing and combustion performance. Coal B is an example of hv bituminous coal and it is widely used in pulverized coal fired boilers. We examined the system constitution of the oxygen combustion boiler by numerical analysis when these coals were used as fuels. Combustion performance parameters, such as ignition performance, heat absorption by the furnace wall, combustion efficiency (i.e. unburned carbon content in fly ash), and NOx emission were predicted for several furnace structures and operating conditions. Table 1. Coal properties used for numerical analyses Coal

HHV (MJ/kg)

Water (wt%)

A B

22.7 25.8

10 14

VM ash (wt%, dry) 6.7 29 35 11.5

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C 91.6 81.9

H O N S (wt%, dry ash-free) 3.8 5.3

1.7 10.1

1.7 2.2

1.2 0.5

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(a)

(b) Figure 28. (a) Relation between calculated and experimental blow-off limits.

Ignition performance was examined at first. Figure 29 shows calculated relations between coal concentration and flame propagation velocity. The lines in the figure show flame propagation velocities in air atmosphere. Lean flammability limit is defined as the coal concentration when flame propagation velocity becomes zero. It is necessary to raise coal concentration in carrier gas (a mixture of primary air and coal) from the lean flammability limit in order to form a stable flame. The flame propagation velocity needs to be raised more for low-NOx combustion. The coal concentration should be raised as much as possible. But,

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the upper limit of the coal concentration is limited by the coal pulverizer used. In general, it is difficult to raise average coal concentration more than 1.5kg/m3.

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Figure 29. Calculated relations between particle concentration and flame propagation velocity. Temperature of the furnace wall was 1300K.

Case 1 in Figure 29 showed flame propagation velocity when coal B was burned in air. This type coal is widely used in air combustion boilers. We judged that ignition and combustion performances would be good enough if the flame propagation velocity was the same as used for case 1. Case 2 in Figure 29 showed flame propagation velocity when coal A was burned in air. Flame propagation velocity was too low to obtain a stable flame for pulverized coal combustion. Case 3 was the results when the particle diameter of coal A was fine. Usually, coal diameter must be decreased when ignition performance of the coal is inferior. The flame propagation velocity grew large when the coal diameter was decreased. Lean flammability limit became low. But the ignition performance was considerably inferior compared with coal B even if the coal diameter was fine. Increasing coal concentration is required if coal A is used for air combustion systems, but, it is difficult to secure sufficient ignition performance. The geometric symbols in Figure 29 show results for cases 4 to 6 of different flame propagation velocities in CO2/O2 atmosphere. Case 4 gave the flame propagation velocity of coal B for oxy-fuel combustion when oxygen concentration was 21vol%. The flame propagation velocity was around 1/3 of its value for air combustion. It would be necessary to increase the oxygen concentration or coal concentration in carrier gas in order to obtain the same ignition performance as that of air combustion. Coal concentration should be about three times larger than that of air combustion if oxygen concentration was 21vol%. Case 5 shows flame propagation velocity of coal B when oxygen concentration was increased. Both flame propagation velocity and lean flammability limit were almost equal with those of air combustion by controlling oxygen concentration. In this case, ignition performance was equal

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with that of air combustion when oxygen concentration was around 30vol%. But the appropriate oxygen concentration varied with the coal property and diameter. Case 6 showed flame propagation velocity of coal A for oxy-fuel combustion. Coal diameter was the same as that of Case 3. Oxygen concentration was more than 30vol%. For oxy-fuel combustion, oxygen concentration in combustion supporting gas can be changed easily. This is one of the advantages of oxy-fuel combustion. By combining this advantage with a fine grain coal, even if it was coal A (anthracite), good ignition performance that was near that of coal B (hv bituminous coal) was provided. We examined the arrangement of gas ports (burners and gas ports for staged combustion) next. We also examined the distribution of oxygen and flue gas (combustion exhaust gas supplied by recirculation) supply to the gas ports. We wanted to obtain a uniform heat flux distribution to the furnace wall. Figure 30 shows the effect of the arrangement of the gas ports and the distribution of oxygen and flue gas supply on heat flux distribution to the furnace wall. Figure 30a shows heat flux distribution when the ratios of oxygen and flue gas supply to all gas ports were the same. Heat flux distribution in the burner part was high and the heat flux at the upper part of the furnace was low. If the heat flux distribution was highly irregular, it was easy to get an irregular temperature distribution in the steam generated from the furnace. In addition, the life of the furnace would be shortened if the heat flux distribution was very irregular. Figure 30b shows calculation results when the ratio of oxygen and flue gas supply was varied at each gas port. The ratio of oxygen and flue gas supply to the burners decreased in order to decrease heat flux in the burner part. In this case, the lengthwise direction of the burner and the burner were also enlarged. The heat flux distribution varied with the ratio of oxygen and flue gas supply at each gas port. The adjustment of the irregularity of the heat flux distribution would be difficult for air combustion, because it is hard to change oxygen concentration. For the oxy-fuel combustion, it could get closer to a uniform heat flux distribution by a comparatively simple adjustment.

Figure 30. Heat flux distribution to the furnace wall obtained with different operating conditions

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Figure 31 shows schematic side views of the furnaces and example temperature distributions. Figure 31a is the shape of a usual boiler (furnace 1). This is widely used for air combustion. Staged combustion was used to reduce NOx emission. Six sets of burners were arranged in the furnace. All the wall of the furnace was a water wall. Temperature of the water wall was assumed as 673K for the calculation. We chose the most suitable position according to the coal properties. The position of the gas ports (burners and gas ports for staged combustion) in Figure 31a was used when coal B was burnt in air. The furnace shown in Figure 31b (furnace 2) was used when coal A was burnt in air in order to improve the ignition performance. A caster was installed in the wall of the burner part to reduce the heat loss to the wall from the flame. The flame temperature in furnace 2 became higher in comparison with that in furnace 1.

Figure 31. Schematic side views of the furnaces and examples of temperature distribution.

The flame propagation velocity was calculated by the proposed model shown in Section 3. Calculation conditions are shown in table 2. For coal A, particle concentration was increased and particle diameter was decreased. Oxygen concentration in the carrier gas was increased for oxy-fuel combustion. Wall temperature and β are also shown in the table. The value of β varied with furnace shape. The evaluation for β is explained schematically in Figure 32 and was the same as that shown in Figure 25 the blow-off experiment. In the burners of furnace 1, there were many parts facing the water wall. Lean flammability limit was calculated at first. The influence of the wall temperature on lean flammability limit was corrected for with the results of Figure 24. Maximum flame propagation velocity was evaluated from the results of Figure 20 (relation between lean flammability limit and

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maximum flame propagation velocity). The relation between flame propagation velocity and coal concentration was estimated from the results of Figure 18.

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B

5

A

Wall temperature (water wall)

4

Wall temperature (caster or flame)

A

Β

3

21vol %

air

1.0 kg/m3

1

0.188

1573K

673K

21vol %

air

1.0 kg/m3

1

0.188

1573K

673K

21vol %

air

1.0 kg/m3

2

1.00

1573K

673K

around 30 vol %

oxyfuel

1.0 kg/m3

1

0.188

1573K

673K

larger than 30 vol %

oxyfuel

1.5 kg/m3

1

0.188

1573K

673K

Furnace

A

coal concentration in carrier gas

2

under 200mesh; 81wt% under 200mesh; 91wt% under 200mesh; 91wt% under 200mesh; 81wt% under 200mesh; 91wt%

Combustion system

B

Oxygen concentration

Coal

1

Diameter

CASE No.

Table 2. Operating conditions used for evaluation of flame propagation velocities

Figure 32. Evaluation of β value for the furnaces

Figure 33 shows our calculated results. Cases1, 2 and 3 were calculation results of air combustion. Cases 4 and 5 were results of oxy-fuel combustion. Flame propagation velocity, NOx emission, unburned carbon content in fly ash, and, heat absorption by the furnace wall are shown in the Figure.

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Figure 33. Calculated results of case studies.

Flame propagation velocities were shown as the value that was corrected for the effect of heat loss rate for a burner installed at the position where heat loss rate was the largest as shown in Figure 32. Case 1 showed the calculation results when coal B (hv bituminous) was burned in air. Ignition performance of other cases was evaluated against case 1 as the standard. Flame propagation velocities of coal B in air (cases 2 and 3) were inferior to case 1. The flame propagation velocity of case 2 (without caster) was particularly small. Stable combustion was difficult considering the load changes of the boiler. The flame propagation velocity of case 3 (with caster) was improved by around 70% compared to case 1. When coal A (anthracite) is used in an air combustion boiler, it is necessary to prevent heat loss to the furnace wall from the flame. Cases 4 (coal B) and 5 (coal A) were results of oxy-fuel combustion. Flame propagation velocities of both coals A and B rose by controlling oxygen concentration in the carrier gas (primary gas) adequately. We evaluated NOx emission, unburned carbon content in fly ash, and heat absorption by the furnace wall next. When coal A was burned in air, NOx emissions and unburned carbon in fly ash increased in comparison with case 1. The caster was installed in case 3 to improve the ignition performance. NOx emissions of case 3 rose very much because burning temperature became higher and the formation rate of thermal NOx increased. Heat absorption by the furnace wall was reduced because the caster was installed. When coal A was burned in air, it was difficult to obtain equal combustion performance to that of case 1. However, when coal A was used for oxy-fuel combustion (case 5), NOx emissions and unburned carbon in fly ash could be decreased significantly. The NOx emissions were close to those of case 4 (coal B for oxy-fuel combustion). Unburned carbon in fly ash was inferior to that of case 4, but was at the same level as case 1 (coal B for air combustion). Heat absorption by the wall was the same level as case 1. A high-efficiency and low-emission combustion system was possible for oxy-fuel combustion, even if the coal used was difficult to ignite and burn.

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5. CONCLUSION 1. The NOx reaction mechanism was investigated for air combustion and oxy-fuel combustion. We proposed the gas phase stoichiometric ratio (SRgas) as a key index to evaluate NOx concentration in fuel-rich flames. The SRgas was defined as:

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SRgas ≡ amount of fuel required for stoichiometry combustion / amount of gasified fuel where the amount of gasified fuel was defined as the amount of fuel which had been released to the gas phase by pyrolysis, oxidation and gasification reactions. When SRgas300 MWth 200 200

1.3. Formation of NOX during Combustion NOx are formed during combustion through complicated reactions from two sources: the molecular nitrogen in the combustion air and the fuel-nitrogen (fuel-N, mainly organic nitrogen) which is contained in some fuels such as coal, biomass and petroleum oils. Combustion of gaseous fuels that do not contain fuel-N usually produces less NOx than the combustion of fuel-N containing fuels such as coal and oil. There are three routes for the formation of NO from nitrogen in combustion air:

• • •

Thermal NO Prompt NO Formation of NO through intermediate component N2O

Relative importance of these three routes is dependent on the operating conditions and fuel. In most practical combustion devices the thermal NO and fuel-NO are the main routes.

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1.3.1. Thermal-NO Formation Thermal NO is formed through the extended Zeldovich mechanism [23]: O + N2 ⇔ NO + N

(R1)

N + O2 ⇔ NO + O

(R2)

N + OH ⇔ NO + H

(R3)

The contribution of reaction (R3) is small for lean mixture, but for rich mixtures it should be considered. Forward reaction of (R1) controls the system, but it is slow at low temperatures (because of high activation energy). Thus it is effective in post-flame zone where temperature is high and the time is available. Concentrations up to 1000 to 4000 ppmv thermal-NO can be observed in uncontrolled combustion systems. From reactions (R1-R3), the rate of formation of thermal NO can be calculated:

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d [ NO] = k1 f [O][ N 2 ] − k1r [ NO][ N ] + k 2 f [ N ][O2 ] dt − k 2 r [ NO][O] + k 3 f [ N ][OH ] − k 3r [ NO][ H ]

(E1)

where k1f, k2f, and k3f are the forward rate coefficients, and k1r, k2r, and k3r are the backward rate coefficients, of reaction (R1-R3). To calculate the NO formation rate, we need to know the concentrations of O, N, OH, and H radicals which can be computed using detailed kinetic mechanisms for the fuel used. For very approximate calculations, however, these may be assumed to be in chemical equilibrium. At moderately high temperatures, N does not stay at thermodynamic equilibrium and a better approximation could be to assume N to be at steady state. With the assumption of N radicals are at steady state, we have d [ NO] = k1 f [O][ N 2 ] − k 2 r [ NO][O] − k 3r [ NO][ H ] + (−k1r [ NO] + k 2 f [O2 ] dt ⎛ k [O][ N 2 ] + k 2 r [ NO][O] + k 3r [ NO][ H ] ⎞ ⎟ (E2) + k 3 f [OH ]) × ⎜ 1f ⎜ ⎟ k1r [ NO] + k 2 f [O2 ] + k 3 f [OH ] ⎝ ⎠ d[NO] When [NO] 900 C

+

SO2/HCl

MgO

+

CHi

SO2/HCl NO

CaSO4/ CaCl2

MgSO4/ Mg/Cl2 High O2

NO

Sulphur and chloride capture

Figure 42b. Schematic of overall reduction processes with CMA. Coal Combustion Research, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

HCN

UREA

Low O2

N2

Reburn and Advanced reburn NOx reduction

150

Bill Nimmo and Hao Liu 900 800 700

NO, ppm

600 500 400 300 λ1=1.4

200

λ1=1.15 λ1=1.05

100 0 100 90

λ1=1.4

NO Reduction, %

80

λ1=1.15 λ1=1.05

70 60 50 40 30 20

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10 0 0

5

10

15

20

25

Rff, % Figure 43. Basic reburn. Influence of CMA feed rate on NO reduction for primary zone stoichiometries (λ1=1.05,1.15 and 1.4).

4.4.1. Basic CMA Over Coal Reburning Reductions in NO by CMA reburning are shown in Figure 43 for primary zone stoichiometries of 1.05, 1.15 and 1.4 and are indicative of the reductions that can be achieved with CMA alone. All NO concentrations presented are corrected to 3% O2. The data are shown for comparison with reductions achieved with urea addition and discussed later. The feed rate of CMA is expressed as a percent of the total combustibles fed to the furnace and indicated as Rff in Figure 43. The general trend of increasing NO reductions with increasing Rff are consistent with previously reported data for coal [109, 40]. Optimum NO reductions were obtained at λ1=1.05 of up to 75% at Rff = 17.3% with lesser reductions at λ1=1.15; 5% to 12 % lower across the range of Rff studied. The poorest reduction was reported to be 37%

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at λ1=1.4 at Rff=20.7. It is clear that the primary zone stoichiometry has a strong influence on the reduction chemistry of the reburn zone. Previous optimization studies [109] showed that with bituminous coal as the reburn fuel, λ1=1.05 gave best performance which increased almost linearly up to Rff = 18% and thereafter began to level off and peaking at about Rff=20% with marginal improvement in reductions of up to 5% (max.). The results presented here for CMA reburn show signs of the same leveling off in NO reductions as the reburn zone stoichiometry becomes more fuel-rich and is partly dependent on λ1. Over the range of Rff studied, λ2 is always fuel-lean at λ1=1.4, always fuel-rich at λ1=1.05 and changes from fuellean to fuel-rich as Rff is increased at λ1=1.15. The clear influence of reburn zone stoichiometry is shown in Figure 44a and the overlapping trend lines at different primary zone conditions is related to the formation of CHi radicals and the competing reaction mechanisms of a) destruction by oxidation or b) reaction with NO. Therefore there is a band of tolerance for O2 penetration into the reburn zone. If the penetration is too great (fuel-lean), then any available CHi is destroyed and if penetration is too low (fuel-rich) there will be insufficient generation of CHi for NO reduction.

100

(a)

λ1=1.05 λ1=1.15 λ1=1.4

(b)

λ1=1.05 λ1=1.15 λ1=1.4

80 60

NO reductions, %

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40 20

0 100 80 60 40 20

0 0.8

0.9

1.0

1.1

1.2

1.3

1.4

λ2 Figure 44. Influence of primary zone (λ1) and reburn zone stoichiometry (λ2) on NO reduction. (a) Basic CMA reburn. (b) Advanced CMA/urea reburn at NSR=2.5.

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4.4.2. Cma/Urea Advanced Reburning Having identified the optimum conditions for reburning previously described, tests were performed using urea co-injected with CMA solution into the reburn zone for further NO reduction by SNCR in an advanced reburn-rich (AR-rich) configuration. This method utilized the good atomization performance of the CMA nozzles ensuring intimate mixing of urea with the combustion gases. The overall process of advanced reburning using CMA/urea is shown schematically in Figure 42 with the experimental configuration reported in this section. The objective of the study was to identify the optimum conditions for NO and SO2 reduction from advanced reburning using CMA and urea. A number of operational parameters influence the efficiency of NO and SO2 reductions. Primary zone stoichiometry was fixed at three levels λ1=1.05, 1.15 and 1.4, which affects the oxygen concentration entering the reburn zone at the critical point of CHi and NHi formation. Aqueous urea solution was blended into the CMA solution and injected via twin-fluid atomizers at the end of the primary zone initiating a reburn reaction zone for NO reduction. In addition, the reduction of SO2 begins here and continues into the burnout zone. Modifying the feed rate of CMA alters the stoichiometry in the reburn zone (λ2) having the effect of increasing the concentration of CHi relative to the NO entering from the primary zone. CMA has an air requirement for complete combustion of about one half that of Daw Mill coal, therefore, a coal equivalent reburn fuel fraction (Rff) can be calculated to enable comparison with published data for other fuels. Increasing the CMA feed rate also increases the calcium to sulphur ratio (Ca/S), which affects the degree of SO2 removal and is, hence, intrinsically linked with NO reduction. MgO also plays a part in SO2 reduction but to a lesser degree than CaO at the high temperatures and short residence times which prevail in the 80kW furnace reported here, therefore, SO2 reduction data will only be presented as a function of Ca/S. It is possible, however, that MgO may contribute to a larger extent in the burnout zone where temperatures are typically 800-1000oC [121]. Figure 45 shows the effect of increasing NSR on NO reduction. In all cases the urea feed rate (NSR) was determined by the emission concentration of NO which differed depending on Rff. Results were obtained at λ1 = 1.05, 1.15 and 1.4 for Rff of 5%, 9.5%, 13.6% 17.3% and 20.7%. It is possible to extrapolate the data to slightly higher Rff (i.e. 20.7, Ca/S = 2.5) for results at λ1 = 1.05 and 1.15 assuming little variation over Rff = 18%. It is also clear that the benefit of increasing NSR at high Rff is low since the difference between basic and advanced reburn diminishes as CMA input is increased. At an Rff of 10% the improvement at NSR = 2.5 was in the region of 30% for all three primary zone stoichiometries (Figure 46). For λ1 = 1.4 this level of improvement also extended to the higher levels of Rff whereas at the other λ1 conditions the improvements fell to less than 10% with increasing Rff. The highest reductions of 85% were achieved at NSR = 2.5, Rff=20.7% and λ1 = 1.05 but were only marginally higher compared with reductions of 82% at Rff=14%. Significant differences in improvement of about 5% and 15% are observed when the same comparison is made at λ1 = 1.15 and 1.4 respectively. This result is important since similar reductions can be obtained at lower CMA consumption rates. In contrast to reported results for co-injection of ammonia and methane [165] advanced reburning co-injection of urea/CMA did not result in increases in NO, even at the leaner reburn zone stoichiometries; improvement in NO reduction over basic reburning was always observed (Figure 46). With ammonia/methane the NHi active NO reductant is exposed to the O2 entering the reburn zone from the primary zone and a greater proportion will be oxidized to NO than would be the case with urea, particularly in aqueous solution.

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There is a delay in N release imposed by the time it takes for droplets to evaporate and urea to decompose, which may result in significant penetration into the reburn zone where CMA combustion has already reduced the concentration of oxygen. The overall effect is of staged addition of reburn fuel and urea.

λ1=1.4

80 60 40 20

NSR = 0 NSR = 1.0 NSR = 1.5 NSR = 2.5

0

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NO reduction, %

100

λ1=1.15

80 60 40 NSR = 0 NSR = 1.0 NSR = 1.5 NSR = 2.5

20 0

100

λ1=1.05

80 60 40 NSR = 0 NSR = 1.0 NSR = 1.5 NSR = 2.5

20 0 0

2

4

6

8

10 12 14 16 18 20

Rff, % Figure 45. Advanced reburn. Influence of urea SNCR on the enhancement of NO reduction over basic reburn with CMA.

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Bill Nimmo and Hao Liu λ1=1.4

30 25 20 15 10 NSR = 1.0 NSR = 1.5 NSR = 2.5

improvement (AR over BR), %

5 0 30

λ1=1.15

25 20 15 10 NSR = 1 NSR = 1.5 NSR = 2.5

5 0 30

λ1=1.05

25 20 15

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10 NSR = 1 NSR = 1.5 NSR = 2.5

5 0

8

10

12

14

16

18

Rff, % Figure 46. Advanced reburn. Improvement in NO reduction due to urea SNCR over basic CMA reburning.

Another important factor in determining the optimum conditions of operation is the efficiency of utilization of the nitrogen (ηN) derived from the SNCR agent (urea) and is calculated from the difference between NO emissions of basic and advanced reburn as follows:-

ηN =

(NOBR − NO AR ) ×100 (NOBR × NSR )

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(E14 )

155

Developments in Nox Emission Control by ‘Reburning’ …

where, NOBR is NO emission after basic reburning, NOAR is NO emission after advanced reburning with urea and NSR is the N/NO stoichiometric ratio. Rff=9.5 (λ2=1.27)

λ1=1.4

30

Rff=13.6% (λ2=1.21) Rff=17.3% (λ2=1.16)

25 20 15 10 5

NH2 utilisation, %

30

Rff=9.5 (λ2=1.04)

λ1=1.15

Rff=13.6% (λ2=0.99) Rff=17.3% (λ2=0.95)

25 20 15 10 5 30

λ1=1.05

25 Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

20 15 Rff=9.5 (λ2=0.95)

10

Rff=13.6% (λ2=0.91) Rff=17.3% (λ2=0.87)

5 0.5

1.0

1.5

2.0

2.5

3.0

NSR Figure 47. Advanced reburn. The effect of NSR on ammonia utilization efficiency for primary zone stoichiometry λ1=1.05,1.15 and 1.4.

Influencing factors are oxygen concentration, mixing and temperature. Figure 47 shows the effect of NSR and Rff on N utilization for different conditions of primary zone stoichiometry (λ1). Maximum N utilization of about 30% was achieved at λ1=1.05, NSR = 1.5 and Rff = 10% however, reasonable N utilization of 25% was achieved at NSR = 2.5, where greater NO reductions were obtained. Under reburn zone conditions hydrocarbon fragments from CMA decomposition and NH3 from urea decomposition are in competition for available oxygen before depletion, at λ2 < 0, to form active CHi and NHi species respectively for NO reduction reactions. If the conditions are too fuel-lean then rates of destruction of the active

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Bill Nimmo and Hao Liu

species will be greater than NO reduction rates (full oxidation). On the other hand if the conditions in the reburn zone are too fuel-rich then insufficient generation of active species will occur, leading to NHi and hydrocarbon fragments being oxidized in the burnout zone. 500 λ1=1.4 λ1=1.05

NO, ppm

400

300

200

100

0 100

NO reduction, %

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90 80 70 60 50 λ1=1.4 λ1=1.05

40 920

940

960

980 1000 1020 1040 1060 1080 o

Temperature, C Figure 48. SNCR. Effect of temperature on NO reduction for different primary zone stoichiometries. NSR = 1.5.

Temperature has an influence on the rate of the principal NO reducing reactions with a sensitivity that is dependent on stoichiometry. NO reductions for urea SNCR on coal combustion with NO reburning are presented in Figure 48 for primary zone stoichiometries of 1.05 and 1.4, plotted against injection temperature. The data represent substantial portions of the upper zone of the temperature window for the reaction of NH2 with NO and indicates that peak reductions occur at about 960 oC. Although the stoichiometry in both cases is fuel-lean, there is clearly less sensitivity to temperature at λ1 = 1.05 with a drop of 10% in reduction

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efficiency, whereas at λ1 = 1.4 a 30% drop was observed when the temperature was increased from 950 to 1080oC. This sensitivity is likely to be reduced even more at fuel-rich reburn zone stoichiometries with further broadening of the temperature window. 1500 1400 1300 1200

o

Temperature, C

1100 1000 900 800 700

CMA/Urea injection

burnout air

λ1=1.05, Rff=9.5% λ1=1.05, Rff=17.3%

600

λ1=1.15, Rff=9.5%

500

λ1=1.15, Rff=17.3%

400

λ1=1.4, Rff=17.3%

300 1000

λ1=1.4, Rff=9.5%

1500

2000

2500

3000

3500

4000

Distance from burner, mm

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Figure 49. Temperature profiles prevailing during advanced reburn experiments for primary zone stoichiometries of λ1=1.05,1.15 and 1.4. Reburn fuel inputs of 9.5% (closed symbols) and 17.3% (open symbols).

CMA/urea, advanced reburning temperature profiles are presented in Figure 49 for three different primary zone conditions and two CMA input feed rates. Injection temperatures ranged from 1300oC at λ1=1.05 to 1150oC at λ1=1.4 with temperature drops in the reburn zone in the region of 150oC in all cases. These injection temperatures are at the top end of the SNCR temperature window for NO reduction (above about 1100oC). The SNCR data in Figure 48 serves to show the sensitivity of NO reduction to temperature with fuel-lean and fuel-rich reburn zones. The lean SNCR condition (λ1=1.4) is more sensitive to temperature than the richer condition (λ1=1.05) and in the advanced reburn experiments will benefit from lower CMA injection temperatures. The broadening of the temperature window by operating with a fuel-rich reburn zone means that urea can still be effective in advanced reburning even though the reburn zone temperature appears to be above the optimum for SNCR reduction of NO.

4.4.3. Optimization of NO and SO2 Reductions The previous discussion has been centered on optimization of NO reduction. The feed rate of CMA also has an effect on SO2 reduction (Figure 50); up to 85% at Ca/S of 2.5, λ1=1.15. However, the efficiency of the process decreases as the feed rate is increased, as shown in Figure 51, with optimum Ca utilization of 35-44% at ratios of 0.5 to 1.5. This is

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Bill Nimmo and Hao Liu

slightly lower than is normally reported as the generally accepted operating level of Ca/S of 2 and may be due partly to SO2 capture by MgO and partly due to the good mixing of the CaO in the reactor. The open structure of the sorbent particles also exhibits improved capture performance [120]. The Mg/Ca ratio was obtained from analysis of the precursor CMA solution by XRF and the sorbent particles by XRD to be 2.3:1, therefore, to adjust Ca/S to Ca:Mg/S multiply by 3.3. There is an influence of primary zone stoichiometry, which may be related to the oxidation of the coal sulphur and the possibility, at λ1=1.05, of H2S and SO2 coexisting in the furnace, at least up to the introduction of burnout air. Ca will react with H2S to form CaS, which may then oxidize to CaSO4 in the burnout zone. It is possible, but unlikely, that undetected S in the form of H2S will slip through the furnace due to oxidizing conditions in the burnout zone. The differences between SO2 emissions at high and low CMA inputs are consistent across the range of Ca/S levels at about 150ppm. Calcium utilization efficiency diminishes as Ca/S ratios increase (Figure 51).

Equivalent Rff, %

90

λ 1=1.15

1400

SO2, ppm

100 λ 1=1.05

1600

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20

15

10

λ 1=1.4

80 70

1200

60

1000

50 800 40 600

30

400

20

SO2 reduction, %

5

10

200

0 0 0.0

0.5

1.0

1.5

2.0

2.5

Ca/S ratio Figure 50. SO2 reductions for a range of CMA feed rates. Equivalent Rff shown for cross-reference with figures 43, 45 and 46.

Sulphurmapping by EDX analysis on ash samples at the furnace conditions described here, have shown that in addition to relatively high concentrations of S in the sulphated Ca/Mg particles, there were faint signs of S on the coal sourced ash particles indicating that S release from the coal particles may not be complete. A possible source is the inorganic pyritic form of S which may not be released in the primary combustion zone under low oxygen conditions (λ1=1.05) and persist into the more fuel-rich reburn zone. On emergence into the burnout zone at lower temperatures and short residence times, the ash-bound S may remain in place and be emitted. It is also possible that some of the ash-bound S may be in the form of CaSO4 derived from the inherent Ca content of the coal.

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Developments in Nox Emission Control by ‘Reburning’ … 50

Calcium utilisation, %

45 40 35 30 25 20 15

λ1=1.05 λ1=1.15 λ1=1.4

10 0.5

1.0

1.5

2.0

2.5

Ca/S

.

Figure 51. Influence of Ca/S ratio on calcium utilization for different primary zone conditions.

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Table 7 Optimum advanced reburning process conditions for either NO, SO2 reduction or Ca, N utilization.

NO emission, ppm SO2 emission, ppm Rff, % λ1 λ2 NSR Ca/S NO reduction (BR) % NO reduction (AR) % SO2 reduction, % Ca utilization, % N utilization, % Overall score, %

Mode A Optimum reduction 123 356 20.7 1.05 0.84 2.5 2.5 78 85 75 31 25 59

Mode B NO Optimum reduction 172 242 20.7 1.15 0.91 2.5 2.5 75 79 85 34 25 60

Mode C SO2 Optimum utilization 223 942 9.5 1.05 0.95 1.5 1.0 53 73 35 40 30 46

Table 7 summarises the process conditions that will give:-

• • • •

Mode A: optimum NO reduction Mode B: optimized SO2 reduction Mode C: optimized N utilization efficiency Mode D: optimized Ca utilisation efficiency

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Mode D N- Optimum utilisation 322 1144 5 1.15 1.09 2.5 0.5 20 61 22 44 20 33

Ca-

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Maximizing NO and SO2 reductions (Modes A and B, respectively) leads to low reductant utilization efficiencies and high rates of consumption of CMA. Optimizing utilization efficiencies (Modes C and D) gave poor SO2 reductions, as discussed earlier. NO control was best performed at λ1=1.05 but SO2 reductions were greater at more fuel lean primary zone conditions (λ1=1.15). Highest NO reductions of 85% under AR-rich conditions were achieved under Mode A, but were only slightly higher compared with reductions of 79% under Mode B where SO2 reductions were optimized at 85%. The lower NO reductions in this mode of operation are due to the higher concentration of O2 available in the primary and reburn zones which lead to lower reburn efficiencies due to CHi consumption. N-utilization was also at an acceptable level of 25% compared to the maximum utilization efficiency which was obtained at NSR = 1.5 of 30% for the same conditions of stoichiometry operating in Mode C. Operation at this lower level of reburn (9.6%) could significantly reduce the consumption of CMA with some impact on NO reduction (73%). SO2 removal performance would be compromised severely with reductions lowered from 75% at Mode A to 35% at Mode C. Optimizing Ca utilisation (Mode D) resulted in poor NO and SO2 reductions, at 61% and 22% respectively and can be discounted as a viable option. By calculating an average score from the reductions and utilisations obtained under each mode of operation, it was found that Modes A and B performed equally with 59% and 60%, respectively, however, operational parameters could be tailored to suit optimum SO2 or optimum NO reduction. The technique offers flexibility of operation depending on the emission control requirements of a particular plant and fuel. Thus, the simultaneous reduction of NOx and SOx has a considerable engineering appeal [166] due to the potential simplicity of the injection system required, particularly if wet-spray methods are adopted where co-injection of CMA solution with SNCR agent (e.g. urea or ammonia solution) is possible. It has been reported [167] that the addition of oxygenated compounds can broaden the effective temperature ‘window’ for NOX reduction by SNCR mainly at the lower temperature end. For example, at temperatures below 900oC, methyl acetate, has been reported to broaden the window by around 75oC. The temperature window for SNCR using urea usually starts at about 900oC where little reaction takes place with maximum reductions usually occurring in the region of 1000oC. Beyond this temperature there appeared to be little effect of adding oxygenated products. It is possible therefore that the use of a combination of CMA and urea may have a hitherto unforeseen benefit in broadening the effective temperature window during advanced reburning, particularly at low reburn fuel inputs (60%) content, has significant advantage over coal or other solid fuels, and much lower reburn fuel fractions are required to obtain good NOx reductions but may encounter the same barrier to entry into the power plant market as waste. The adoption of a reburning process using tyre rubber in the UK would almost certainly require reclassification of the material from waste to fuel but it would provide a means of utilizing tyre rubber reducing NOx and gaining valuable energy. Tyre rubber will not be adopted in the UK, even for co-firing, due to its classification as waste. CMA has shown multi-control capabilities but a detailed cost-benefit analysis will be necessary to show that the cost of production from the raw materials (dolomite and acetic acid) can be outweighed by the technical benefits and other capital savings. CMA combined with urea injection in an Advanced Reburning configuration could eliminate the need for flue gas desulphurization units and other more expensive forms of NOx control. Future applications of reburning/advanced reburning in coal-fired power plants depend on many factors such as the NOx emission legislations, the availability and economics of reburn fuel (natural gas, biomass etc.) on site, the development of alternative NOx control measures, acceptable levels of carbon in ash and potential interferrences/interactions with removals of other pollutants (trace metals, SOx etc.) and carbon capture and storage.

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[97] Fan, Z. L.; Zhang, J.; Sheng, C. D.; Lin, X. F.; Xu, Y. Q. Energy and Fuels 2006, 20, 579-582. [98] Duan, J.; Luo, Y. H.; Yan, N. Q.; Chen, Y. Energy and Fuels 2007, 21, 2007, 15111516. [99] Dagaut, P.; Lecomte, F. Energy and Fuels 2003, 17, 608-613. [100] Guarneri, F.; Ikeda, E.; Mackie, J. C. Energy and Fuels 2001, 15, 743-750. [101] Carlin, N. T.; Annamalaia, K.; Harman, W. L.; Sweeten, J. M. Biomass and Bioenergy 2009, 33, 1139 – 1157. [102] Maly, P. M.; Zamansky, V. M.; Ho, L.; Payne, R. Fuel 1999, 78, 327 – 334. [103] Xu, H.; Smoot, L. D.; Hill, S. C. Energy and Fuels 1998, 12, 1278-1289. [104] Xu, H.; Smoot, L. D.; Hill, S. C. Energy and Fuels 1999, 13, 411-420. [105] Xu, H.; Smoot, L. D.; Tree, D. R.; Hill, S. C. Energy and Fuels 2001, 15, 541-551. [106] Vissianski, V. V.; Zamansky, V. M.; Maly, P. M.; Sheldon, M. S. Proc. Combust. Inst. 2000, 28, 2475–2481. [107] Tree, D. R.; Clark, A. W. Fuel 2000, 79, 1687 – 1695. [108] Nimmo, W.; Patsias, A.A.; Hampartsoumian, E.; Gibbs, B.M.; Fairweather, M.; Williams, P.T. Fuel 2004, 83(9), 1143-1150. [109] Nimmo, W.; Javed M.T.; Gibbs, B.M., J. Energy Inst. 2008, 81(3), 131-134. [110] Hampartsoumian, E.; Folayan, O.O.; Nimmo W.; B.M. Gibbs Fuel 2003, 82(4), 373385. [111] Mereb, J. B.; Wendt J. O. L. Fuel 1994, 73, 1020-1026. [112] Song, Y. H.; Beer J. M.; Sarofim A. F. Combust. Sci. and Tech. 1981, 25, 237-240. [113] Chan, L. K.; Sarofim, A. F.; Beer J. M. Combust. Flame 1983, 52, 37-45. [114] Suuberg, E. M.; Teng, H.; Calo, J. M. Proc. Combust. Inst. 1990, 23, 1199-1205. [115] Smart, J.P.; Morgan, D.J. Fuel 1994, 73(9), 1437-1442. [116] Naja T.A., PhD thesis “Coal as a reburn fuel for NOx reduction”, The University of Leeds, 1997. [117] Alzueta, M. U., Glarborg, P.; Dam-Johansen, K. Combust. Flame 1997,109(1-2), 2536. [118] Niksa, S.; Cho S. Energy and Fuels 1996, 10, 463-473. [119] Kilpinen, P.; Glarborg P.; Hupa M. Ind. Eng. Chem. Res. 1992, 31, 1477-1490. [120] Glarborg, P.; Miller, J, A.; Kee, R. J. Combust. Flame 1986, 65(2),177-202. [121] Levendis, Y.A.; Zhu, W.; Wise, D.L.; Simons, G.A. AIChE Journal 1993, 39(5), 761773. [122] Steciak, J.; Levendis, Y.A.; Wise, D.L. Environ. Energy Eng. 1995, 41(3), 717-722. [123] Nimmo, W.; Patsias, A.A.; Hampartsoumian, E.; Gibbs, B.M.; Williams, P.T. Fuel 2004, 83(2),149-155. [124] Maly, P. M.; Zamansky, Proceedings of the International Technical Conference on Coal Utilization and Fuel Systems 1997, 22, 815-826. [125] Chen S.L.; Cole J.A.; Heap M.P.; Kramlich J.C.; McCarthy J.M.; Pershing D.W. Proc. Combust. Inst. 1988, 22, 1135-1143. [126] Muzio, L.J.; Arand, J.K.; Maloney, K.L. Proc. Combust. Inst. 1978, 17, 89-96. [127] Zamansky, V.M.; Ho L.; Maly, P.M.; Seeker W.R. Proc. Combust. Inst. 1996, 26, 2075. [128] Folsom, B. A.; Sommer, T. M.; Engelhardt, D. A.; Moyeda, D. K. 1997 Fifth Annual Clean Coal Technology Conference Tampa, Florida January 1997, 7-10.

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[154] Chen, W.; Smoot, L. D.; Hill, S. C.; Fletcher T. H. Energy and Fuels 1996, 10, 10461052. [155] De Soete, G. C. Proc. Combust. Inst. 1988, 15, 1093-1102. [156] Ryan, B. F.; Joiner B. L.; Ryan Jr. T. A. ‘Minitab Handbook, Second Edition’, PWS Publishers, 1985. [157] Thorne, L.R.; Branch, M.C.; Chandler, D.W.; Kee, R.J.; Miller, J.A. Proc. Combust. Inst. 1986, 21, 965-977. [158] Ucar, S.; Karagoz, S.; Ozkan, A. R.; Yanik, J. Fuel 2005, 84, 1884-1892. [159] Rodriquez, I.D.; Laresgoiti, M. F.; Cabrero, M. A. Fuel Process. Tech. 2001, 72(1), 922. [160] Gonzalez, J. F.; Encinar, J. M.; Canito J.L.; Rodreguez, J. J. J. Anal Appl Pyrolysis 2001, 58-59, 667-683. [161] Williams, P. T., and Brindle, A. J. J. Anal. Appl. Pyr. 2003, 67(1), 143-164. [162] Conesa, J. A.; Fullana, A.; Font, R. Energy and Fuels 2000, 14(2), 409-418. [163] Casaca, C.; Costa, M. Combust. Sci. Tech. 2005;177 (93): 539-557. [164] Bilbao, R.; Millera, A.; Alzueta, M. U.; Prada L. Fuel 1997, 76(14-15), 1401-1405. [165] Folsom, B.A.; Payne, R.; Moyeda, D.; Zamansky V.; Golden, J. EPRI/EPA Joint Symposium on Stationary Combustion NOx control, Kansas City MO, May 1995. [166] Han, X.; Wei, X.; Schnell, U.; Hein, K.R.G. Combust. Flame 2003, 132, 374-386. [167] Gullett, B.K.; Bruce; K.R., Hansen, W.F.; Hofmann, J. E. Environ. Prog. 1992, 11(2), 155-162. [168] Rota, R.; Zaneolo, E.F. Fuel 2003, 82, 765-770.

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Chapter 5

MINERAL TRANSFORMATION AND ASH DEPOSIT DURING COAL COMBUSTION Yongchun Zhao, Junying Zhang* and Chuguang Zheng

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State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, 430074 China Inorganic components and minerals in coals were recognized as being the main source of ash deposit. In this paper, the mineral transformation during coal combustion was summarized. Five coal samples were collected from typical coal mines and power plants in China to investigate mineral transformation during coal combustion. The thermal-gravity curves of the low temperature ashes of coals were analyzed to study the thermal behavior of minerals. The results indicate that boehmite, kaolinite, and rutile are refractory minerals. Most of the iron-bearing mineral transformed into iron oxide. Ca-Mg silicate was detected in the ash when it closes to be melt. To understand the physico-chemical characteristics of ash deposit, six samples of ash deposits were collected from a typical coal-fired power plant (Zhuzhou Power Plant) in Hunan, China. X-ray diffraction (XRD), field emission scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy (FSEM-EDX), optical microscopy, and X-ray fluorescence (XRF) were used to analyze mineralogy, chemical composition, and microstructure of ash deposits, which would provide valuable information for the elucidation of ash deposit formation mechanism. The results show that, the ash deposit is mainly composed of silica amorphous phases, the chemical compositions of different layers vary from each other. The identified minerals include mullite, cristobalite, hematite, quartz, hercynite, and anorthite. The silica-rich glass phases are derived from volatilization-recondensation of SiO and the interaction between aluminosilicates and other minerals. Several types of crystals were identified in the ash deposits, including iron-oxides crystals, Fe-Ca-bearing phases, Si-riched phases, and aluminosilicate phases. The deposition of particles as well as the following melting is the main reason for the porosity structure of deposit. The interaction and eutectic of minerals in coals during combustion result in the serious deposition in the coal-fired power plant. *

Correspoinding author: Email: [email protected].

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1. INTRODUCTION Coal is one of the most important energy resources powering the built environment in which we live [1]. Coal-fired power plants generate over 50% of the electricity in the U.S. and higher percentages of electricity in China; it will be the major source of electricity in China for decades [2-3]. More than 80% of coal-fired boilers are affected by ash deposit. Ash deposits formed during fuel thermal conversion and located on furnace walls and convective pass tubes, may seriously inhibit the transfer of heat to the working fluid and hence reduce the overall process efficiency [4-8]. In severe cases, the ash deposits cause water tube wall crack and therefore subsequently unscheduled boiler shutdowns [5]. The problem is serious in China due to the varying of coal quality. And the total economic loss from boiler deposits is up to several billion yuan per year in China [9]. Mineral impurities in coal are known to be primary contributors to the slagging and fouling of boilers [10-15]; their transformation processes and products have significant effects on ash deposition [9, 16-20]. Simple empirical relationships between fusion temperature of the ashes and the mineralogy or other components of coals were proposed [11, 21], but some researchers questioned these approaches. Despite a long history of investigation promoted by the observations, many questions remain unanswered [5]. Deposits are complex and heterogeneous materials. The formation process of ash deposit mainly includes diffusion, thermophoresis, and inertial impaction [7]. Inorganic components and minerals in coals were recognized to be the main source of deposits [22]; their composition, occurrence, distribution, and thermal behavior have significant influence on the formation of deposits [18, 23-24]. Especially problematic are iron-bearing minerals and calcium-bearing minerals, so the coal rich in iron-bearing minerals or calcium-bearing minerals can easily form ash deposits [9, 16, 25-28]. In addition, the boiler types, burner layout, as well as combustion conditions also have significant influence on ash deposition [7, 29]. Ash deposition occurs as slagging and fouling; slagging deposits occur in the hightemperature radiant sections of the boilers and are usually associated with some degree of melting of the ashes [27]. The slagging deposits show different characteristics, depending on their location in the furnaces, because of differences in temperature and composition of the particulate matter. Slags, which come from the hottest zone in the furnace, consist of a framework of anhydrite (CaSO4) crystals with minor amount of Si-Al-Fe glasses [27]; while the slags in the superheater tube banks at the furnace exit are composed of friable aggregates of particulate matter, with a composition of calcite and anorthite. The difference in chemical compositions and mineral phases play a key role in determining the viscosity of slags [30-31]. Thermal conductivity and emissivity, the two properties with the greatest impact on heat transfer, also demonstrate strong and complex dependencies on deposit structure [32]. Knowledge of physicochemical characteristics of ash deposits is of great help in clarifying formation mechanisms of ash deposit and reducing slagging. The varied feed coal qualities resulted in the serious ash deposition in China coal-fired power plants; however, the references about the deposition characteristics in the coal-fired power plant in China are scarce. In this work, five coal samples and six samples of ash deposits were collected from several typical coal mines and a typical coal-fired power plant (Zhuzhou Power Plant) in

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Hunan, China, respectively. X-ray diffraction (XRD), field emission scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy (FSEM-EDX), optical microscopy, and X-ray fluorescence (XRF) were used to investigate mineralogy, mineral transformation process, chemical composition, and microstructure of ash deposits in detail.

2. MINERAL TRANSFORMATION DURING COAL COMBUSTION 2.1. Minerals in Low Temperature Ashes Five coal samples (ZGR, PX, LPS, XLT, SF) were collected from typical coal mines and power plants to investigate mineral transformation during coal combustion. The proximate and ultimate analysis results are listed in Table 1. Table 1. Proximate and ultimate analysis of feed coal Coal samples

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ZGR bituminous PX bituminous LPS anthracite XLT lignite SF bituminous

Proximate analysis (ad) M V A FC 7.22 35.15 20.19 35.47 0.35 29.2 33.0 37.45 1.09 21.90 25.73 51.28 14.60 46.91 13.74 24.74 10.19 37.66 6.50 62.34

Figure 1. X-ray diffraction pattern of ZGR coal.

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C 55.26 79.26 62.91 49.21 80.53

Ultimate analysis (ad) H O N 3.31 10.75 1.08 4.05 1.22 1.64 4. 06 1.19 1.50 5.40 1.42 2.47 4.80 0.89 0.37

S 0.41 3.09 2.93 13.16 13.41

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Figure 2. X-ray diffraction pattern of PX coal.

Figure 3. X-ray diffraction pattern of LPS coal.

Figure 4. X-ray diffraction pattern of XLT coal.

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Figure 5. X-ray diffraction pattern of SF coal.

Five coal samples were pulverized and ashing using K1050 low temperature asher. XRD was used to determine the mineral composition, and Reltvied method was used to calculate the semi-quantity content of minerals. The XRD spectrometry was shown in Figure 1-5. The mineral compositions were listed in Table 2. As shown in Table 2, minerals in ZGR coal include boehmite, kaolinite, quartz and calcite; PX coal includes quartz, kaolinite, pyrite, calcite, rutile, and FeSO4; LPS coal contains quartz, kaolinite, pyrite, dolomite, nacrite, and muscorvite; XLT coal includes quartz, kaolinite, pyrite, dolomite, illite, calcite, anatase, and bassanite; while SF coal contains kaolinite, quartz, bassinite, pyrite, and magnesian calcite. ZGR coal is a typical aluminum enriched coal with relative high content of boehmite. XLT and SF coals are two calcium-enriched coals. Clay minerals may constitute up to 60 weight percent of the mineral matter, quartz is usually the second most abundant mineral, up to 20% being common [11]. Kaolinite and quartz almost found in all five coal samples. Significant amount of illite was also identified in XLT lignite. The carbonate minerals (calcite, siderite and to a lesser extent, dolomite and ankerite) and the iron disulphide minerals (pyrite and marcasite) comprise, on the average, up to 10% of each group. Pyrite was found in all coal samples except ZGR coal. Carbonate minerals, calcite and dolomite were also the important components in the samples. Sulphate and feldspar minerals are commonly present but rarely in concentrations of more than a few weight percent [11]. Only PX coal contains few percent of FeSO4. Bassanite only occurs in the high calcium coal samples. The occurrence of other minerals in concentrations exceeding a few percent is rare.

2.2. Minerals in High Temperature Ash As coal is heated, the inorganic minerals undergo transformations and reactions that yield a complex mixture of solid, molten, and volatile species. To understand the influence of mineral transformation on ash deposition, the mineral compositions of high temperature ashes (HTAs) were investigated in details. First of all, five grams of coal samples were ashed in a

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muffle furnace under 815 . The production rates of HTAs of four typical coals were listed in Table 3. The HTA production rate ranges from 19.01% to 58.99%. Table 2. Mineral compositions of LTAs Coal samples ZGR bituminous PX bituminous LPS anthracite XLT lignite SF bituminous

Minerals quartz, kaolinite, boehmite, calcite quartz, kaolinite, pyrite, calcite, rutile, FeSO4 quartz, kaolinite, pyrite, dolomite, nacrite, muscorvite quartz, kaolinite, pyrite, dolomite, illite, calcite, anatase, bassanite quartz, kaolinite, pyrite, bassinite,magnesian calcite

Table 3. HTA percent of different coals Coal samples

Coal mass (g)

HTA mass(g)

ZGR bituminous

4.75

2.802

PX bituminous LPS anthracite XLT lignite

4.4 4.03 4.156

1.837 1.7 0.79

Ash colour White coarse grain henna red dark

HTA percent 58.99% 41.75% 42.18% 19.01%

Table 4. Mineral compositions of HTAs at different temperatures

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Coal ZGR bituminous PX bituminous

Temperature ( ) 1050 1200 1270 1050 1200 1270

LPS anthracite XLT lignite

1050 1200 1270 1050 1200

SF bituminous

1050

Minerals mullite, alumina, rutile, quartz, anorthite mullite, alumina, quartz, cristobalite mullite, alumina, rutile, essonite, plagioclase quartz, mullite, alumina, hematite, anorthite, rutile, quartz, mullite, cristobalite, magnetite, hematite, anorthite quartz, cristobalite, mullite, hematite, magnetite, shannonite quartz, mullite, alumina, hematite, dolomite quartz, mullite, alumina, dolomite, hematite, quartz, mullite, andradite, essonite quartz, hematite, gehlenite, calcium slfate, calcium silicate quartz, hematite, calcium slfate, gehlenite, essonite, fayalite quartz, hematite, dolomite, gehlenite, wollastonite, calcium slfate,

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To understand the detail mineral transformation process, five coal samples were ashed under three different temperatures (1050 , 1200 , and 1270 ), and X-ray diffraction was used to determine the mineral composition of HTAs, the result was shown in Table 4. The SF ash was soft and melting under 1200 . The XLT ash was melted and adhered on the surface of porcelain boat after calcinations under 1270 . Minerals in HTAs has significant influence on ash fusion temperature, the related minerals which have been identified in HTAs were summarized and listed in Table 5. The relation of minerals in HTAs between ash deposit behaviors will be discussing in the next part.

2.3. Thermogravimetric Analysis The thermogravimetric curves of the low temperature ashes of coals were analyzed to study the thermal behavior of minerals (Tables 6-10).

2.3.1. ZGE coal ZGR coal is a typical high alumina coal, which contain about 15 wt.% of boehmite in coal, the TG-DTG-DSC curves were shown in Figure 6, the endothermic peaks and weight loss peaks were identified and listed in Table 6. It has an obviously weight lost peak between 400~600 , the peak is located at about 500 , which maybe derived from the dehydration of crystal water of kaolinite and boehmite.

⎯→ Al2O3 ⋅ 2SiO2 + 2 H 2O Kaolinite: Al2 Si 2 O5 (OH ) 4 ⎯⎯ 723 K

⎯→ γ − Al2O3 + H 2O Boehmite: Al2O3 ⋅ H 2O ⎯⎯ Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

773 K

Kaolinite is a common clay mineral in coal, the evolution of kaolinite with thermal treatment has been described by Querol et al. [33]. Kaolinite losses the combined water from the hydroxyl groups surrounding the aluminum atoms, yielding in the formation of an amorphous phase, metakaolin at about 400 , after that, a fast reorganization of oxide ions in the lattice structure develops into a spinel-like form of γ-Al2O3, and further formation of mullite (3Al2O3·2SiO2) [34]. From the DSC curves in Figure 6, there is an obvious exothermal peak at about 1000 , this is the feature peak of spinel formation. At about 1300 , there is also an exothermal trend which implies that the crystallization process of mullite. Some silicate minerals like essonite and plagioclase are formed by the combination of SiO2 and other minerals at high temperature.

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Yongchun Zhao, Junying Zhang and Chuguang Zheng Table 5. Minerals related to slag in HTAs

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Mienral

Chemical formulas

Melting point ( ) 1810* 1610 1680 1730 2050 1827 1050* 1200

Mullite Quartz Cristobalite Tridymite Alumina Rutile Muscovite Chlorite Plagioclase K-feldspar

Al6Si2O13 SiO2 SiO2 SiO2 Al2O3 TiO2 KAl3Si3O10(OH)2 (MgFe)5Al2Si3O10(OH)8 NaAlSi3O8-CaAl2Si2O8 KAlSi3O8

Albite Pyrrhotite Hematite Magnetite Ferrous oxide Hercynite Fayalite Fe-cordierite Almandine Andradite

Na AlSi3O8 FeS Fe2O3 Fe3O4 FeO FeAl2O4 Fe2SiO4 2 FeO2Al2O35SiO2 Fe3 Al2(SiO4)3 Ca2Fe3(SiO4)3

Essonite Ca-Fe-olivine Anorthite Gehlinite Shannonite Forsterite Wollastonite Okenite Larnite Montmorillonite

Ca3 Al2(SiO4)3 CaFeSiO4 CaAl2Si2O8 Ca2Al2SiO7 . CaMgSiO4 MgSi2O4 CaSiO3 Ca3Si2O7 2CaOSiO2 (NaCa)0.3(AlMgFe)2Si4O

Diopside Hedenbergite Calcium sulfate

CaMgSi2O6/CaFeSi2O6 Na3(Na,K)(Al4Si4O16) CaSO4

1200 11701200 1180 1100 1553 1590 1490 1890 1544 1464* 2130 13301430 1280 950* 1725

Sodium sulfate Ferrous sulfate

Na2SO4 FeSO4

1157 500

*decomposition temperature

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1170* 1120* 1193 1565 1594 1429 1780 1205

Density (g/km3) 2.69 2.65

3.9-4.0 4.2-4.3 2.75-3.0 2.6-3.3 2.6-2.76 2.562.59 2.60 4.62 5.2 5.14 5.7 3.55 4.39 4.312 3.86 3.59 2.76 3.03 3.2 3.2 2.86-2.9

2.4-2.6 3.29 3.19 2.752.85 2.68 1.8-1.9

Mineral Transformation and Ash Deposit during Coal Combustion

179

Figure 6. TG-DSC curves of ZGR coal.

Table 6. Endothermic peak and weight loss peak of ZGR TG-DSC curves

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Endothermic peak

weight loss peak

Initial temperature

Peak temperature

1

100

520

2

800

1025

3

1250

1280

1

170

215

2

240

520

Phase transition Dehydration of kaolinite, boehmite, decomposition of calcite Transition of quartz, boehmite Formation of anorthite, cristobalite transition Dehydration Dehydration of kaolinite, boehmite, decomposition of calcite

2.3.2. PX coal PX coal is a typical high silica coal, it contains 61.5% SiO2 in ash. The mineral composition of PX coal includes quartz, kaolinite, pyrite, calcite, rutile, and FeSO4. The mineral composition in HTA is shown in Table 4. With increasing the temperature, part of quartz transformed to cristobalite. Kaolinite dehydrates and decomposes to form mullite and alumina under 1200 . Anorthite was identified in HTA of 1050 . When temperature was up to 1200 , iron-bearing minerals occur as hematite and magnetite. Shannonite was formed after temperature raised to 1270 . TG-DTG-DSC curves were shown in Figure 6, the endothermic peaks and weight loss peaks were identified and listed in Table 7. The phase transformation of quartz was occurred between 1050 to 1200 , partly of quartz transformed to cristobalite. Kaolinite was dehrodration and decomposition to form mullite, little alumina was found in HTA of 1050 .

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The mainly product of pyrite during PX coal combustion was hematite and magnetite, magnetite was more stable than hematite. The finally stable product of FeSO4 is also magnetite. The decomposition product of calcite often combined with SiO2 to form anorthite at high temperature. With temperature continuous increasing, other silicate minerals like shannonite can be formed. The melting point of shannonite is 1490 , it was identified in HTA of 1270 .

Fe 2 SO4 (OH)2 ⋅ 5H 2O → FeSO4 → FeO+SO2 → Fe2O3 → Fe3O4

Figure 7. TG-DSC curves of PX coal.

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Table 7. Endothermic peak and weight loss peak of PX TG-DSC curves.

Endothermic peak

weight loss peak

1 2

Initial temperature 430 560

Peak temperature 470 580

3

780

1250

1

90

200

2

375

560

3 4

750 990

800 1060

Phase transition Dehydration of kaolinite Transition of quartz Decomposition of calcite, quartz transition, formation of anorthite Dehydration Dehydration of kaolinite, decomposition of pyrite Decomposition of calcite Oxidation of pyrrhotite

2.3.3. LPS coal The LPS coal is typical high Fe-Ca coal (with 12.4% Fe2O3 and 7.5% CaO in ash). TGDTG-DSC curves of LPS coal were shown in Figure 8, the endothermic peaks and weight loss peaks were identified and listed in Table 8. The XRD analysis shows that the minerals in

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HTA are mainly composed of quartz, mullite, alumina, hematite, and dolomite. Andradite and essonite were only identified in HTA of 1270 . The transformation process of kaolinite and pyrite are similar to the description above. The muscovite was decomposed to form mullite, quartz, alumina, and other minerals before 1050 . High content of Fe2O3 and CaO is the main reason of the formation of andradite and essonite. Andradite: Essonite:

CaO+Fe 2O3 +SiO 2 → Ca 2 Fe3Si3O12

3CaO+Al2 O3 +3SiO 2 → 3CaOAl2 O3 3SiO 2

There are two endothermic peaks in DSC curve of LPS LTA, the first peak has broad endothermic range, up to 970 , which maybe derived from the overlap of several processes. The decomposition temperature of dolomite is high in LPS coal, which is raise up to 950 .

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Dolomite:

MgCa (CO3 ) 2 → CaO + MgO + 2CO2

Figure 8. TG-DSC curves of LPS coal.

Table 8. Endothermic peak and weight loss peak of LPS TG-DSC curves Initial temperature Endothermic peak

weight loss peak

Peak temperature

1

600

970

2

1190

1205

1 2

100 420

195 560

3

730

765

4

950

1070

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Phase transition Dehydration of kaolinite, muscovite. Decomposition of dolomite and pyrite Transition of quartz, boehmite quartz transition, formation of Ca-Fe-aluminosilicate Dehydration Dehydration of kaolinite, Dehydroxylation of muscovite Decomposition of dolomite, oxidation of pyrrhotite

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Yongchun Zhao, Junying Zhang and Chuguang Zheng

2.3.4. XLT coal The XLT coal is a typical high calcium lignite. TG-DTG-DSC curves of XLT coal were shown in Figure 9, the endothermic peaks and weight loss peaks were identified and listed in Table 9. Clay minerals in XLT coal decomposed to form mullite, quartz, and these secondary minerals can react with calcium oxide and iron oxides to form gehlenite, calcium sulfate, calcium silicate, essonite, fayalite, and others. The reaction equations as follows:

CaO + SiO2 ⎯ ⎯→ CaO ⋅ SiO2 o

> 900 C CaO+Al2O3+SiO2 ⎯⎯⎯→ Ca2Al2SiO7

2CaO+ Al2O3SiO2→ Ca2Al2SiO7 950-1000

2 FeO + SiO2 → 2 FeO ⋅ SiO2 >1200 Illite often undergoes dehydration and decomposition to form mullite: 0

850 C 2( KAl2 (Si3 Al )O10 (OH )2 ) ⎯⎯⎯ → 2 H 2O + K

0

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900 C K2O ⋅ 3 Al2O3 ⋅ 6SiO2 ⎯⎯⎯ → 2 Al2O3 ⋅ 3SiO2

Figure 9. TG-DSC curves of XLT coal.

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Table 9. Endothermic peak and weight loss peak of ZGR TG-DSC curves Initial temperature Endothermic peak

weight loss peak

1

Peak temperature 460

2

595

630

3

1000

1150

4

1220

1325

1

90

120

2

250

440

3 4

595 700

630 730

5

900

1150

Phase transition Dehydration of kaolinite, gypsum Decomposition of calcite Decomposition of illite, CaSO4 Decomposition of CaSO4, phase transition, melting Dehydration of gypsum Dehydration of kaolinite, pyrite tranformation Decomposition of pyrite Decomposition of calcite Decomposition of CaSO4, pyrrhotite

Calcium oxide not only derives from the decomposition of calcite, but also from the decomposition of calcium sulfate. The decomposition temperature is about 1050 , as shown in Figure 9 and Table 9. o

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1000 −1200 C CaSO 4 ⎯⎯⎯⎯⎯ → CaO+SO 2

The weight loss curves indicate that the decomposition of pyrite can divided into two steps, the first is formation of pyrrhotite at low temperature, and then the oxidation step of pyrrhotite to form iron oxides. Hematite was found in HTA of 1050 .

2.3.5. SF coal The ash fusion temperature of SF coal is low to 1210 . The kaolinite, pyrite, and magnesium calcite are decomposition before 1050 . The combination of these oxides at high temperature results the formation of gehlenite, wollastonite, and calcium sulfate. The decomposition process of carbonates and oxidation process of pyrite occur at the same temperature range, which result the eutectic of calcium-iron-aluminasilicates. Calcium appears to be more effective flux than ferric iron in an oxidizing environment[29]. This is the main reason of the low melting temperature of SF coal. CaSO4+Al2O3+SiO2→Ca2Al2SiO7 or CaAl2Si2O8 +SO2 CaSO4+SiO2→CaSiO3 or Ca2SiO4+SO2

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Figure 10. TG-DSC curves of SF coal.

Table 10. Endothermic peak and weight loss peak of SF TG-DSC curves

Endothermic peak

Peak temperature 490

2

800

1070

3 1

1200 100

1310 130

2

370

525

3

680

815

4

980

1130

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weight loss peak

1

Initial temperature 405

Phase transition Dehydration of kaolinite Decomposition of kaolinite, gypsum, Mg-calcite Mineral melting Dehydration of gypsum Dehydration of kaolinite, pyrite tranformation Decomposition of Mg-calcite Decomposition of gypsum, oxidation of pyrrhotite

Figure 11. Furnace of the 1025 t/h down-fired boiler (dimensions in mm)[35].

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Mineral Transformation and Ash Deposit during Coal Combustion

3. ASH DEPOSITION DURING COAL COMBUSTION To understand the formation mechanism of ash deposition, several deposit samples were collected from the Zhuzhou power plant, the boiler (DG1025/18.2-II14) with a capacity of 1025 t/h was manufactured by Dongfang Boiler Group Company. It adopts a I-type layout, double arches and a single furnace. The schematic diagram of the furnace is shown in Figure 11. The structure of furnace and arrangement of burners has been described elsewhere in more details [35]. The feed coal is blend coal mixed with low-volatile anthracite, lean coal, and few bituminous. The proximate analysis and ash chemical composition of coal are listed in Table 11. ASTM tests were used to determine the ash fusion temperatures. Table 11. Proximate analysis and ash chemical composition of feed coal Ash fusion temperature ( ) DT ST FT 1098 1138 1202 SiO2 44.86

Al2O3 25.29

CaO 4.78

Proximate analysis, wt.% (as air-dried) M V A 1.2 14.3 39.3 Chemical composition (wt%) Fe2O3 SO3 K2O MgO TiO2 14.97 5.94 1.27 1.00 1.56

FC 45.2 MnO 0.33

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The different property of feed blend coal and the design coal results a serious ash deposition in the furnace. The deposit samples were collected from the front wall (sample 1, 3) and rear wall (sample 2, 4) in fuel-burning zone (sample 1, 2) and fuel-burnout zone (sample 3, 4), as well as the left side wall (sample 5) and the right side wall (sample 6), respectively. The sample collection positions are shown in Figure 12. 1.front wall F (FWF); 2.rear wall F (RWF); 3.front wall (FW); 4.rear wall (RW); 5.left side wall (LSW); 6.right side wall (RSW)

Figure 12. Samples collection points in the furnace.

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3.1. General Characteristics of Deposits

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As shown in Figure 13, all of the ash deposits are porous materials. The severity of deposition following the order: RWF> FWF> RW> RSW> FW> LSW. The RWF slag is completely melted with dense structure, the FWF slag is also melted but only melts on the surface, the inner layer of FWF slag is composed of loose ash particles. The RSW slag, which has a dark red colour, is partly melted. The FW slag is made of sintered products of ash particles. The slag LSW is grey white with loose structure enriched of ash particles. The deposits consist of several layers, which have different compositions, porosities, and microstructures. Along the growth direction, the deposits were divided into three layers: inner layer, middle layer, and outer layer. All of the slag samples were crushed and ring milled to a particle size less than 200 mesh for analysis.

Figure 13. Photos of ash deposits from different positions in the boiler.

3.2. Chemical Composition of Ash Deposits The chemical composition determines indirectly the structure of the deposit at a given temperature [36]. It also affects the emissivity due to the presence of colouring agents which increase the adsorptivity of the deposit, and together with the particle size distribution, will influence the temperature of the onset of sintering, and the course of fusion of the deposit [23, 36]. The chemical compositions of six deposits from different positions were analyzed by XRF, as shown in Table 12. The deposits are mainly composed of SiO2 and Al2O3, about 90%

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Mineral Transformation and Ash Deposit during Coal Combustion

of the deposit. This belongs to the typical sialic type according to the classification system of Vassilev and Vassileva [37]. The silica ratio (SiO2/(SiO2+Fe2O3+CaO+MgO)) is known to affect the fusion characteristics and the sintering temperature of ash [36]. However, the severity of deposition is not proportional to this value. This implies that the chemical composition is not the only factor that determines ash deposition, the occurrence and speciation of inorganic matters are the more important influence factors. Table 12. Chemical composition of ash deposits

FWF RWF FW RW LSW RSW

SiO2 60.12 63.46 61.63 64.3 60.61 63.3

Al2O3 28.86 24.83 26.39 24.47 26.81 25.46

Fe2O3 4.39 4.63 4.67 3.95 5.56 3.9

CaO 2.6 2.42 2.02 3.14 2.6 2.18

MgO 0.99 1.23 1.61 1.2 1.19 1.24

Na2O 0.39 0.5 0.49 0.28 0.62 0.84

K2O 1.5 1.75 1.87 1.65 1.45 1.99

P2O5 0.25 0.32 0.35 0.28 0.37 0.28

TiO2 0.91 0.87 0.98 0.75 0.8 0.8

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Table 13. Chemical composition of deposit layers

FWF(outer) FWF(inner) RWF(outer) RWF(middle) RWF(inner) FW(outer) FW(middle) FW(inner) RSW(outer) RSW(middle) RSW(inner)

SiO2 61.3 58.9 63.1 64.1 63.2 62.6 60.4 61.9 64.6 64.1 61.2

Al2O3 26.4 31.3 24.8 23.9 25.8 24.2 27.3 27.7 24.6 25.1 26.7

Fe2O3 4.8 4.0 4.2 4.6 5.1 4.9 5.6 3.5 3.8 4.0 3.9

CaO 2.7 2.5 2.7 2.6 2.0 2.5 1.5 2.0 2.1 2.1 2.4

MgO 1.3 0.7 1.5 1.3 0.8 1.8 1.7 1.3 1.0 1.1 1.6

Na2O 0.8 0.0 0.9 0.6 0.0 0.8 0.3 0.4 0.8 0.5 1.2

K2O 1.5 1.5 1.6 1.7 1.9 1.8 1.9 1.9 2.1 2.0 1.9

P2O5 0.3 0.2 0.4 0.3 0.2 0.4 0.3 0.3 0.3 0.2 0.3

TiO2 0.9 0.9 0.8 0.8 0.9 0.9 1.1 0.9 0.8 0.8 0.8

The chemical compositions of different deposit layers are shown in Table 13. The compositions of different layers are similar, while the silica ratio decreases from the inner to outer layer except RSW deposit, as shown in Figure 14. The silica components that have a higher fusion temperature mainly come from the fly ash particles, which formed during coal combustion. The high concentration of SiO2 causes the inner layer of slag to have a higher viscosity than others at high temperature [30]. The contents of other oxides such as Fe2O3, CaO, MgO, etc. which have a significant influence on deposit, vary in different deposit layers.

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RSW(inner) RSW(middle) RSW(outer)

FW(inner) FW(middle) FW(outer)

RWF(inner) RWF(middle) RWF(outer)

FWF(inner) FWF(outer)

Figure 14. The silica ratios of ash deposits.

Table 14. Semiquantitative content of mineral composition in ash deposits (wt. %) Samples

RWF

FWF

RW

FW

LSW

RSW

Mullite Quartz Cristobalite Hematite Hercynite Anorthite

83 12 2 3

74 16 1 9

50 18 24 6

60 5 27 8

36 26 2

39 12 24 8 5 12

20 7

RWF (middle) 83 5 6 6

RWF (outer) 89 10 1

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36

RWF (inner) 73

Figure 15. X-ray diffraction patterns of ash deposits (RWF+FWF) (M: mullite; C: cristobalite; Q: quartz; He: hematite).

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Figure 16. X-ray diffraction patterns of ash deposits (RSW+LSW).(M: mullite; C: cristobalite; Q: quartz; He: hematite; Hy: hercynite; A: anorthite).

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3.3. Mineralogy of Ash Deposits The minerals in deposits contain mullite, cristobalite, quartz, hematite, hercynite, and anorthite (Figure 15-17, Table 14). Iron-bearing mineral is one of the primary components contributing to deposit strengthening; hematite has been identified in all deposits except LSW deposit (Figure 16). The LSW deposit with loose structure (Figure 13) is mainly composed of ash particles and the slag severity is the weakest. This validated that the iron-bearing mineral has a significant effect on ash deposition. Besides hemetite, hercynite is also identified in RSW (Figure 16) and RW deposits (Figure 17). Although there is no iron-bearing minerals enrichment in all deposits, the fluxing influence can not be ignored. Cristobalite, which formed at 1470 , is the metastable high temperature transformation phase of quartz in coal. Mullite is derived from the decomposition of kaolinite, the occurrence of hercynite and anorthite indicate a degree of chemical reaction between the aluminosilicates and the decomposition products of carbonates minerals or pyrite in coal [38]. The interaction or coalescence of minerals would results the fast ash deposit growth and the fast drop in heat transfer. This is attributed to the production of the coarse particle size distribution as well as the large proportion of sticky ash particles [17]. A high degree of crystallinity anorthite is also observed in the deposits by Unsworth (1988), they thought anorthite is formed via solid-state reactions and not by recrystallization from a homogeneous melt [39].

FeO + Al2O3 → FeAl2O4 CaO + Al2O3 ⋅ 2SiO2 (metakaolin) → CaAl2 Si2O8

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The minerals in different layers of RWF deposition were identified (Figure 18). Compared the contents of minerals in different layers of RWF deposition (Table 14), it can be found that iron-bearing mineral is relatively enriched in the inner layer, while more mullite is identified in the outer layer. The mineral distribution in different position also indicated that the deposits in high temperature area near the flame (RWF, FWF) contain more mullite (Table 14), while the deposits in the burnout area contain more cristobalite, the interaction formation minerals are also found in the later area.

Figure 17. X-ray diffraction patterns of ash deposits (RW+FW) (M: mullite; C: cristobalite; Q: quartz; He: hematite; Hy: hercynite).

3.4. Microstructure of ash deposits 3.4.1. Optical microscopy Ash deposits are porous materials, whose physical and chemical characteristics change with time, during the process of initiation, growth and maturation [36]. Lots of pores with different diameters were distributed in the deposits (Figure 19). According to the pore size distribution, it is classified into three categories: macro pore >1mm, medium pore 0.1-1mm, micro pore 0.05). So the possibility that the members of ethnic Hmong clan P have been exposed to a lower level of As pollution could be largely ignored. Table 3. Comparison of GSTP1 A1578G (Ile105Val) genotypes of arseniasis cases versus non-arseniasis individuals (Lin et al. 2006) Genotypes or alleles A/A A/G G/G A G

Arseniasis group 14 (23.3%) 36 (60%) 10 (16.7%) 64 (53.3%) 56 (46.7%)

Non-arseniasis group 69 (59.0%) 46 (39.3%) 2 (1.7%) 184 (78.6%) 50 (21.4%)

A/A A/G G/G A G

10 (28.6%) 19 (54.3%) 6 (17.1%) 39 (55.7%) 31 (44.3%)

38 (62.3%) 23 (37.1%) 0 (0 %) 99 (81.1%) 23 (18.9)

Members of both clans: female only

A/A A/G G/G A G

5 (20%) 16 (64%) 4 (16%) 26 (52%) 24 (48%)

30 (53.6%) 24 (42.9%) 2 (3.6%) 84 (75%) 28 (25%)

Han clan G1

A/A A/G G/G A G

8 (16%) 33 (66%) 9 (18%) 49 (49%) 51 (51%)

26 (47.3%) 27 (49.1%) 2 (3.6%) 79 (71.8%) 31 (28.2%)

A/A A/G G/G A G

4 (14.8%) 18 (66.7%) 5 (18.5%) 26 (48.1%) 28 (51.9%)

16 (50%) 16 (50%) 0 (0%) 48 (75%) 16 (25%)

A/A A/G G/G A G

5 (21.7%) 14 (60.9%) 4 (17.4%) 24 (52.2%) 22 (47.8%)

9 (39.1%) 12 (52.2%) 2 (8.7%) 30 (65.2%) 16 (34.8%)

Members of both clans

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Members of both clans: male only

Han clan G1: male only

Han clan G1: female only

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OR

95% CI

χ2, P*

4.72

2.349.54

20.230, 0.000

3.22

2.005.18

24.202, 0.000

4.13

1.6810.14

10.117, 0.001#

3.42

1.786.58

14.233, 0.000##

4.62

1.5214.03

7.938, 0.005#

2.77

1.375.58

8.390, 0.004##

4.71

1.8711.85

11.698, 0.001#

2.65

1.504.70

11.459, 0.001##

5.75

1.6220.43

8.091, 0.004#

3.23

1.487.03

9.031, 0.003##

2.31

0.638.47

1.643, 0.200#

1.72

0.74-

1.614,

215

Possible Involvement of Ethnicity and Clan Consanguinity …

Hmong clan P

Hmong clan P: male only

Hmong clan P: female only $$$

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Between Hmong clan P and Han clan G1

Genotypes or alleles A/A A/G G/G A G

Arseniasis group 6 (60%) 3 (30%) 1 (10%) 15 (75%) 5 (25%)

Non-arseniasis group 43 (69.4%) 19 (30.6%) 0 (0%) 105 (84.7%) 19 (15.3%)

A/A A/G G/G A G

6 (75%) 1 (12.5%) 1 (12.5) 13 (81.2%) 3 (18.8%)

22 (75.9%) 7 (24.1%) 0 (0%) 51 (87.9%) 7 (12.1%)

A/A A/G G/G A G

0 (0%) 2 (100%) 0 (0%) 2 (50%) 2 (50%)

21 (63.6%) 12 (36.4%) 0 (0%) 54 (81.8%) 12 (18.2%)

A/A

Non-arseniasis group in Hmong clan P 43 (69.4%)

Non-arseniasis group in Han clan G1 26 (47.3%)

A/G G/G A G

19 (30.6%) 0 (0%) 105 (84.7%) 19 (15.3%)

27 (49.1%) 2 (3.6%) 79 (71.8%) 31 (28.2%)

3.98 95% CI

0.204##

1.51

0.385.97

0.347, 0.556#

1.84

0.605.67

1.161, 0.281##

1.05

0.176.42

0.003, 0.960#

1.68

0.387.41

0.479, 0.489##

/

/

/

4.50

0.5835.21

2.386, 0.122

0.40

0.190.84

5.874, 0.015$

0.46

0.240.88

5.737, 0.017$$

OR

χ2, P*

*Chi-square test was used # The frequencies of combined mutant genotypes (G/G1578+ A/G1578) of arseniasis cases versus nondiseased individuals were compared. ## The frequencies of mutant allele G1578 of arseniasis cases versus non-diseased individuals were compared. $ The population frequency of combined mutant genotypes (G/G1578+ A/G1578) in non-diseased individuals of clan P versus individuals of clan G1 was compared. $$ The population frequency of combined mutant allele G1578 in non-diseased individuals of clan P versus individuals of clan G1 was compared. $$$ Since only limited cases among female clan P members were found (n=2), no comparison was made in this subgroup.

Genetic Polymorphism Might Play a Role in the Modulation of Arseniasis Risk in Exposed Rural Population Active efflux system of glutathione-conjugates of arsenic mediated by MRP1/ABCC1 has been proved to be an important route of arsenic detoxification in eukaryotes. The formation of As (GS)3 is necessary for the efflux and GSTP1 is critical for complex formation (Leslie et al. 2004). Glutathione S-transferase P1 A1578G polymorphism was taken as example to test the possible involvement of heredity-related factors in the modulation of

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arseniasis risks in the case of decades-long exposure to indoor combustion of local high Ascontent coal. The overall comparison displayed a clear-cut picture that the diagnosed patient group in this village displayed an elevated presentation of mutant genotypes combined G/G1578 + A/G1578 (76.6% vs. 41.0%, OR=4.72, 95% CI: 2.34-9.54, P=7*10-6) and of mutant allele G1578 (46.7% vs. 21.4%, OR=3.22, 95% CI: 2.00-5.18, P=9*10-7). A similar situation was also observed inside Han clan G1: a higher frequency of the mutant genotypes combined (84.0% vs. 52.7%, OR=4.71, 95% CI: 1.87-11.85, P=6*10-4) and an increased frequency of mutant allele G (51.0% vs. 28.2%, OR=2.65, 95% CI: 1.50-4.70, P=7*10-4) were detected among patients. However that was not the case in ethnic Hmong clan P, either as a whole or separately in both genders. Although a relatively higher display of G/G1578 + A/G1578 was also found in diseased Hmong patients than in non-arseniasis members, deviation was quite small and no statistical significance was ever reached (40% vs. 30%, P=0.556). This was also the case, when the portions of G1578 allele carriers were compared (25% vs. 15.3%, P=0.281).

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Some Kind of Possible Genetic Heterogeneity of Ethnic Hmong People in the Area Interestingly, the overall spectrum of polymorphism at the locus of GSTP1 A1578G (Ile105Val) in non-diseased Hmong showed a distinct difference to that of their ethnic Han counterparts (P=0.0275). The population frequency of mutant genotypes (G/G1578 + A/G1578) was less profound in non-diseased Hmong individuals than in neighboring Han counterparts (30.6% vs. 52.7%, OR=0.40, 95% CI: 0.19-0.84, P=0.015) and the same as for mutant allele G1578 alone (OR=0.46, 95% CI: 0.24-0.88, P=0.017). It should be mentioned that the population frequencies of variant polymorphic forms of GSTP1 A1578G (Ile105Val) (Lin et al. 2006) or of GSTM1 (Lin et al. 2007) of normal WanShu-Maio (Bent–Comb-Hmong) differ greatly from other Eastern Asian populations. When the non-diseased individuals of both clans were compared with a healthy ethnic Han population in Guangdong (old spelling: Canton) province (Nie et al. 2002) in Southern China, non-diseased members of Han clan G1 showed very similar frequencies of wild and mutant alleles, compared with general Guangdong population (28.2% vs. 24.4% for G allele, P=0.429), while Hmong clan P showed a different spectrum with a significant lower portion of G allele (15.3% vs. 24.4%, OR=0.56, 95% CI: 0.32-0.97, P=0.037). The normal Han population in Guangdong province is located in the Pearl River Delta. In contrast, the Southwest Guizhou Ethnic Minority Autonomous Prefecture is situated in the up-reaching area of the same river. Since ancient time the area along Pearl River has developed closer ethnological, philological, economic and cultural interactions than any other area in Southern China (Lin et al. 2006). In another study (Lin et al. 2007) we found an unusual higher frequency (71%) of GSTM1 0/0 polymorphic status in an ethnic Hmong clan of our target township, which is quite exceptional compared to any other Eastern Asian population (mostly, around 50%) reported so far. The significant differences in population frequencies, either of genotype or of phenotype, of polymorphic loci between the ethnic Hmong groups and other East/Southeast Asian populations have also been reported in the Hmong population settled in Minnesota,

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Possible Involvement of Ethnicity and Clan Consanguinity …

217

USA (Kiffmeyer et al. 2004; Straka et al. 2006), whose families have emigrated from Southeast Asian countries after the Vietnam War. Their forefathers had left Chinese southwest provinces (including Guizhou province) and moved south during the last couple of centuries. The above reports might hint at some kind of genetic heterogeneity of Hmong people in the area (Lin et al. 2007).

CONCLUSIVE REMARKS The data obtained either from field investigation or from the genotyping study of polymorphic locus of metabolic gene suggested that the ethnicity and clan consanguinity might play a role in the modulation of arseniasis risk in this special As exposure scenario. The ethnic Hmong villagers seemed to be the least susceptible to arseniasis, compared with other local ethnic groups.

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LITERATURE Chen, C. J., Hsu, L. I., Wang, C. H., Shih, W. L., Hsu, Y. H. & Tseng, M. P., et al. (2005). Biomarkers of exposure, effect, and susceptibility of arsenic-induced health hazards in Taiwan. Toxicol Appl Pharmacol, 206, 198-206. Chen, J. G., Chen, Y. G., Zhou, Y. S., Lin, G. F., Li, X. J., & Jia, C. G., et al. (2007). A follow-up study of mortality among the arseniasis patients exposed to indoor combustion of high arsenic coal in Southwest Guizhou Autonomous Prefecture, China. Int Arch Occup Environ Health, 81, 9-17. Chen, J. G., Lin, G. F., Chen, Y. G., Jia, C. G., Zhou, Y. S. & Meng, H. et al. (2009). Arseniasis prevalence and mortality in a multiethnic, endemic township in Guizhou, China. Int Arch Occup Environ Health, 82, 499-508. Ding, Z. H., Zheng, B. S., Long, J. P., He, B. K., Finkelman, R. B. & Chen, C. G. et al. (2001). Geological and geochemical characterities of high arsenic coal from endemic arseniasis area in southwestern Guizhou Province, China. Appl Geochem, 16, 1353-1360. Ferreccio, C., Gonzalez, C., Milosavjlevic, V., Marshall, G., Sancha, A. M. & Smith, A. H. (2000). Lung cancer and arsenic concentrations in drinking water in Chile. Epidemiology, 11, 673-679. Hopenhayn-Rich, C., Biggs, M. L., Fuchs, A., Bergoglio, R., Tello, E. E., Nicolli, H. & Smith, A. H. (1996). Bladder cancer mortality associated with arsenic in drinking water in Argentina. Epidemiology, 7, 117-124. Hopenhayn-Rich, C., Biggs, M. L., Smith, A. H., Kalman, D. A., & Moore, L. E. (1996). Methylation study of a population environmentally exposed to arsenic in drinking water. Environ Health Perspect, 104, 620-628. Hopenhayn-Rich, C., Biggs M. L., & Smith A. H. (1998). Lung and kidney cancer mortality associated with arsenic in drinking water in Córdoba, Argentina. Int J Epidemiol, 27, 561-569. Jin, Y., Liang, C., He, G. & Cao, J. (2003). Study on distribution of endemic arseniasis in China. Wei Sheng Yan Jiu (J Hyg Res), 32, 519-540. in Chinese.

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Kiffmeyer, W. R., Langer, E., Davies, S. M., Envall, J., Robison, L. L. & Ross, J. A. (2004). Genetic polymorphisms in the Hmong population: implications for cancer etiology and survival. Cancer, 100, 411-417. Leslie, E. M., Haimeur, A. & Waalkes, M. P. (2004). Arsenic transport by the human multidrug resistance protein 1 (MRP1/ABCC1), evidence that a tri-glutathione conjugate is required. J Biol Chem, 279, 32700-32708. Lin, G. F., Chen, J. G., Zhou, Y. S. & Shen, J. H. (2003). Family aggregation of chronic arsenic poisoning associated with indoor burning of high arsenic coal. Toxicology, 191, 19 (abstract). Lin, G. F., Du, H., Chen, J. G., Lu, H. C., Guo, W. C. & Meng, H., et al. (2006). Arsenicrelated skin lesions and glutathione S-transferase P1 A1578G (Ile105Val) polymorphism in two ethnic clans exposed to indoor combustion of high arsenic coal in one village. Pharmacogenet Genomics, 16, 863-871. Lin, G. F., Du, H., Chen, J. G., Lu, H. C., Kai, J. X. & Zhou, Y. S., et al. (2007). Glutathione S-transferases M1 and T1 polymorphisms and arsenic content in hair and urine in two ethnic clans exposed to indoor combustion of high arsenic coal in Southwest Guizhou, China. Arch Toxicol, 81, 545-551. Liu, J., Zheng, B. S. & Aposian, H. V. (2002). Chronic arsenic poisoning from burning higharsenic coal in Guizhou, China. Environ Health Perspect, 110, 119-22. Nie, L. H., Hu, Y. L. & Wang, S. Y. (2002). Genetic polymorphism of human glutathione Stransferase P1 gene among the Han nationality in Guangdong province. Chin J Pathophysiol, 18, 480-482. in Chinese. Straka, R. J., Burkhardt, R. T., Lang, N. P., Vang, T., Hadsall, K. Z. & Tsai, M. Y. (2006). Verified predominance of slow acetylator phenotype N-acetyltransferase 2 (NAT2) in a Hmong population residing in Minnesota, Biopharm Drug Dispos, 27, 299-304. Tondel, M., Rahman, M., Magnuson, A., Chowdhury, I. A., Faruquee, M. H. & Ahmad, S. A. (1999). The relationship of arsenic levels in drinking water and the prevalence rate of skin lesions in Bangladesh. Environ Health Perspect, 107, 727-729. Vahter M. (2000). Genetic polymorphism in the biotransformation of inorganic arsenic and its role in toxicity. Toxicol Lett, 112-113, 209-217. WHO Environmental Health Criteria (2001). 224, Arsenic and Arsenic Compounds (Ng J, ed) 2nd ed., International Program on Chemical Safety, World Health Organization, 2001, Geneva. Zheng, B. S., Wang, B. B., Ding, Z. H., Zhou, D. X., Zhou, Y. S., Chen, C. C. & Finkelman, R. B. (2005). Endemic arsenosis caused by indoor combustion of high-As coal in Guizhou Province, P.R. China. Environ Geochem Health, 27(5-6), 521-528. Zhou, D. X., Liu, D. N., Zhu, S. L., Li, B. L., Jin, D. X. & Zhou, Y. S., et al. (1993). Investigation of chronic arsenic poisoning caused by high arsenic coal pollution. Chin J Prev Med, 27, 147-150 (in Chinese). Zhou, D. X., Zhou, Y. S., Zhou, C., Jin, D. X., Peng, J. H. & Luo, M. L., et al. (1994). Correlation of total arsenic intake and incidence of arsenic poisoning in coal type endemic area. Chin J Endemic Diseases, 13, 215-218 (in Chinese).

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Chapter 7

PYROGENIC METAMORPHISM OF THE CARBONACEOUS ROCKS IN THE SOUTH OF THE SIBERIAN PLATFORM N.I. Akulov*, V.V. Akulova and E.V. Khudonogova Institute of the Earth’s Crust, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia

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1. INTRODUCTION Coal combustion is rather common natural phenomenon. Pyrogenic metamorphismrelated events occurred many times during the Earth’s long history (Bentor & Kastner, 1976; Bustin & Mathews, 1982; Church et al., 1979; Chesnokov & Sherbakova, 1991; Cosca et al., 1989; Essene et al., 1984; Fermor, 1918; McLintock, 1932; Rattigan, 1967; Sokol et al., 1998; Stracher & Taylor, 2004; Tilley, 1924; Venkatesh, 1952; and others). The products of oxidation and combustion within the burning coal-waste heaps (terricones) are a serious problem because they exert an adverse effect on the atmosphere, soils, and surface and ground water resources. However, they are of considerable practical importance, and the coal-waste heaps alone are compact natural laboratories to study a whole variety of previous and current geological and geochemical processes of pyrogenic metamorphism. The burnt rocks have a wide use in road filling and in building industry for building material production, whereas coal ash is used for the extraction of rare metals: germanium, gallium, titanium, and vanadium. The burnt rocks have been also used in gemology. Pyrogenic metamorphism has recently been the subject of some interesting investigations resulted in discovery of new minerals and solutions for complex problems of pyrogenic mineralogy (Chesnokov, 2001; Heffern & Coates, 2004; Panov, 2004; Potapov & Maksimovich, 2006; Sharygin et al., 2009; Sokol et al., 2000; 2005; Stracher & Taylor, 2004; * Institute of the Earth’s Crust, Siberian Branch of the Russian Academy of Sciences, 644033, Irkutsk, Russia, [email protected] Coal Combustion Research, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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Stracher, 2007; Zborshik & Osokin, 2000, 2004; and others). Nevertheless, in the context of geology pyrogenesis is poorly known. The lack of lithologic basis for the analysis of trends in natural types of pyrogenesis does not allow comparison between the processes of lithogenesis and pyrogenesis. The purpose of this work has been to study the most important products of pyrogenic metamorphism of the carbonaceous deposits of the Siberian platform and their formation peculiarities making comparison with the basic stages of lithogenesis.

2. SUBJECT AND TECHNIQUE OF RESEARCH

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The studies have covered pyrogenically metamorphized complexes quarried along the Bratsk-Sedanovo-Kodinsk road in Priangarye and Kansk-Bolshiye Klyuchi-Irbeiskoe road in the area of the Kansko-Achinsk coal basin, and on the terricones of the Cheremkhovo coal deposit (Fig. 1). The natural occurrence of quarried pyrogenically metamorphized complexes is associated with extensive areas (about 2 km2) of red and variegated rocks, formed by the natural burning of coal beds that enabled one to collect many burnt rock samples in rather deep (up to 10 m) quarries excavated for road filling.

Figure1. A general map of the investigated areas. 1-2 – coal basins: 1- Kansko-Achinsk, 2- Irkutsk; 3 – investigated areas of the Jurassic rocks that have been altered by burning of coal beds: 1- quarries worked near the town of Kansk (Kansko-Achinsk coal basin), 2- quarries along the Sedanovo-Kodinsk road, 3- waste heaps of the Cheremkhovo coal deposit (Irkutsk coal basin); 4-5 – boundaries: 4Siberian platform, 5- national.

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Some interesting results have been obtained in the investigation of artificial redeposition of the Lower Jurassic carbonaceous rocks on refuse heaps – terricones. In deep mining at the Cheremkhovo coal deposit, the barren host rocks (sandstones, carbonaceous aleurolites, argillites etc.), high-ash and low-grade coals and technogenic waste were tossed into the refuse heap. In response to heat generated from in-situ burning of coal beds the Mesozoic carbonbearing formations were highly altered so that it is impossible to determine their initial composition. Because of this, they are said to be the products of underground burning and were originally referred to as burnt rocks (Leonhard, 1824). E. Callegari and N.N. Pertsev (2007) reported that the term “burnt rocks” is comparable with such former terms as thermantide, thermantide porcellanite, fused shale, porcellanite, clinker, and porcelain jasper. Glassy or vitrified varieties of burnt rocks used to be spoken of respectively as buchite (coalfire buchite) or fritted rock. The term “burnt rocks” implies also ash deposits produced by burning of coal beds. The collected samples have been roughly grouped into baked but unfused rocks (pyrogenic breccias) and fused rocks (paralavas). Although the term “clinker” has been widely used in geology for all the rocks altered during natural coal combustion, it is important to subdivide rocks into types for ambiguity avoidance, which is why the additional term “pyrogenic breccia” as used herein has been introduced. The contents of rock constituent (Fe2O3total., Al2O3, MgO, CaO, SiO2, MnO, TiO2, Na2O, K2O, P2O5, Stotal) and trace (Cr, V, La, Ce, Nd, Pb, Ni, Zn, As, Cu, Sc, Ga, Co, Ba) elements have been determined by X-ray fluorescence (XRFA) with S4 PIONEER X-ray spectrometer (Brukner, Germany) using standard methods and practice. Each analytical sample has been assigned optimal angular positions to measure intensities of analytical lines and background, method of correction of the element interaction, and standard rock samples to calculate calibration characteristics (Revenko & Khudonogova, 2005). The radiating elements have been made by compressing the samples into tablet form. The precision of results for the analyte contents has been estimated in the meteorological investigation. The values determined for the mean square deviations of the analysis results correspond to precision category III (analyst E.V. Khudonogova). Most x-ray determinations have been made using DRON-3.0 diffractometer (CuKα radiation) at a voltage of 25 kV and current intensity 20 mA. X-ray measurements have been performed by Z.F. Ushapovskaya. Petrographic composition of the burned rocks has been analyzed with an “Olympus DP12” microscope.

3. GENERAL CHARACTERISTIC OF CARBONACEOUS FORMATIONS The southern part of the Siberian platform has abundant coal deposits formed during various periods in the platform development. We recognize three stages of coal accumulation: Late Paleozoic, Mesozoic and Cenozoic, with numerous evidence of coal burning underground only found for the Mesozoic stage because the Mesozoic complex rocks are well exposed and easily accessible for visual study. The Late Paleozoic and Cenozoic carbonaceous deposits are much less abundant. Besides, the Cenozoic carbonaceous deposits

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are primarily confined to deep pericratonic basins on the southeast margin of the Siberian platform wherein they are overlain by a large unit of Quaternary sediments. In this connection, both Cenozoic and Upper Paleozoic carbonaceous deposits are almost inaccessible for visual study. The Jurassic formation in the south of the Siberian platform has been most thoroughly penetrated in the Irkutsk and Kansko-Achinsk coal basins (Fig. 1). In the Irkutsk coal basin, only Lower Jurassic deposits are carbonaceous, being as thick as 300 m and containing as many as 46 coal beds. The thickest coal beds have been penetrated in the Cheremkhovo deposit located 130 km northwest of Irkutsk. Three out of five coal beds are workable. The coal beds vary in cumulative thickness from 8 to 18 m. According to the data of A.S. Strugov (1974), these are hard, gas-containing coal seams whose ash content is 35.7%, sulfur content as high as 1.79%, highest specific heat 7560-7700 kcal/kg, and combustion heat 3810-3920 kcal/kg. In the Kansko-Achinsk coal-basin area, the burnt rocks are associated with the Upper Jurassic carbonaceous deposits. 30 coal deposits have been discovered and 7 coalfields have been found throughout the basin. As of now, the deposits under exploitations are Borodinsky, Berezovsky, Nazarovsky, Pereyaslovsky, Irbeisky, and others. The coal mined here is singletype and brown. Its ash content is as high as 12%, average humidity 35%, density up to 1.5 t/m3, heating capacity 2800-3800 kcal/kg, and total sulfur content 0.3-1.0% (Gavrilin & Ozersky, 1996). CaO dominates in the ash in a quantity ranging from 25 to 61%; the concentrations of toxic and radioactive elements are insignificant.

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4. PRODUCTS OF PYROGENIC METAMORPHISM A process for forming burnt rocks was called pyrogenic metamorphism (Kalkowsky, 1886). It is to be supposed that pyrogenic metamorphism of carbonaceous rocks originates from a self-burning coal seam, viewed as the seat of pyrogenic source formation. Coal burning lasts hundreds of years and involves vast areas. The quarries that penetrated the combustion products may exhibit a series of pyrogenic changes from peripheral part to source zones. Pyrogenic metamorphism gives rise to fusion of sedimentary rocks in the source zone, associated with irreversible thermal and metasomatic alternations of rocks in the peripheral part. The study of numerous “red-bed” outcrops produced by coal combustion near the settlement of Irbeiskoe (Kansko-Achinsk coal basin area) has revealed a strongly magnetic ore body that is a solidified paralava. Its occurrence is associated with fusion of siderite concretions in the source zone of pyrogenesis (Akulov, 2006). X-ray-structural analysis has shown that the paralava is composed primarily of magnetite and contains admixtures of αcristobalite, X-ray amorphous material, magnetoplumbite, and galenite. Most of the X-ray amorphous material that may have arisen by rapidly cooling supernatant liquid consists of amorphous alloys – metallic glasses (metglasses). Magnetite or black iron ore has a bluish tarnish, high magnetic content and high specific gravity (Fig. 2). An ore body is a compound system because the pressure of overlying rocks pushed the “siderite” paralava into cavities and fractures produced by burning coal (Fig. 3). The magnetite body thickness grows abruptly and attains several meters there. An apparent

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extension of the ore body was about 30 m. The percentage composition in the central part of the ore body is as follows: Fe2O3+FeO (96.39); Al2O3 (1.09), MgO (2.56), CaO (0.34), SiO2 (1.56), MnO (1.94), TiO2 (0.027), Na2O (0.048), K2O (0.006), P2O5 (0.25); Stotal (0.033). The trace element data show high cobalt and barium contents. The presence of magnetite ores with high iron content and a large extension of the ore body allowed us to apply for the certificate of the Fedosyevsky ore occurrence.

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Figure 2. Magnetite iron ore resulted from burning of coal beds and fusion of siderite concretions. Mined in the quarry near the settlement of Irbeiskoe (Kansko-Achinsk coal basin).

Figure 3. Magnetite-filled fractures in burnt sandstone.

Almost twenty years have passed since that time, and when got there again we have found that the Fedosyevsky magnetite ore occurrence is quarried actively for a new road

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filling, not for iron production. The roadbeds constructed with the use of burnt rocks are characterized by red color (from grayish-cherry to tile-red) and an enhanced stability.

a 2. Fig. b

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c

Fig. 6.

Figure 4. Pyrogenically metamorphized complex composed of picturesque red rocks. Geological section of one of the waste heaps in the Cheremkhovo coal deposit (the rocks are snow-powdered). The fragments show: a – sintered products in undisturbed mine-waste succession; b – white sheets of porcellanite; c – white-surface porcellanite with dotty inclusions of black hematite.

Pyrogenic metamorphism in artificially redeposited carbonaceous rocks results from burning coal-waste heaps. Carbonaceous rock debris, which has been hitherto in the diagenesis stage, appears to be substantially immediately under low-baric high-temperature catagenesis. Open combustion of the coal-waste heap materials is a rapid process, rarely lasting more than a month. It gives rise to an entirely new pyrogenically metamorphized

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complex that is usually composed of picturesque red debris fused together (Fig. 4). Besides, the coal-waste heaps have shown small-size (0.3×0.16×0.05 m) fusions of lens-shaped ore bodies that are not magnetite but hematite, containing inclusions of gypsum and some quantities of α-cristobalite and plagioclase (Fig. 5). Hematite has the following percentage composition: Fe2O3+FeO (81.9); Al2O3 (1.87), MgO (0.27), CaO (0.11), SiO2 (13.54), MnO (0.56), TiO2 (0.039), Na2O (0.058), K2O (0.065), P2O5 (0.24); Stotal (0.24). The trace element data show high cobalt (120 ppm) and chrome (170 ppm) contents.

Hematite

а-сristobalite

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Gypsum

Figure 5. Hematite ore admixed with gypsum and α-crystobalite.

It is pertinent to note that pyrogenic source whose temperature runs as high as 2000°C becomes a reasonable source for fusion of sandstones and other sedimentary rocks producing basalt-like paralavas. All parabasalts are homogeneous and cryptocrystalline. They have dark-gray color and massive vesicular, scoriaceous or fluidal texture. They occur in a small paralava flow or parasill. The thickness of single paralava flows is usually small and rarely attains 1 m. As far as we know, pyrogenic metamorphism is characterized by relatively high temperature, low general pressure and low oxygen and water fugacity that provide the formation of entirely differentiated basic paralavas similar in composition to the Moon basalts (Panov, 1993; Sokol et al, 2000). A comparative analysis of the pyrogenic paralavas and different types of the Earth and Mars basaltoids has shown the similarity of their chemical compositions (Fig. 6, 7). They have been found to contain 46.6 - 51.1% of silica and to be richly supplied with Fe, Al, Ca and poorly supplied with Mn, P, K. Besides, they have high zinc content.

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226 60 50

Content, %

40 30 20 10 0 0

1

SiO2

2

3

Al2O3

FeO

4

5

MgO

6 7 Types of the rocks

CaO

Figure 6. 3

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Content, %

2,5 2 1,5 1 0,5 0 0

Na2O

1

K2O

2

TiO2

3

MnO

4

5 6 Types of the rocks

7

P2O5

Figure 7. Figure 6. and Figure 7. Comparative analysis between the pyrogenic paralavas and Earth and Mars basaltoids showing they are similar in their chemical compositions. Types of the rocks: 1 – islandarc basalts*; 2 – Siberian trapps*; 3 – Mars basalts (?) (Shergotty)*; 4 – basalts from the EastPacific Rise (В 13-30/7; http://www.fegi.ru); 5 – basalts from the East-Pacific Rise (В 13-30; http://www.fegi.ru); 6 – pyrogenic paralava. *A.A. Yaroshevskii. Problems of modern geochemistry (http://geo.web)

Pyrogenically metamorphized rocks in the peripheral part, directly adjacent to paralavas, are represented by different types of variegated pyrogenic breccias (earlier called clinker) intensively altered during thermal metasomatism. In geology terms, all consolidated rocks consisting of angular fragments are called breccias (sedimentary, volcanic, tectonic).

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Pyrogenic breccias are the result for simultaneous processes of burning, incomplete fusion, and welding of burnt sedimentary rock fragments. The welded, thermally altered sedimentary rock fragments are cemented together by fused parabasalts, pyrogenic glass (silicon oxides) or calcite. The pyrogenic breccias are the most typical product of pyrogenic metamorphism. Close to and on contact with paralavas there are occurrences of paralavobreccias. These are hybrid rocks composed of xenoliths of burnt rocks stiffened with parabasaltic lava (Fig. 8).

Figure 8. Paralava breccia as a type of pyrogenic rocks.

The breccias cemented by pyrogenic glass, whose porosity is associated with loosely packed variously seized fragments of the burnt rocks, appear to be some distance away from the source zone. The pyrogenic breccia varieties may be classified into there types according to fragment sizes that are 10 cm or larger for coarse-grained breccias (1), 1-10 cm for breccias (2) and 1 mm - 1 cm for fine--grained breccias (3) (Fig.9-11).

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Figure 9. Coarse-grained pyrogenic breccia cemented or welded by pyrogenic glass (silica), always high-porosity because of loosely packed variously seized fragments of thermally altered rocks.

Figure 10. Fine-grained pyrogenic breccia cemented by opaque or lacteous quartz (silica).

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Figure 11. Pyrogenic breccia cemented by calcite and quartz.

Pyrolytic schist

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Pyrogenic breccia

Figure 12. A sharp contact line between red pyrogenic breccias and pyrolized grayish-black schists.

The refuse heaps that consist of coarse-grained carbonaceous material rise above the surrounding surface thus providing good aeration in rocks and, consequently, oxidizing roasting. The sites in refuse heap undergone oxidizing roasting always have red color because of intensive iron oxidation and impregnation of rocks with finely dispersed hematite. Aeration zones in refuse heaps are viewed as the seat of the highest-temperature reworking of clastogenic material (welding, fusion) that produces the chimney structures of strongly welded red coarse-grained breccias. Similar chimneys have been observed in paralava outcrops in Australia (Baker, 1953), in naturally burned coal beds in the Powder River Basin (Cosca et al., 1989) and also from the combustion metamorphic rocks of the Monterey Formation (Bentor et a1., l98l).

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The fusion and welding temperature for coarse-grained breccias reached 1000°C, as derived from studies of the pyrometamorphic rocks associated with naturally burned coal beds in the Powder River Basin showing that glass compositions that have been used to estimate minimum temperatures of paralava formation and are in the range from 1020 to 1400°С (Cosca et al.,1989). The decorative clinker tile is made at 1300°С (Lokhova et al., 2002). Besides, V.V. Sharygin and his coauthors (2009) suggested that the Ravat coal-fired paralava crystallized at 1100-1200°С. However, in spite of such high formation temperature of red pyrogenic breccias, a line of their contact with pyrolized grayish-black schists is very sharp and does not display evidence of thermal or metasomatic effect (Fig. 12). The reason is that pyrolized schists are highly chemically and thermally durable and low-porosity and therefore almost gas and liquid impermeable. Just as a fireproof wall, they block the thermal effect of the whole complex of pyrogenic breccias (clinkers). Notice that the tile-red products of oxidizing roasting commonly include snow-white and ash-gray porcellanites that show high degree of hygroscopicity and possess schistosity or foliation (Fig. 2b). Porcellanite is a very dense porcelain-like rock consisting of quartz, mullite, feldspar, and some gypsum, aragonite and plagioclase. White-surface porcellanite has well-defined dotty inclusions of black hematite (Fig. 2c). Porcellanite has the following percentage composition: SiO2 (59.96), Al2O3 (33.07), K2O (1.18), CaO (0.93), MgO (0.55), Na2O (0.4), Fe2O3+FeO (0.24); MnO (0.01), TiO2 (0.15), P2O5 (0.058); Stotal (0.68). It shows high content of lead (1100 ppm), barium (220 ppm), arsenic (110 ppm), and gallium (130 ppm). Some of the burnt rocks are loose. Loose starch-like formations are usually represented by yellow or pink slaggy ashes. Yellow slaggy ashes are the products of the low-temperature stage of burning. Pink slaggy ashes are typical of burning at moderate temperature (to 800° C) in oxidizing environment. Like yellow slaggy ashes, they are the basic component of pyrogenically changed complex in small refuse heaps. Slaggy ash formation is associated with low-temperature burning. Loose slaggy ash mass has the following percentage composition: SiO2 (71.53), Al2O3 (15.75), K2O (1.04), CaO (2.53), MgO (2.6), Na2O (0.17), Fe2O3+FeO (5.04); MnO (0.03), TiO2 (0.23), P2O5 (0.037); Stotal (0.15). It has low contents of Сo, Cr, Pb, La, F, As, V, Ce, Nd, Ni, Zn, Cu and Sc along with a high content of barium (190 ppm). To this end, we emphasize that the only process giving rise to combustible gases (hydrogen) in refuse heaps is the interaction of water vapor with overheated coal that occurs during rainfalls. This interaction is sometimes followed by explosions and ejection of overheated pyrogenic material outside the refuse heap causing fire damage to adjacent buildings. This is the reason why fire extinction by water spray in refuse heaps is forbidden (Merkulov, 1981).

5. DISCUSSION Natural burning of coal seams has been long known to be of widespread occurrence. In the southeast of Australia, 200 km north of Sydney, lies the Vingen Mountain that has been smoking for hundreds of years because of the subsurface coal fire (Rattigan, 1967).

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The coal seams are still burning on the right side of the Amur Rver valley, 350 river kilometers from Blagoveshensk (Russia). N.M. Przhevalsky who was the first explorer to see this fire put it down as “burning Tsagyan Mountain in Amur”. It occurred 135 years ago when the famous traveler made his first trip to the south of the Far East (Gusev & Likutov, 1990). The Fan-Yagnob coal deposit in Tadzhikistan is known to have been burning for more that 2000 years (Sharygin et al., 2009). There are several hypotheses concerning the reasons for spontaneous ignition in the case of carbonaceous deposits: 1) lightning strokes; 2) oxidation of coal; 3) biochemical processes; 4) forest fires, and others. We feel inclined to share the opinion of M.P. Zborshik and V.V. Osokin (2000) who consider that spontaneous ignition of carbonaceous deposits, refuse heaps in particular, is associated with biochemical processes. Spontaneous ignition of coal is favored by its authigenic pyrite content (a hypothesis of decomposition of pyrite-bearing carbonaceous rocks through an exothermic reaction producing iron disulfide with the participation of moisture, thionic bacteria and oxygen). Under moist acid conditions, the thionic bacteria (Thiobacillus ferrooxidans) are responsible for the emission of sulfur and the release of a large amount of heat from the pyrite-bearing coals (Zborshik & Osokin, 2000). Dissolved carbon dioxide is a carbon source for bacterial growths, with the components of iron and sulfur oxidation used as an energy source. On spontaneous heating of carbonaceous material higher than 100°C, the metasomatosis and pyrolysis processes become active, and on reaching 120°C, the sulfur starts boiling with sulfuric and water vapor entered the fumarolic system. On heating of pyrolysis rocks higher than 248°C, sulfuric vapor may ignite when exposed to oxygen and start to burn intensively along with numerous hydrocarbon products of pyrolysis. The biochemical process is followed by chemical – pyrogenic. Pyrogenic metamorphism of carbonaceous material may entirely redistribute the chemical composition therein. The primary mineral-forming elements associated with the process of pyrogenic metamorphism are Si–Al–Fe–O, and the secondary elements are Ca–Mg–S–Ti. Unmined-deposit coal is burning much slower than coal-waste heaps. Natural burning of coal seams lasts for hundreds of years whereas the whole coal-waste burning, from biochemical transformations to pyrogenesis, only covers several tens of years. Pyrogenesis results in large-scale emissions of gases into the atmosphere, occurrence of numerous cavities (burnouts) that favor sliding of rock blocks, and appearance of entire pyrogenic complex, which is to be recognized as an individual pyrogenic formation. Considering that pyrogenic metamorphism is everywhere confined to carbonaceous deposits and associated with a vast number of neoformations, pyrogenesis is to be interpreted as one of the forms of geological activity and recognized as geological activity of ignition.

6. CONCLUSIONS 1. The investigations have shown that the effect of pyrogenic metamorphism on the artificially redeposited refuse heap material is not as significant and intensive as on natural carbonaceous deposits. This is reflected by both the volume of thermally altered rock and the degree of thermal alternation.

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N.I. Akulov, V.V. Akulova and E.V. Khudonogova 2. Pyrogenic breccias are the basic products of pyrogenic mertamorphism in coal-waste heaps resulting in thermal baking throughout the carbonaceous complex artificially redeposited around the periphery of pyrogenic source. 3. Pyrogenesis is one of the forms of geological activity (geological activity of ignition) producing entirely new rock types that are to be combined in a pyrogenic formation.

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REFERENCES Akulov, N. I. (2006). – Nodules in carbonaceous sediments of the Southern Tunguska Basin. – Lithol. Miner. Res., 41, 1, 73–84. Baker, G. (1953). – Naturally fused coal ash from Leigh Creek, South Australia. – Transactions of the Royal Society of Southern Australia, 76, l-20. Bentor, Y K., Kastner, M., Perlman, I. & Yellin, Y. (1981). – Combustion metamorphism of bituminous sediments and the formation of melts of granitic and sedimentary composition. – Geochimica et Cosmochimica Acta, 45, 2229-2255. Bentor, Y.K. & Kastner, M. (1976). – Combustion metamorphism in Southern California. – Science, 193, 486-488. Bustin, R .M. & Mathews, W .H. (1982). – In situ gasification of coal, a natural example: History, petrology, and mechanics of combustion. – Can. J. Earth Sci., 19, 514-523. Callegari, E. & Pertsev, N.N. (2007). – A systematic nomenclature for metamorphic rocks: 10. Contact metamorphic rocks. Recommendations by the IUGS Subcommission on the systematics of metamorphic rocks. – Recommendations, online version: http://www.bgs.ac.uk/SCMRH. Chesnokov, B.V. (2001). – Fundamental characteristics of mineralization in burnt waste heaps of the Chelyabinsk coal basin. – In: Potapov, S.S. (ed.): Mineralogy of technogensis-2001. – Inst. Miner. UB RAS, Miass, 9-15 (in Russian). Chesnokov, B.V. & Sherbakova, E.P. (1991). – Mineralogy of burnt waste heaps of the Chelyabinsk coal basin (an experience of mineralogy of thechnogenesis). – Nauka, Moscow, 151 pp. (in Russian). Church, B.N, Matheson, A. & Hora, Z.D. (1979). – Combustion metamorphism in the Hat Creek area, British Columbia. – Can. J. Earth Sci., 16, 1882-1887. Cosca, M.A., Essene, E.J., Geissman, J.W., Simmons, W.B. & Coates, D.A. (1989). – Pyrometamorphic rocks associated with naturally burned coal beds, Powder River Basin, Wyoming. – Am. Miner., 74, 85-100. Essene, E. J., Coates, D. A., Geissman, J.W. & Simmons, W .B. (1984). – Paralavas formed by burning of coal beds (abs.). – EOS, 65, 300. Fermor, L.L. (1918). – Preliminary note on the burning of coal seams at the outcrop. – Transactions of the Mining, Geological, and Metallurgical Institute of India, 12, 50-63. Gavrilin, K.V. & Ozersky, A.Yu. (1996). – Kansko-Achinsk coal basin. – Nedra, Moscow, 272 pp. (in Russian). Gusev, M.N. & Likutov, E.Yu. (1990). – Characteristics of incised meanders in the Upper Amur River. – Geomorphology, 1990, 1, 63-71 (in Russian). Heffern, E.L. & Coates, D.A. (2004). – Geologic history of natural coal-bed fires, Powder River basin, USA. – International Journal of Coal Geology, 59, 1–2, 25–47.

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Kalkowsky, E. (1886). – Elemente der Lithologie. – Carl Winter’s Universitatsbuchhandlung, Heidelberg, 316 pp. Leonhard, K.C. (1824). – Charakteristik der Felsarten, Part 1-3. Part 2, 231-597. – Engelmann, Heidelberg. Lokhova, N.A., Makarova, I.A. & Patramskaya, S.V. (2002). – Fired materials based on burnt clays and microsilica. – Bratsk State Technical University Press, Bratsk, 163 pp. (in Russian). McLintock, W.F.P. (1932). – On the metamorphism produced by the combustion of hydrocarbons in the Tertiary sediments of south-west Persia. – Miner. Mag., 23, 207-227. Merkulov, V.A. (1981). – Environment protection in coal mining. – Nedra, Moscow, 184 pp. (in Russian). Panov, B.S. (1993). – Some problems of ecological mineralogy of the Donetsk basin. – Mineralogical Magazine, 15, 6, 43-50 (in Russian). Panov, B.S. (2004). – Technogenic deposits of Donbass and Ukraine. – In: Bashkov, S.O. (ed.): Epigenetic alteration of aqueous rocks under the influence of technogeneous factors. – Donetsk National Technical University Press, Donetsk, 3-7 (in Russian). Potapov, S.S. & Maksimovich, N.G. (2006). – To mineralogy of burnt waste heaps of the Kizelov coal basin of the Perm territory. – The seventh All Russian readings devoted to Ilmen mineralogist V.O.Polyakov. – Miass, 56-57 (in Russian). Rattigan, J.H. (1967). – Phenomena about Burning Mountain, Wingen, New South Wales. – Aust. J. Sci., 30(5), 183–184. Revenko, A.G. & Khudonogova, E.V. (2005). – X-ray fluorescence determination of minor and tracer element contents in various types of rocks, soils, and sediments using the S4 PIONEER spectrometer. – Ukr. Chem. J.. 9-10 (71), 39-45 (in Russian). Sharygin, V.V., Sokol, E.V. & Belakovsky, D.I. (2009). – Fayalite-sekaninaite paralava from the Ravat coal fire. – Geol. Geophys., 50, 8, 910-932. Sokol, E., Volkova, N. & Lepezin, G. (1998). – Mineralogy of pyrometamorphic rocks associated with naturally burned coal-bearing spoil-heaps of the Chelyabinsk coal basin Russia. – Eur. J. Miner., 10, 1003-1014. Sokol, E.V, Frenkel, A.E., Sharygin, V.V., Kuzmin, D.V., Nigmatulina, E.N. & Lepezin, G.G. (2000). – Parabasalts from burnt waste heats of the Chelyabinsk coal basin as analogues of the Moon’s low-magnesium basalts: from paradox to reality. – Uralian Geology Journal, 6, 165-168 (in Russian). Sokol, E.V., Maksimova, N.V, Nigmatulina, E.N., Sharygin, V.V. & Kalugin, V.M. (2005). – Pyrogeniс metamorphism. – Izd-vo SB RAS, Novosibirsk, 284 pp. (in Russian). Stracher, G.B. & Taylor, T.P. (2004). – Coal fires burning out of control around the world: Thermodynamic recipe for environmental catastrophe. – International Journal of Coal Geology, 59, 1–2, 7–17. Stracher, G.B. (2007). – Coal fires burning around the World: Opportunity for innovative and interdisciplinary research. – GSA Today, 17, 11, 36-37. Strugov, A.S. (1974). – Comparative characteristics of coal-bearing basins of the Siberian platform. – Geol. Geophys., 6, 3-14 (in Russian). Tilley, C.E. (1924). – Contact metamorphism in the Comrie area of the Perthshire Highland. – Quarter. J. Geol. Soc. London, 80, 22-71. Venkatesh, V. (1952). – Development and growth of cordierite in para-lava. – Am. Mineral., 37, 83l-848.

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Zborshik, M.P. & Osokin, V.V. (2000). – Burning coal deposits and their extinction. – Donetsk National Technical University Press, Donetsk, 180 pp. (in Russian). Zborshik, M.P. & Osokin, V.V. (2004). – Neoformations within coals and carbonaceousargillaceous rocks predetermining dangerous and harmful occurrences therein. – In: Bashkov, S.O. (ed.): Epigenetic alteration of aqueous rocks under the influence of technogeneous factors. – Donetsk National Technical University Press, Donetsk, 8-11 (in Russian).

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Chapter 8

SHORT COMMUNICATION: INFLUENCE OF A COAL-FIRED POWER PLANT ON TERRESTRIAL BIOTA AT CANDIOTA, SOUTH OF BRAZIL A. M. Divan Junior1, P. L. Oliveira2, V. Schmidt3, J. S. Bernardo-Silva2, R. Hentschel1, B. Darski-Silva1, M. T. Raya-Rodriguez4 and S. M. Hartz4

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1

Laboratory of Plant Bioindication, Center of Ecology, Federal University of Rio Grande do Sul 2 Post-Graduation in Ecology, Federal University of Rio Grande do Sul 3 Department of Preventive Veterinary Medicine, Faculty of Veterinary, Federal University of Rio Grande do Sul 4 Department of Ecology, Institute of Biosciences, Federal University of Rio Grande do Sul

ABSTRACT A study was carried out to examine and compare the floristic composition of grassland sites exposed to fly ash emitted by a coal-fired power plant in Southern Brazil. The impact of the thermal plant on vicinity environment was evaluated for two years (2007–2009), by means of studying heavy metal content in wild plant species, richness of plant species, amphibians species and reptiles species and oral pathologies of sheep. In addition, the foliose lichen species, Heterodermia cf. obscurata, transplanted from an unpolluted site was exposed at the same sampling areas and their heavy metal content evaluated. The vegetation is composed primarily of grasslands with prominence of species Poaceae and Asteraceae plant families. In all, 165 plant species, mainly herbaceous ones, were found. Besides changes of floristic composition associated with seasonal changes, we observed a significant reduction in plant species richness in a site situated around 6 km from the power plant in the prevailing winds direction. Among the herbaceous plant

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A. M. Divan Junior, P. L. Oliveira, V. Schmidt et al. species evaluated, Elephantopus mollis (Asteraceae) presented the highest content of cadmium and zinc and Paspalum notatum (Poaceae) the highest content of lead. The sampling areas, located in the prevailing wind direction, presented the highest level of metal accumulation. Moreover, statistically significant negative correlations were found between zinc content of E. mollis and cadmium and zinc content of foliose lichen and the plant species richness of sampling areas around the thermal plant. A total of 17 amphibians composed mainly of Hylidae and Lepdodactylidae families and 12 reptiles species with prominence of Colubridae family were found. A direct association was not found between fly ash emitted by coal-fired on herpetofauna richness. Among the oral pathologies in sheep herds, there was a high prevalence of excessive tooth wear, both in young animals as well as the old. There was a positive association between the intense occurrence of tooth wear and the bioaccumulation factor of cadmium in E. mollis. These results put in evidence the influence of coal-fired power plant emissions on terrestrial biota.

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1. INTRODUCTION In Brazil, the electricity generation is based primarily on hydroelectric dams due to their large hydropower resources. The thermal power generation based on coal combustion is limited to a few power plants because the largest coal reserves in Brazil are located in Southern states far away from the most industrialized and economically productive Southeast region. The increasing demand for electrical power and the recent climate and hydrological changes caused Brazilian government experts to attempt to diversify the electricity generation using other ways of generation including coal-fired thermal power plants. The largest coal reserves in Brazil are concentrated in the region of Candiota in the southeast part of the state of Rio Grande do Sul, about 50 km from the Uruguayan border. The coal from open cast mines is used locally to generate electricity (446 MW) in the Thermoelectric Power President Médici—UTPM. Despite the use of electrostatic precipitator devices, important environmental impacts of coal combustion result from massive emissions of fly-ash. The deposition of fly-ash dust on vegetation interferes with light requirements for photosynthesis and with energy balance of leaves by impairment of stomatal transpiration mechanism (Gupta et al., 2002). Moreover flyashes are enriched with heavy metals, which may be dispersed in the environment together with the stack emissions (Sawidis et al., 2001). Heavy metals such as Copper and Zinc are essential nutrients for all living organisms but become toxic at higher concentrations. Other metals such as Cadmium and Lead do not appear to have any essential role in metabolism (Azevedo and Lea, 2005). The uptake and accumulation of these heavy metals by plants is a way of admitting highly toxic elements into the trophic chain that may further suffer biomagnifications, particularly the Cd. In attention to the exigency of Brazilian Federal Environmental Agency (IBAMA), the present study aimed to assess the impact of atmospheric emissions generated by the UTPM as expressed by bioaccumulation of Cd, Pb and Zn by wild plants, richness of plant, amphibian and reptile species, and prevalence of oral cavity’s pathologies in ovine herds in order to evaluate the impact of fly-ash deposition on these different aspects of the biota.

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2. MATERIAL AND METHODS 2.1. Description of Study Area The study area is situated in the Campanha do Sudoeste region (Rambo, 1956), whose landscape is topographically flat with low undulated hills on which the predominant vegetation is grasslands (in Portuguese, campos). This region is limited to the north for the plateau slope, to the south by the Uruguayan border, to the east by the Sudeste mountain range and to the west by the Uruguay River, covering 50,000 km2 or 18% of the total surface of Rio Grande do Sul. Cattle and sheep breeding and agriculture, together with environment factors (topography, water availability, soil types) exerts strength influence in the physiognomy of plant communities of this region. Among the types of campos, two are detached: Campo baixo—also called “campo limpo”, which is kept under regular grazing, with adequate cattle allotment (Boldrini and Miotto, 1987; Girardi-Deiro et. al., 1992). Botanically, in this type of grassland predominate species of the Poaceae family, and Campo alto—where there is a dense and continuous herbaceous stratum, with good covering of Poaceae species. Physiognomically, however, sub-shrub eudicotyledonous species stand out, with prominence for Asteraceae species. The regional climate is of the Cfa type with an annual precipitation of 1,460 mm, and mean annual temperature of 17.9 oC. The prevailing wind direction at any time of the year is from the northeast, with a mean velocity of 4 m s-1 (Migliavacca et al., 2004). The power plant consumes around 100,000 t month-1 of low caloric coal with high ash content (Pires and Querol, 2004) to generate electricity.

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2.2. Sampling Five sampling areas (SAs) were placed in the vicinity area around the thermal power plant (Figure 1). The reference site (SA 1) was situated about 17 km windward from the power plant. In these sites, samples of leaves or shoot of three herbaceous species were collected for evaluation of heavy metal content. The selected plant species, based in a previous study (Divan Junior et al., 2009), were the following: Baccharis trimera (Less.) D.C. (Asteraceae), Elephantopus mollis Kunth (Asteraceae) and Paspalum notatum Flügge (Poaceae). The field activities were performed every three months for two years (2007-2009). The plants sampled were obtained from open air plants distant from hindrance to free air circulation. The material collected, from at least eighteen different individuals of each species, was pooled to form one mixed sample, whose Cd, Pb and Zn contents were determined after removal of the particulate material deposited on the aerial part. For this purpose, the samples were washed in running tap water and flushed three times with deionized water. After the washing process, the samples were dried at 60oC in an oven until they reached constant weight.

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Figure 1. Map of the Candiota Region showing the location of the sampling areas (SA).

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Disks of foliose lichen thalli of Heterodermia cf. obscurata, transplanted from an unpolluted site, were exposed at each sampling area. These disks were fixed in holes of a woody plate attached to posts at a height of 1.5 m above the ground and exposed for about three months by two consecutive periods. At the end of each exposure period, the lichen material from each site was divided into three sub-samples per site and their metal content was evaluated. Soil samples were collected from all sampling areas. The soil was sampled from the first 20 cm in depth of topsoil and stored in polyethylene bags. Each soil sample (around 1.0 kg) was composed of 18 randomly subsamples mixed into one composite sample and brought to the Plant-Soil Laboratory Analysis of Agronomy Faculty (UFRGS) for analysis of metal contents.

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2.3. Survey Methods (Floristic, Herpetofauna and Oral Pathologies) In each sampling area, it was carried floristic surveys in 0.25 m2 sample plots alternately disposed throughout a transect. The same areas were inventoried at different seasons of the year during the period of studies. The amphibians sampling methods consisted at a presence record and the estimate number of calling individuals of each species. This record was made once every thirty minutes from 20:00 until 23:00 (sun time) at each wetland at each point per sampling. The surveys per pond were made by walking around the wetland to be sure that all species were detected. We obtained the abundance matrix of anurans by taking the greatest number of anuran calls recorded in the 30 min sampling intervals (Ávila and Ferreira, 2004). Reptiles sampling methods was conducted by direct visualization considering the thermoregulatory needs of the group. For many species of snakes and lizards, this method means the most efficiency to know relative abundance (Turner, 1977). Dental health was evaluated in 218 ewes from five sheep breeding farms each one localized in one sampling area. The oral cavity was examined and it was observed the prevalence of pigmentation of teeth, presence of yellow to brown discoloration of teeth, and excessive attrition, i.e., presence of irregularities of wear tooth (Jubb et al., 1985; Jones et al., 2000).

2.5. Analytical Methods The Zn content was determined by flame atomic absorption spectrometry in a Perkin Elmer 3300 and Cd and Pb contents by atomic absorption spectrometry in a Perkin Elmer SIMAA 6000 graphite oven. The analytic detection limits used were: 0.010 mg kg-1, 0.050 mg kg-1 and 0.500 mg kg-1 for Cd, Pb and Zn, respectively. In order to ensure the quality of data, the accuracy of the instrumental methods and the analytical procedures was checked by using Apple leaves (NIST SRM 1515) as reference material. The soil samples were air-dried and ground prior to analysis. Soil environmentally available heavy metals were extracted according to the EPA 3050 soil digestion method (U. S. Environmental Protection Agency, 1986) and determined using inductively coupled plasma

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A. M. Divan Junior, P. L. Oliveira, V. Schmidt et al.

optical emission spectroscopy (ICP/OES). The analytic detection limits were: 0.2 mg kg-1 for Cd and 2 mg kg-1 for both Pb and Zn.

2.6. Statistical Analyses We performed a cluster analysis (UPGMA) based on an Euclidean data-matrix to classify the EAs in relation to the anuran, reptiles and field plant species composition (Legendre and Legendre 1998). We evaluated the number of sharp clusters using bootstrap resampling (Pillar 1999). UPGMA and correlation analysis were performed using Statistica 7 (Statsoft, Inc., 2004). All other analyses were performed using MULTIV 2.3.20 (Pillar 2004).

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3. RESULTS AND DISCUSSION Among the herbaceous plant species evaluated, Elephantopus mollis (Asteraceae) presented the highest contents of cadmium and zinc and Paspalum notatum (Poaceae) the highest contents of lead. The SA 2, located in the prevailing wind direction, presented the highest levels of metal accumulation in E. mollis. The lichen’s metal content showed a similar pattern as herbaceous plant species, with the cadmium, lead and zinc contents of lichens exposed at SAs 2 and 5 showing significantly difference from these metal contents of lichens exposed at reference sampling area (Table 1). Paspalum notatum (Poaceae) stood out among all species found, as by remain in the plant communities during all seasons as by the highest absolute frequency (practically present in all parcels of the five SAs). Other high frequently species were Axonopus affinis (Poaceae), Bulbostylis capillaris (Cyperaceae), Desmodium incanum (Fabaceae), Eleusine tristachya (Poaceae), Piptochaetium montevidense (Poaceae) and Sporobolus indicus (Poaceae). In spring and/or summer periods there were many annual species very frequent and abundant, amongst which the Asteraceae Chevreulia sarmentosa and Soliva anthemifolia, beyond species of other families, as Dichondra sericea, Evolvolus sericeus, Facelis retusa and Oxalis spp. It is also worthy of mention the presence of sub-shrub species in grasslands tending to a woody vegetation of higher size, especially in SAs 3 and 4. Among these species can be detached the Asteraceae Baccharis coridifolia, Baccharis dracunculifolia, Eupatorium buniifolium, Senecio brasiliensis, beyond Psidium luridum (Myrtaceae), Sida rhombifolia (Malvaceae) and Solanum reflexum (Solanaceae). In addition, the SA 4 present the highest number of species, SAs 1, 3 and 5 showed intermediate number of species and SA 2, situated in the predominantly winds direction, and thus, exposed to airborne emissions from the coal-fired power plant, showed the lowest number of species (Table 1). Having in mind that SA 2 was the site most polluted by emissions from the power plant, as demonstrated by significant higher metals content of E. mollis at SA 2 as compared to SA 1 (Table 1), we suppose that these changes in richness can be attributed to the airborne emissions.

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Table 1. Plant, amphibian, reptile species richness, oral pathologies of sheeps, and cadmium, lead and zinc content of some selected wild plant species of five sampling areas around a coal-fired power plant

Plant species richness Total species Richness m-2

Amphibian species richness Reptile species richness Oral pathologies of sheeps (%) Excessive tooth wear Abnormal pigmentation of teeth Heavy metal contents (mg kg-1) Elephantopus mollis Cadmium

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Lead

Zinc

Baccharis trimera Cadmium

Lead

Zinc

Paspalum notatum Cadmium

Lead

Sampling Areas SA 3 SA 4

SA 1

SA 2

108 5.7 ± 0.4

100 5.1 ± 0.4

133 7.1 ± 0.7

95 4.5 ± 0.4

12

90 4.4 ± 0.4 * 12

ns 11

* 15

* 13

2

6

1

6

4

2.5

32.1

12.1

7.4

9.5

37.5

75.0

45.5

85.2

50.8

0.285 ± 0.049

0.831 ± 0.195 * 3.06 ± 1.54 124 ± 23 *

0.355 ± 0.102

0.637 ± 0.126

0.748 ± 0.165

ns 1.07 ± 0.12

ns 1.11 ± 0.22

* 2.16 ± 0.64

65.9 ± 7.4

85.4 ± 8.7

118 ± 8

ns

ns

*

0.125 ± 0.019

0.188 ± 0.028

0.123 ± 0.026

0.777 ± 0.192

1.06 ± 0.43

0.848 ± 0.415

46.0 ± 4.2

72.4 ± 8.1

50.1 ± 9.3

-

*

-

SA 3

SA 4

SA 5

0.069 ± 0.012

0.111 ± 0.038

0.084 ± 0.029

1.37 ± 0.24

1.55 ± 0.14

1.54 ± 0.35

1.38 ± 0.45 59.4 ± 3.6 0.167 ± 0.024

0.144 ± 0.024 1.34 ± 0.911 0.62 ± 0.378 52.0 ± 59.5 5.2 ± 4.7 Sampling Areas SA 1 SA 2

0.085 ± 0.018 1.69 ± 0.23

0.046 ± 0.005 1.86 ± 0.34

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SA 5

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A. M. Divan Junior, P. L. Oliveira, V. Schmidt et al. Table 1 (Continued) Zinc

Heterodermia obscurata (lichen) Cadmium

Lead

Zinc

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‡

43.0 ± 5.0 -

49.6 ± 4.6 -

0.529 ± 0.189

0.744 ± 0.169 * 2.74 ± 0.68 * 94.4 ± 6.2 *

4.28 ± 1.61 67.9 ± 1.7 -

38.8 ± 4.4

62.2 ± 9.9

60.1 ± 9.5

-

*

-

0.689 ± 0.180

0.148‡

1.21 ± 0.44

ns 4.61 ± 1.21

0.724‡

* 5.78 ± 1.10

ns 76.1 ± 3.8

49.9‡

* 87.2 ± 4.4

ns

-

*

= insufficient sample size to replicate analysis. Means with * underneath differ significantly from the mean of reference sampling area (P < 0.05). (ns = not significant)

Since a long time it is known that air pollution can alter species composition on community level (Smith, 1974; Rosenberg et al., 1979; Murray, 1981). According to Smith (1974), the relationship between air pollution and plant communities can be classified in three classes. The Smith’s Class I is distinguished by low pollution load and inconspicuous or minimal physiological changes because the vegetation acts only as a sink for air pollutants. The Class II occurs if the air pollution load increases to intermediate levels; then, tolerance threshold of air pollution sensitive species can be exceeded causing impairment to individual members of the biota, such as reduced growth and reproduction or increased morbidity. These air pollution responses can lead to changes in species composition. Finally, Smith’s Class III, characterized by higher air pollution load, may include acute mortality of plant individuals and simplification and loss of ecosystem services. From a conservationist perspective, studies dealing with the sink function and the subtle impact of air pollutants on plant communities are much more important than acute damage approach (Smith, 1974). Based on Smith’s classes system of air pollution impact, our data give support only for the occurrence of classes I and II, i.e., the plant cover functioned as a sink for atmospheric emissions and occurred only minor changes on species composition of plant communities. Air pollutants are capable of change the composition of plant communities by increasing evenness and reducing the richness of herbaceous and woody species as showed by Narayan et al. (1994). In contrast to most of the woody species, the definition of individuals in rhizomatous stemmed herbaceous plants is a very difficult task (Rosenberg et al., 1979), so, we don’t attempt to estimate the diversity of herbaceous cover. We survey 17 species of amphibians and 12 of reptiles. Amphibians are grouped in six families (Cycloramphidae, Hylidae, Leiuperidae, Leptodactylidae, Microhylidae and Ranidae) and reptiles in seven families (Amphisbaenidae, Chelidae, Colubridae, Emydidae, Gymnophtalmidae, Scincidae, Teiidae and Viperidae). Apparently, it was not found directly association between fly ash emitted by coal-fired on herpetofauna richness.

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Abnormal discoloration (yellow to brown color) of ruminant teeth is common and is caused by impregnation of mineral salts with chlorophyll and porphyrin pigments from herbage (Barker and Van Dreumel, 1985). Among the animals from SA 1 the abnormal discoloration prevalence was 37.5% (Table 1), among the sheeps from the other farms the prevalence of abnormal discoloration was higher (45.5% to 85.2%). Yellow to brown discoloration of teeth due to deposition of tetracycline antibiotics in mineralizing dentin, enamel, and probably cementum, occurs in all species (Barker and Van Dreumel, 1985), but there are no historic of tetracycline antibiotic use in these farms. Zinc and cadmium accumulate in liver and kidneys, but showed no signs or specific lesions associated (Jones et al., 2000), even so Cd is considered one of the heavy metals most toxic to humans and animals, being implicated in several pathological signs. Zinc, Cadmium and copper contents were measured in viscera and not identified themselves or significant levels of toxicity (data not shown), maybe, because less than 1 percent of dietary Cd is absorbed by ruminants. Lead is the most common cause of toxicoses in domestic livestock. Elevated dietary calcium, phosphorus, iron, zinc, fat, and protein decrease the absorption and retention of Pb (National Research Council, 2001). Chronic exposure to low levels of Pb is not associated with clinical symptoms in animals because bones sequester Pb and release it gradually to the blood for excretion (National Research Council, 2001). Acute intoxication with Pb causes blindness and irritability (Radostis et al, 1994). Although the low level of Pb in sheep’s bones in the sampling area (data no shown), we observed blinded cattle in the SA 5, near the calcareous mine. In carboniferous limestone areas where Pb is often mined, there are often fluvialdeposited sediments downstream. Disturbed soils and spoil heaps from historical mining activity present a particular hazard to the grazing animal, since the concentrations of metals may be high in both topsoil and herbage (Wilkinson et al., 2003). The main factors affecting the accumulation of potentially-toxic metals by grazing animals are the presence of the metal, its concentration in herbage and at the soil surface, and the duration of exposure to the contaminated pasture and soil. In addition, the elapsed time between the contamination of the pasture and grazing, the quantity of soil ingested together with herbage, the mechanism of absorption of the metal into blood and the presence or absence of antagonistic metals can interact to influence the rate and extent of accumulation of heavy metals in edible body tissues (Wilkinson et al., 2003). Contaminants may be deposited on the soil surface via aerial deposition (e.g., radionuclide, vehicle and industrial emissions), during flooding and by direct application to the ground (e.g., metals in fertilizers and in sewage sludge). The grazing animal can ingest the metals either by consuming herbage that is internally or externally contaminated, or by consuming contaminated soil (Wilkinson et al., 2003). Although tooth wear is most evident in herbivores, and irregularities of wear are perhaps the most common tooth abnormalities (Jubb et al., 1985), tooth wear is often suggested as an important factor limiting the life span of animals (Martin et al., 2008). Soil ingestion results in abrasion during prehension and is generally accepted as the final insult required causing tooth wear. Factors such as soil type, stocking rate, grazing management, and pasture composition all influence the amount of soil ingestion (NADIS, 2003) and consequently the occurrence of tooth wear. The precise cause of excessive tooth wear is complex and has defied clarification. The appearance of dental disease has been loosely associated with pasture improvement through

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fertilizer application, the introduction of modern pasture species and close intensive grazing (NADIS, 2003). Some phosphate fertilizers can also contain significant amounts of cadmium (National Research Council, 2001). Cluster analysis indicated the formation of two sharp groups of EAs described by amphibians species (P = 0.47), reptiles (P = 0.35), field plant species (P = 0.32) and total species assemblages (P = 0.22). The sites SA 3 and SA 5 were joined into the same cluster for all cluster analysis while the SA 2 represented isolated group for all taxa excepted amphibians (Figure 2). Moreover, we found statistically significant negative correlations between zinc content of E. mollis and cadmium and zinc content of foliose lichen and the plant species richness of sampling areas around the thermal plant, and a positive association between the occurrence of tooth wear and the bioaccumulation factor of cadmium in E. mollis (Figure 3). All these results together allow us to suppose that they are related to atmospheric emissions from the power plant.

4. CONCLUSION

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The use of different approaches of analysis, such as cluster analysis of anuran, reptiles and field plant species richness, and correlation analysis between metal content in plants and plants species richness or oral pathologies of sheep allow us to conclude that the coal-fired plant emissions may have a significant impact on different aspects of the surrounding terrestrial biota from the power plant.

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Figure 2. Clusters analysis to Amphibian (A), Reptile (B), Plant (C) and all taxa (D).

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7.5

7.5

B Plant species richness (Richness m-2)

Plant species richness (Richness m-2)

A 7.0 6.5 6.0 5.5 5.0 4.5

r = -0.897 P = 0.039

4.0

7.0

6.5

6.0

5.5

5.0

r = -0.987 P = 0.002

4.5

4.0

3.5 0.0

0.2

0.4

0.6

0.8

1.0

40

1.2

50

60

80

90

100

10

C

30

25

20

15

10

r = 0.942 P = 0.017

5

Plant species richness (Richness m-2)

35

Excessive tooth wear

70

Lichen zinc content (mg kg-1)

Lichen cadmium content (mg kg-1)

D 9 8

r = -0.437 P = 0.012

7 6 5 4 3 2

0 0.6

0.8

1.0

1.2

1.4

1.6

Bioaccumulation factor of cadmium in E. mollis

1.8

20

40

60

80

100

120

140

160

Cadmium content in E. mollis (mg kg-1)

180

200

 

Figure 3. Correlation analysis between plant species richness and lichen cadmium content (A), plant species richness and lichen zinc content (B), excessive tooth wear and bioaccumulation of cadmium in E. mollis (C) and plant species richness and cadmium in E. mollis (D).

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REFFERENCESS Ávila, R. W. & Ferreira, V. L. (2004). Richness Á R of sppecies and divversity of vocaalization of anurans in an urban areaa of Corumbá,, Mato Grossoo do Sul, Brazzil. Revista Brrasileira de 2 887-892. Zoologia, 21, A Azevedo, R. A. A & Lea, P. J. (2005). Toxic metals in i plants. Braazilian Journaal of Plant Physiologyy, 17, 1-1. B Boldrini, I. I. & Miotto, S. T. T S. (1987). Levantamento L o fitossociológgico de um caampo limpo da Estação o Experimentaal Agronômicca, UFRGS, Guaíba, G RS. 1 etapa. Actaa Botanica. Brasilica, 1, 1 49-56. D Divan Junior, A. A M., Oliveiira, P. L., Perrry, C. T., Atzz, V. L., Azzzarini-Rostirola, L. N. & Raya-Rodrriguez, M. T. T (2009). Using U wild plant p species as indicatorrs for the accumulatiion of emisssions from a thermal pow wer plant, Candiota, C Souuth Brazil. Ecologicall Indicators, 9,, 1156-1162. G Girardi-Deiro, A. M., Gonçalves, J. O. N. N & Gonzagaa, S. S. (19922). Campos naturais nos c diferentes tipos de sollos no Municcípio de Baggé-RS. 2. Fissionomia e composição I Sériie Botânica. 422, 55-79. florística. Iheringia, G Gupta, D. K., Rai, R U. N., Triipathi, R. D. & Inouhe, M. (2002). Impaccts of fly-ash on soil and plant respo onses. Journal of Plant Reseearch, 115, 401-409. Joones, T. C., Hunt, H R. D. & King, N. W. (2000). Patologia Veterináária. (6 ed.). São Paulo: Manole. Juubb, K. V. F., Kennedy, P. C. & Palmer, N. (1985). Pathology of doomestic animaals. (3 ed.). San Diego:: Academic Prress. Legendre, P. & Legendre, L. (1998). Numeerical ecologyy. (2 ed.). Amssterdam: Elsevvier. M Martin, O., Cllauss, M., Sttreich, W. J. & Hatt, J. M. M (2008). Irrregular toothh wear and longevity in i captive wildd ruminants: A pilot surveyy of necropsyy reports. Jourrnal of Zoo and Wildliffe Medicine, 39, 3 69-75. M Migliavacca, D. D M., Teixeiraa, E. C., Machhado, A. C. M. M & Pires, M.. R. (2005). Composição C química daa precipitação atmosférica no n sul do Brasiil – Estudo preliminar. Quím mica Nova, 28, 371-379. M Murray, F. (1981). Effects of o fluorides onn plant comm munities aroundd an aluminiuum smelter. Environmeental Pollutionn, 24, 45-56. N NADIS – Natiional Animal Diseases Infoormation Servvice, (2003). Sheep S Diseasees Focus – Dental Diiseases. Avaiilable in: > N Narayan, D., Agrawal, M., M Pandey, J. J & Singh, J. (1994). Changes in vegetation characteristics downwinnd of an alumiinium factory in India. Annnals of Botanyy, 73, 557565. N National Reseaarch Council, (U.S.) ( – Subcoommittee on Dairy D Cattle Nutrition N (20011). Nutrient requiremen nt of dairy cattle. (7 ed.). Saan Diego: Acaademic Press. Piillar, V. D. (1999). The boootstrapped orddination reexam mined. Journaal of Vegetatioon Science, 10, 895-902. Piillar, V. D. (2 2007). MULTIV; multivariaate exploratorry analysis, raandomization testing t and bootstrap resampling; r u user’s guide, v. v 2.5. Departtamento de Ecologia, E UFR RGS, Porto Alegre (sofftware and maanual availablee at http://ecoqqua.ecologia.uufrgs.br/).

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Pires, M. & Querol, X. (2004). Characterization of Candiota (South Brazil) coal and combustion by-product. Coal Geology, 60, 57-72. Radostis, O. M., Blood, D. C. & Gay, C. C. (1994). Veterinary Medicine. (8 ed.). London: W. B. Saunders, 1994. Rambo, B. (1956). A fisionomia do Rio Grande do Sul. Porto Alegre: Editora Selbach. Rosenberg, C. R., Hutnik, R. J. & Davis, D. D. (1979). Forest composition at varying distances from a coal-burning power plant. Environmental Pollution, 19, 307-317. Sawidis, T., Chettri, M. K., Papaioannou, A., Zachariadis, G. & Stratis, J. (2001). A study of metal distribution from lignite fuels using trees as biological monitors. Ecotoxicology and Environmental Safety, 48, 27-35. Smith, W. H. (1974). Air pollution – Effects on the structure and function of the temperate forest ecosystem. Environmental Pollution, 6, 111-129. StatSoft, Inc. (2004). STATISTICA, data analysis software system, version 7. StatSoft, Tulsa (http://www.statsoft.com/). Turner, F. B. (1977). The dynamics of populations of squamates, crocodilians and rhynchocephalians. In C. Gans, & D. W. Tinkle, (Eds.) Biology of Reptilia. Vol 7. Ecology and Behavior. New York: Academic Press, 1977. 157-264. U. S. Environmental Protection Agency, (1986). Method 3050b: Acid Digestion of Sediments, Sludges, and Soils. Rev. 2. Avaliable in: (http://www.epa.gov/osw/hazard/ testmethods/ sw846/pdfs/3050b.pdf) Access in: October 8, 2009. Wilkinson, J. M., Hill, J. & Phillips, C. J. C. (2003). The accumulation of potentially-toxic metals by grazing ruminants. Proceedings of the Nutrition Society, 62, 267-277.

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INDEX

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A  abatement, 73, 86 access, 134 acetic acid, 164 acid, 86, 96, 231 activation energy, 8, 9, 10, 16, 17, 19, 21, 23, 24, 88 active site, 15, 17, 23, 161 ADC, 110 adjustment, 67 adsorption, 14, 20, 200 age, 211, 212 air pollutants, 242 alcohol, 212, 213 alcohol consumption, 212, 213 allele, 215, 216 aluminium, 247 aluminum, 175, 177 ambient air, 86 ammonia, 95, 96, 102, 103, 104, 106, 107, 108, 110, 123, 124, 129, 130, 133, 135, 136, 140, 152, 155, 160 ammonium, 97 amorphous phases, viii, 171, 199, 200 amphibia, 236, 241 amphibians, ix, x, 235, 236, 239, 242, 244 anatase, 175, 176 anisotropy, 17 annealing, vii, 1, 17, 20, 21, 23, 34 ANOVA, 213 applications, vii, 1, 21, 91, 96, 101, 134, 164 Argentina, ix, 205, 217 aromatic rings, 5, 11 arsenic, ix, 201, 205, 206, 207, 209, 212, 213, 215, 217, 218, 230 arsenic poisoning, ix, 205, 209, 218 Asian countries, 217 assessment, 109, 138

atoms, 11, 47, 89, 177 Australia, 229, 230, 232 availability, 29, 92, 100, 102, 103, 121, 135, 136, 164, 237 avoidance, viii, 75, 221

B  background, 221 bacteria, 231 Bangladesh, ix, 205, 218 banks, 172 barium, 223, 230 basic research, 5 behavior, viii, 13, 21, 171, 172, 177, 199, 201 behaviors, 21, 177 Beijing, 1 benzene, 3, 5, 97 bioaccumulation, x, 236, 244, 246 biomass, viii, 13, 53, 74, 85, 87, 97, 98, 100, 101, 109, 138, 145, 147, 164 blood, 86, 214, 243 boilers, 27, 40, 54, 58, 63, 65, 66, 72, 73, 74, 78, 79, 86, 87, 91, 95, 96, 97, 101, 103, 163, 172, 202 Boltzmann constant, 22 bonding, 90 bonds, 11 branching, 103 Brazil, ix, 235, 236, 247, 248 breeding, 237, 239 brothers, 213 browser, 50 bun, 209 burn, 16, 59, 70, 71, 100, 106, 119, 121, 140, 231 burning, ix, 13, 14, 24, 25, 41, 42, 43, 44, 45, 46, 47, 50, 52, 54, 59, 70, 71, 75, 76, 77, 87, 96, 185, 202, 206, 209, 218, 219, 220, 221, 222, 223, 224, 227, 230, 231, 232, 233, 248

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Index

burnout, viii, 13, 21, 34, 43, 45, 46, 47, 48, 74, 85, 91, 100, 101, 103, 106, 107, 108, 123, 134, 135, 136, 138, 140, 141, 144, 152, 156, 158, 164, 185, 190

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C  cadmium, x, 236, 240, 241, 243, 244, 246 calcium, 101, 152, 159, 172, 175, 176, 182, 183, 197, 199, 202, 243 carbon, viii, 3, 4, 5, 10, 12, 13, 14, 15, 17, 19, 21, 22, 24, 25, 31, 34, 47, 52, 53, 65, 69, 70, 72, 73, 74, 77, 79, 85, 86, 100, 101, 102, 103, 106, 113, 114, 119, 120, 121, 134, 138, 139, 144, 147, 164, 221, 231 carbon dioxide, 13, 74, 231 carbon monoxide, 4, 13, 72, 86 carcinogen, ix, 205 carrier, 4, 58, 59, 65, 66, 68, 69, 70, 72, 104, 106, 107, 108 case study, 71, 72 catalyst, 33, 95, 96 catalytic activity, 17 catalytic effect, 19 cattle, 98, 237, 243, 247 causality, 209 CCR, 108, 138, 143, 144, 145 chemical properties, 18 chemical reactions, 15, 16, 40, 43 chemical reactivity, 16 chemiluminescence, 110 Chile, ix, 205, 217 China, viii, 1, 36, 171, 172, 173, 200, 201, 205, 206, 207, 216, 217, 218 chlorine, 138 chlorophyll, 243 chromatography, 111 chronic diseases, 212 CIA, 103 classification, 164, 187, 203 clinical symptoms, 243 cluster analysis, 240, 244 clusters, 3, 10, 11, 13, 240 CO2, viii, 3, 4, 5, 11, 13, 14, 15, 20, 21, 22, 29, 40, 44, 45, 52, 53, 57, 58, 66, 71, 72, 74, 75, 81, 82, 83, 84, 95, 98, 100, 103, 105, 110 coal dust, 74, 84 cobalt, 223, 225 cohort, 210 coke, 3, 74, 83 color, 206, 224, 225, 229, 243 combined effect, 10 combustion environment, 27 combustion processes, 91

community, 207, 242 competition, 132, 136, 155 components, viii, 11, 40, 41, 42, 76, 89, 99, 119, 171, 172, 175, 187, 189, 231 composition, viii, ix, x, 2, 9, 47, 73, 75, 95, 106, 171, 172, 173, 175, 177, 179, 185, 186, 187, 188, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 221, 223, 225, 230, 231, 232, 235, 240, 242, 243, 248 compounds, ix, 5, 30, 89, 90, 91, 99, 160, 205 condensation, 2, 3 conductivity, 22, 172, 198 configuration, 104, 110, 131, 133, 136, 141, 149, 152, 164 consanguinity, 212, 217 consumption, 10, 40, 113, 149, 152, 160, 212 consumption rates, 152 contamination, 243 control, viii, 40, 71, 85, 86, 87, 91, 92, 95, 96, 97, 98, 99, 101, 103, 109, 123, 149, 160, 163, 164, 170, 233 control measures, 91, 163, 164 conversion, 12, 27, 31, 33, 34, 73, 86, 89, 91, 104, 106, 113, 114, 120, 121, 146, 172, 199 cooking, 206 cooling, 43, 222 copper, 54, 243 corn, 207, 213, 214 correlation, 120, 134, 164, 240, 244 correlation analysis, 120, 240, 244 correlation coefficient, 120 correlations, x, 21, 236, 244 corrosion, 40, 201 cost-benefit analysis, 164 critical value, 100 crystalline, 193, 202, 203 crystallites, 6 crystallization, 177 crystals, viii, 171, 172, 190, 192, 193, 195, 197, 198, 199, 200 cyanide, 90, 134

D  data analysis, 248 database, 64, 81 decomposition, 2, 8, 11, 99, 100, 107, 155, 178, 179, 180, 181, 182, 183, 189, 231 decomposition temperature, 178, 181, 183 definition, 202, 242 degree of crystallinity, 189 dehydration, 177, 182 demographic factors, 212 density, 16, 18, 21, 23, 24, 25, 108, 222 dentin, 243

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Index Department of Energy, 202 depolymerization, 9, 10 deposition, viii, 171, 172, 175, 185, 186, 187, 189, 190, 198, 200, 201, 202, 203, 236, 243 deposits, viii, ix, 171, 172, 186, 187, 188, 189, 190, 191, 192, 193, 197, 198, 199, 200, 201, 202, 203, 220, 221, 222, 231, 233, 234 deprivation, 94 desorption, 14, 15, 20, 31 destruction, 86, 118, 119, 129, 134, 138, 151, 155 destruction reaction, 119 detection, 239, 240 detoxification, 215 deviation, 10, 24, 213, 216 diffusion, 3, 15, 16, 18, 21, 118, 172 distribution, 5, 9, 10, 13, 14, 18, 20, 21, 23, 24, 27, 28, 43, 48, 53, 64, 67, 68, 98, 116, 172, 186, 189, 190, 217, 248 diversity, 11, 242, 247 doping, 106, 124 dosage, 214 drawing, 43, 55, 111 drinking water, ix, 205, 207, 212, 213, 217, 218 drying, 206 DSC, 177, 179, 180, 181, 182, 183, 184, 202 duration, 54, 210, 213, 214, 243 dynamics, 50, 78, 82, 248

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E  economics, 98, 164 ecosystem, 242 elucidation, viii, 171 emission, viii, 27, 40, 52, 53, 65, 68, 69, 70, 71, 73, 85, 87, 91, 100, 102, 119, 123, 124, 141, 142, 143, 144, 145, 146, 149, 152, 155, 159, 160, 164, 201, 231, 240 endothermic, 14, 177, 179, 180, 181, 182 energy, viii, 2, 10, 12, 19, 22, 24, 32, 39, 40, 54, 62, 85, 90, 164, 171, 172, 173, 231, 236 energy consumption, 40 engineering, 53, 98, 160 England, 169 environment, ix, 4, 22, 25, 40, 74, 84, 86, 110, 111, 116, 118, 144, 147, 165, 172, 183, 230, 235, 236, 237 environmental impact, 236 Environmental Protection Agency, 239, 248 EPA, 169, 170, 239 equilibrium, 72, 88, 89, 200, 202 equipment, 54, 55, 59, 61, 62, 71, 92 erosion, 202 estimating, 54, 81, 82 ethnic groups, 210, 213, 217

ethnic minority, 207, 211 ethnicity, 208, 209, 212, 217 etiology, 218 EU, 86 Europe, 74 European Parliament, 165 evolution, 10, 25, 120, 177, 198 examinations, 53 excretion, 243 experimental condition, 7, 43, 44, 45, 57, 77 exposure, ix, 205, 207, 212, 213, 216, 217, 239, 243 extinction, 230, 234 extraction, ix, 219 extrapolation, 136

F  family, x, 212, 236, 237 family history, 212 family members, 213 farmers, 206, 207, 208 fertilizers, 243, 244 field emission scanning electron microscopy, viii, 5, 171, 173 film thickness, 25 flame, viii, 4, 14, 27, 40, 44, 45, 48, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66, 68, 69, 70, 71, 72, 73, 74, 80, 81, 83, 84, 85, 88, 89, 92, 95, 98, 105, 106, 110, 123, 124, 134, 190, 200, 239 flame propagation, 53, 54, 55, 56, 57, 58, 59, 65, 66, 68, 69, 70, 71, 74, 80, 81, 83, 84 flammability, 53, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 71, 80, 81, 82 flammability limit, 53, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 71, 80, 81, 82 flexibility, 149, 160 flooding, 243 flue gas, 40, 45, 67, 86, 91, 92, 95, 110, 130, 138, 144, 164 fluid, 50, 78, 82, 110, 149, 152, 172 fluidized bed, 4, 27, 73 fluorescence, viii, 171, 173, 221, 233 focusing, 86 food, 206, 208, 212 forest ecosystem, 248 formula, 71 fossil, 2, 40, 86 fouling, 172, 201, 202 fractures, 222, 223 fragments, 2, 3, 100, 101, 132, 135, 155, 224, 226, 227, 228 free radicals, 2, 3, 4 frequency distribution, 17, 24

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Index

fusion, 172, 177, 183, 185, 186, 187, 200, 222, 223, 225, 227, 229, 230

G  gallium, ix, 219, 230 gas diffusion, 15, 16 gases, 3, 5, 11, 12, 31, 92, 97, 98, 101, 110, 152, 160, 230, 231 gasification, 9, 13, 21, 30, 42, 44, 45, 46, 49, 50, 71, 76, 77, 98, 232 gender, 212 gene, 217, 218 generation, viii, 22, 28, 40, 50, 85, 151, 156, 236 genotype, 216 geology, ix, 220, 221, 226 Germany, 96, 205, 221 glutathione, 215, 218 government, 236 grading, 210, 212 grains, 200 graphite, 239 grasslands, x, 235, 237, 240 gravimetric analysis, 140 gravity, viii, 110, 171 grazing, 237, 243, 244, 248 groups, 2, 3, 9, 28, 207, 211, 216, 244 growth, 6, 186, 189, 190, 193, 202, 233, 242 Guangdong, 216, 218

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H  hair, 207, 209, 213, 214, 218 Hawaii, 169 hazards, 217 health, 86, 87, 217, 239 heat, 4, 14, 22, 28, 40, 50, 54, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73, 78, 81, 172, 189, 198, 200, 201, 202, 221, 222, 231 heat capacity, 22 heat loss, 22, 54, 59, 60, 61, 62, 63, 64, 68, 70, 71, 72, 81 heat transfer, 4, 22, 40, 50, 60, 78, 172, 189, 198 heating, vii, 1, 3, 4, 5, 6, 7, 9, 11, 19, 28, 29, 34, 43, 54, 61, 72, 98, 110, 116, 118, 119, 140, 206, 222, 231 heating rate, vii, 1, 3, 4, 5, 6, 7, 9, 11, 19, 28, 29, 34, 43, 54, 61, 72, 110, 116, 118, 119, 140 heavy metals, 236, 239, 243 height, 106, 108, 239 heredity, 215 heterogeneity, 21, 217 hexane, 97 high school, 213

Hmong, 206, 207, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218 host, 221 housing, 208 humidity, 222 hybrid, 227 hydrocarbons, 3, 11, 138, 140, 233 hydrogen, 2, 3, 4, 10, 17, 47, 49, 50, 72, 89, 90, 230 hydrogen bonds, 2, 3 hydrogen cyanide, 89, 90 hydroxyl, 9, 177 hydroxyl groups, 177 hypothesis, 231

I  image, 42, 76, 193, 194, 195, 196, 197, 198 images, 191, 192 implementation, 163 impregnation, 229, 243 impurities, 18, 20, 172, 193, 197, 201 incidence, 211, 218 income, 212, 213 India, ix, 205, 232, 247 indication, 114, 132 indicators, 10, 247 industrial emissions, 243 industry, viii, ix, 21, 85, 219 ingest, 243 ingestion, 207, 243 inhibition, 21, 24 inhomogeneity, 9 initial state, 24 initiation, 101, 190 integration, 18 interaction, viii, 29, 97, 135, 171, 189, 190, 200, 221, 230 interactions, 29, 164, 216 intoxication, 243 intrinsic value, 16 ions, 177 iron, viii, 171, 172, 175, 179, 182, 183, 189, 190, 195, 197, 200, 202, 203, 222, 223, 224, 229, 231, 243 irritability, 243 isotope, 15

J  Japan, 39, 73, 75, 83, 96, 97

K  kinetic model, 8, 30, 89, 98 kinetic parameters, 9, 11, 16, 19, 20, 31, 34

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Index kinetics, 3, 10, 16, 20, 22

L  laminar, 54, 89 landscape, 237 languages, 208 lesions, 209, 210, 213, 218, 243 lichen, x, 235, 236, 239, 240, 242, 244, 246 life span, 243 likelihood, 161 limestone, 243 limitation, 16, 165 line, 6, 61, 62, 63, 105, 110, 111, 120, 124, 131, 136, 229, 230 liver, 86, 243 livestock, 243 locus, 216, 217 logging, 110 longevity, 247 low temperatures, 20, 33, 88, 91, 201, 202 LTA, 181 LTD, 108 Luo, 35, 168, 218

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M  magnesium, 101, 110, 149, 183, 233 majority, 11, 105, 144, 207, 210 males, 210, 211 management, 243 manufacturer, 92 manure, 98 mapping, 110 market, 164 marriage, 208 Mars, 225, 226 mass loss, 105, 116, 119 matrix, 3, 17, 239, 240 maturation, 17, 190 measures, 91 meat, 212 melt, viii, 171, 189 melting, ix, 171, 172, 177, 180, 183, 184, 198, 199, 200 melting temperature, 183, 200 melts, 186, 232 men, 211, 212 metabolism, 236 metals, ix, 164, 219, 236, 240, 243, 247 Mexico, ix, 205 microgravity, 53, 57, 74, 84 microscope, 110, 221 microscopy, 190

microstructure, viii, 171, 173, 201 microstructures, 186 mining, 221, 233, 243 mixing, 86, 92, 94, 98, 107, 110, 134, 152, 155, 158 model, 5, 8, 9, 10, 11, 12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 33, 34, 46, 48, 50, 51, 52, 54, 59, 63, 64, 68, 71, 73, 77, 78, 79, 80, 81, 82, 83, 98, 120, 122 modeling, 11, 30, 33, 34, 73, 83, 97, 98 modelling, 98, 100, 202, 203 models, vii, 1, 8, 9, 11, 13, 17, 18, 20, 21, 33, 34, 40, 64, 72, 116 modulus, 23 moisture, 231 molar ratios, 10 mole, 48, 72, 110 molecular weight, 10, 11, 18, 22 molecular weight distribution, 10 molecules, 2, 3 Mongolia, ix, 205 Montana, 32 Moon, 225, 233 morbidity, 242 morphology, 5, 197 mortality, 217, 242 Moscow, 232, 233 mutant, 215, 216

N  Na2SO4, 178 naphthalene, 3 National Ambient Air Quality Standards, 86, 165 National Research Council, 243, 244, 247 nationality, 218 natural gas, 96, 97, 98, 100, 101, 104, 105, 106, 123, 128, 164 Nd, 221, 230 Netherlands, 165 network, 2, 9, 11, 13, 34 New South Wales, 233 nitric oxide, 89, 90, 91, 97, 98, 99, 118 nitrogen, viii, 11, 26, 27, 28, 30, 31, 33, 34, 40, 46, 73, 85, 86, 87, 89, 90, 91, 95, 98, 99, 100, 102, 104, 105, 106, 107, 108, 111, 113, 115, 116, 117, 118, 119, 120, 121, 123, 124, 131, 134, 135, 136, 138, 144, 154, 164 nitrogen compounds, 90, 91, 119 nitrogen dioxide, 86, 87 nitrogen oxides, 86, 87, 99 nitrous oxide, 86 normal distribution, 24 nuclear magnetic resonance, 11 numerical analysis, 42, 47, 50, 65

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Index

numerical computations, 40

O  observations, 5, 172, 202 oil, 87, 92, 97, 100, 101 oils, viii, 85, 87 optical fiber, 54 optical microscopy, viii, 171, 173, 193 optimization, 97, 149, 151, 157 oral cavity, 236, 239 order, 4, 5, 9, 14, 17, 20, 21, 31, 32, 34, 43, 50, 55, 59, 65, 66, 67, 68, 87, 95, 102, 103, 108, 109, 119, 124, 135, 140, 147, 161, 186, 236, 239 oxidation, viii, ix, 2, 17, 19, 21, 23, 25, 27, 30, 31, 42, 45, 46, 49, 50, 71, 76, 77, 85, 90, 100, 102, 103, 131, 135, 136, 151, 156, 158, 181, 183, 184, 199, 219, 229, 231 oxidation rate, 103 oxides, viii, 17, 86, 87, 99, 119, 171, 182, 183, 187, 197, 227 oxygen, viii, 2, 3, 10, 11, 14, 15, 16, 21, 23, 25, 31, 40, 45, 52, 53, 57, 58, 64, 66, 67, 70, 71, 72, 79, 81, 85, 89, 91, 92, 94, 95, 100, 107, 108, 121, 124, 135, 136, 137, 138, 152, 153, 155, 158, 225, 231 ozone, 86

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P  Pacific, 169, 226 parallel, 6 parameter, 24, 25, 26, 53, 108, 131, 132 parameters, 10, 11, 12, 13, 16, 19, 23, 53, 65, 74, 108, 120, 121, 134, 144, 152, 160, 163, 212 particle mass, 22 particles, ix, 4, 7, 11, 15, 20, 26, 41, 43, 47, 53, 54, 59, 76, 82, 98, 104, 105, 110, 113, 116, 118, 121, 144, 158, 161, 162, 171, 186, 187, 189, 192, 193, 197, 198, 200 pathways, 27, 30 Pearl River Delta, 216 perchlorate, 110 performance, viii, 40, 41, 50, 52, 53, 65, 66, 67, 68, 70, 75, 78, 101, 109, 111, 116, 123, 124, 126, 133, 134, 136, 138, 140, 142, 143, 144, 147, 149, 151, 152, 158, 160, 164, 201, 202 petroleum, 74, 83, 87 phase diagram, 200 phase transformation, 17, 179 phenotype, 216, 218 phosphorus, 243 photosynthesis, 236 physical properties, 2

pigmentation, 209, 239, 241 plants, 40, 86, 87, 96, 104, 165, 236, 237, 242, 244, 247 plastics, viii, 85 platform, ix, 220, 221, 222, 233 pollutants, vii, 1, 13, 26, 164, 165, 242 pollution, vii, 1, 214, 218, 242, 248 polymorphism, 215, 216, 218 polymorphisms, 218 poor, 126, 135, 149, 160, 207 population, 40, 207, 209, 210, 211, 215, 216, 217, 218 porosity, ix, 5, 7, 8, 18, 23, 25, 171, 198, 200, 227, 228, 230 porous materials, 186, 190 ports, 67, 68, 94, 105, 106, 108 Portugal, 165 positive correlation, 120 potassium, 200 power, viii, ix, x, 20, 21, 25, 40, 41, 45, 50, 51, 54, 72, 73, 75, 83, 85, 86, 91, 92, 96, 97, 98, 103, 138, 142, 163, 164, 171, 172, 173, 185, 200, 202, 235, 236, 237, 240, 241, 244, 247, 248 power plants, viii, 40, 45, 72, 86, 92, 96, 98, 164, 171, 172, 173, 236 precipitation, 86, 237 prediction, 9, 30, 39, 58 pressure, vii, 1, 3, 5, 7, 8, 21, 23, 25, 28, 29, 34, 74, 94, 110, 222, 225 probability, 56 probe, 4, 43, 105, 110 production, ix, 2, 21, 89, 102, 103, 131, 135, 164, 176, 189, 219, 224 program, 50, 64, 71, 72, 78 propagation, 53, 55, 56, 57, 58, 59, 65, 66, 68, 69, 70, 71, 80, 81 propane, 97, 109, 138, 140, 143, 144, 160 properties, 20, 41, 44, 57, 63, 65, 68, 71, 74, 75, 83, 102, 123, 164, 172, 202, 203 pulse, 54 pumps, 110 pyrite, 175, 176, 179, 180, 181, 183, 184, 189, 231 pyrolysis, viii, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 27, 28, 29, 30, 33, 34, 42, 43, 46, 49, 50, 53, 71, 76, 77, 82, 83, 84, 85, 98, 101, 119, 231 pyrolysis gases, 119

Q  quartz, viii, 29, 54, 171, 175, 176, 179, 180, 181, 182, 188, 189, 190, 191, 195, 200, 203, 228, 229, 230

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R  radiation, 22, 60, 200, 221 radicals, viii, 28, 29, 30, 46, 48, 71, 72, 85, 88, 89, 96, 99, 100, 101, 102, 119, 121, 131, 134, 135, 136, 138, 144, 151 range, 8, 21, 32, 74, 90, 94, 95, 96, 97, 101, 105, 108, 112, 121, 124, 137, 138, 139, 141, 142, 144, 147, 149, 150, 158, 160, 161, 163, 181, 183, 207, 230, 237 raw materials, 164 reaction mechanism, 13, 20, 30, 31, 48, 71, 83, 98, 151 reaction order, 13, 15, 16, 19, 20, 23 reaction rate, 42, 46, 50, 88, 119 reaction time, 10, 43, 134 reaction zone, 131, 132, 152 reactions, viii, 2, 3, 4, 9, 11, 13, 14, 15, 16, 17, 19, 20, 21, 22, 26, 27, 33, 34, 40, 41, 42, 43, 44, 45, 49, 50, 71, 76, 85, 86, 87, 88, 89, 95, 98, 101, 102, 103, 119, 131, 134, 135, 155, 156, 175, 189 reactivity, vii, 1, 6, 7, 13, 15, 16, 17, 23, 31, 33, 111, 113, 114, 120, 132, 140 reason, ix, 30, 171, 181, 183, 198, 200, 230 recovery, 160 recrystallization, 189 region, 47, 49, 50, 52, 59, 60, 71, 72, 95, 138, 140, 147, 152, 157, 160, 236, 237 regression, 212 regression analysis, 212 relationship, 45, 47, 49, 61, 114, 117, 120, 121, 133, 136, 218, 242 reproduction, 242 reptile, 236, 241 reptile species, 236, 241 resistance, 15, 218 resolution, 5 resources, 40, 172, 236 respect, 5, 31, 106, 132, 136 retention, 243 revenue, 103 rice, 98 rice husk, 98 risk, 210, 212, 217 risk factors, 212 room temperature, 54 Royal Society, 232 rubber, viii, 85, 108, 138, 164 rural population, 209, 212 Russia, 219, 231, 233 rutile, viii, 171, 175, 176, 179

S  sampling, x, 4, 43, 71, 104, 106, 108, 110, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244 saturation, 160 savings, 164 scale system, 96 scanning electron microscopy, 203 scatter, 31, 117 scattering, 54 sediments, 222, 232, 233, 243 selectivity, 30, 31 sensitivity, 136, 156, 157 separation, 40 severity, 186, 187, 189, 210, 212 sewage, 243 shape, 50, 53, 67, 68, 71 sheep, ix, x, 235, 236, 237, 239, 243, 244 signs, 151, 158, 243 silica, viii, 171, 179, 187, 188, 193, 197, 199, 200, 203, 225, 228 silicon, 227 simulation, 25, 26, 73, 82, 83, 84 sintering, 17, 186 SiO2, 177, 178, 179, 180, 182, 183, 185, 186, 187, 197, 199, 221, 223, 225, 230 skin, 209, 212, 213, 218 skin cancer, 209, 212 slag, 102, 178, 186, 187, 189, 202 socioeconomic status, 212 software, 247, 248 soil, 237, 239, 243, 247 solid phase, 41, 42, 49, 76 solid waste, 90 South Africa, 108, 138, 139, 140, 142, 143 Southeast Asia, 216 speciation, 187 species, ix, x, 2, 8, 9, 11, 18, 28, 40, 50, 90, 97, 100, 110, 135, 142, 155, 175, 235, 237, 239, 240, 241, 242, 243, 244, 246, 247 species richness, x, 235, 241, 244, 246 specific gravity, 222 specific heat, viii, 4, 40, 75, 81, 222 specific surface, 15, 23, 25 spectroscopy, viii, 171, 173, 240 spectrum, 162, 163, 216 speed, 54, 74, 84 spelling, 207, 216 stability, 54, 80, 110, 224 stabilization, 59, 61 standard deviation, 10, 24 statistics, 11, 13 steel, 29, 108

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stoichiometry, 71, 75, 76, 91, 105, 106, 107, 108, 109, 111, 112, 118, 119, 120, 121, 134, 140, 143, 147, 149, 151, 152, 155, 156, 158, 160 storage, viii, 40, 75, 164 stoves, 206, 207, 208, 213 strategies, 86, 87, 98 succession, 224 sulfur, 73, 222, 231 supply, 60, 61, 63, 64, 67, 71, 91, 94, 104 surface area, 5, 7, 15, 17, 18, 19, 22, 23, 25, 31, 119 surface reactions, 21 surveillance, 210, 212 susceptibility, 209, 213, 217 Sweden, 169 swelling process, 7 symbols, 66, 111, 128, 129, 130, 132, 157 symptom, 210, 211 symptoms, 210, 211, 212 synergistic effect, 131

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T  Taiwan, 217 tar, 3, 4, 5, 6, 7, 10, 11, 98 TCR, 108, 138, 143, 144, 145, 147 testing, 97, 106, 110, 247 tetracycline antibiotics, 243 texture, 225 TGA, 3, 7, 23, 32, 110, 140 Thailand, ix, 205 thermal treatment, 138, 177 thermodynamic equilibrium, 88 threshold, 207, 242 titanium, ix, 197, 219 toluene, 5 toxic metals, 243, 248 toxicity, 218, 243 trace elements, 201 transformation, viii, 16, 26, 27, 28, 171, 172, 173, 175, 177, 181, 189, 200, 201 transformation processes, 172, 200 transformations, 17, 28, 175, 201, 202, 231 transition, 106, 134, 179, 180, 181, 183, 184 transmission electron microscopy, 5 transpiration, 236 transport, 2, 10, 202, 218 treatment methods, 91 trends, ix, 21, 28, 115, 117, 124, 125, 129, 161, 220

U 

Ukraine, 233 uniform, 67 universal gas constant, 18 uranium, 40 urea, 95, 110, 149, 150, 151, 152, 153, 154, 155, 156, 157, 160, 164 urine, 207, 213, 218 Uruguay, 237

V  vanadium, ix, 219 vapor, 3, 7, 54, 231 variations, 4, 20, 25, 31, 34 vegetables, 207, 212 vegetation, x, 87, 235, 236, 237, 240, 242, 247 velocity, 4, 53, 54, 55, 56, 57, 58, 65, 66, 68, 69, 70, 71, 80, 81, 94, 107, 237 village, 207, 208, 210, 212, 216, 218 viscosity, 172, 187, 193, 200, 202 volatility, 114, 132 volatilization, viii, 171, 200

W  wall temperature, 22, 58, 60, 68 waste, viii, ix, 85, 97, 101, 108, 109, 138, 143, 144, 145, 147, 164, 219, 220, 221, 224, 231, 232, 233 waste heat, 233 water resources, ix, 219 water vapor, 230, 231 wear, x, 236, 239, 241, 243, 244, 246, 247 weight loss, 8, 177, 179, 180, 181, 182, 183, 184 welding, 227, 229, 230

X  XPS, 28 X-ray diffraction, viii, 5, 171, 173, 174, 175, 177, 188, 189, 190, 191 X-ray diffraction (XRD), viii, 5, 171, 173 X-ray photoelectron spectroscopy (XPS), 27 XRD, 6, 158, 175, 180

Y  yuan, 172

Z  zinc, x, 225, 236, 240, 241, 243, 244, 246

UK, 74, 85, 86, 105, 108, 111, 113, 116, 121, 164, 165, 166, 202

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