Article pubs.acs.org/EF
Modeling the Nonisothermal Devolatilization Kinetics of Typical South African Coals Burgert B. Hattingh,* Raymond C. Everson, Hein W. J. P. Neomagus, John R. Bunt, Daniel van Niekerk, and Ben P. Ashton Research Focus Area for Chemical Resource Beneficiation, North-West University, Potchefstroom Campus, Private Bag X6001, Potchefstroom, 2520 North West, South Africa Energy Systems, School of Chemical and Minerals Engineering, North-West University, Potchefstroom Campus, Private Bag X6001, Potchefstroom, 2520 North West, South Africa Sasol Technology (Pty) Ltd., Research & Development, Coal & Gas Processing Technology, Box 1, Sasolburg, 1947 Free State, South Africa S Supporting Information *
ABSTRACT: Multicomponent model fitting was conducted in order to evaluate the devolatilization rate behavior of four typical South African coals, with the aid of nonisothermal thermogravimetry. Rate evaluation was conducted at four different heating rates (5, 10, 25, and 40 K/min) by heating the samples under an inert N2 atmosphere to 950 °C. Evaluation of the kinetic parameters of each coal involved the numerical regression of nonisothermal rate data in MATLAB 7.1.1 according to a pseudocomponent modeling philosophy. The number of pseudocomponents used ranged between three and eight, as larger values induced the risk of over fitting. Quality of fit (QOF) was found to decrease with decreasing heating rate as a result of improved separation of the individual component reactions at the lower heating rates. All four coals showed the occurrence of similar pseudocomponent reactions, although significant differences were observed in the fractional contributions of the different pseudocomponents to the overall reaction rates. Modeling results indicated that the assumption of eight pseudocomponents produced the lowest QOF values and subsequently the best fit to the devolatilization profiles of each coal. For the vitrnite-rich coals (G#5 and TSH), no remarkable decrease in QOF could be observed after 6 pseudocomponent reactions, suggesting that even 6 or 7 pseudocomponent reactions would have provided accurate experimental predictions. Activation energies determined from the selected number of pseudocomponents (between 3 and 8) were found to range between 20 and 250 kJ/mol.
1. INTRODUCTION Coal devolatilization plays an important role in not only the metallurgical industry for producing coke but also during coal gasification where it normally constitutes the initial step of the process.1,2 It is therefore important to evaluate the devolatilization behavior of a coal feedstock in order to assess and optimize the production of valuable products such as char/coke, tar, and gas. Extensive research during the past few decades has advanced our knowledge of the kinetics and mechanisms of the devolatilization process. Furthermore, it has also provided us with valuable techniques for predicting, to a reasonable extent, the behavior of coals.3−5 Devolatilization modeling is quite straightforward if the chemical reaction step is rate controlling and the fuels are of a simple characteristic nature. Kinetic models for describing thermal decomposition range in different levels of complexity from free radical mechanistic models for simple hydrocarbon species such as propane6 to more complex reaction schemes. The latter involves a number of individual reactions, incorporating extra transport steps such as in the case of naphtha devolatilization.7 The kinetic description of more complex polyaromatic substances such as coal presents a challenging task, due to a vast amount of reactions involved. Kinetic evaluation of these substances is therefore normally conducted using pseudomechanistic models, which attribute the overall measured reaction rate to the cumulative effect of a number of separate reactions.8,9 © 2013 American Chemical Society
A large number of possible modeling strategies are currently available, of which the simplest are empirical in nature and employ global kinetics.8−11 Available models can be divided into either general weight-loss models or structural models. Typical weight-loss models include models employing a (1) single rate, (2) two rates, (3) multiple rates, and (4) distributed rates.12−21 Although simplistic in nature, the validity of a single reaction rate mechanism is quite limited. Kinetic parameters derived at a single heating rate has been generally shown not to be appropriate to other heating rates.22 Currently, the Distributed Activation Energy Model (DAEM) (Anthony-Howard model) has been shown to be the most powerful model for predicting devolatilization behavior.10,18,19,22−25 This complicated model was first proposed by Pitt26 and assumes the devolatilization process to consist of an infinite series of independent parallel first-order reactions. Accordingly, coal devolatilization can be explained by a distribution of activation energies about some mean activation energy value (Ea,0). The function within the model f(Ea) accounts for a distribution of activation energies and is assumed to be of Gaussian form. A simplistic approach for solving the DAEM considers a common, Received: October 25, 2013 Revised: December 25, 2013 Published: December 26, 2013 920
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first-order reaction model impractical in determination of the kinetic parameters. In a response to this, authors such as Alonso et al.28 formulated a lumped first-order model allowing for the fractional contribution (ξ) made by the different individual peaks or parallel first-order reactions. Extensive research has been conducted in an attempt to understand the devolatilization kinetics of northern hemisphere (Carboniferous) coals, which are normally characterized by their high proportions of vitrinite, large amounts of reactive inertinite, and very low ash contents.40−45 These coals have been shown to be very different from South African coals, due to their differences in depositional environment.46−48 In contrast, Permian aged Gondwanaland coals are mainly rich in inertinite with a high abundance of minerals, while some coal deposits (Grootegeluk and Venda-Pafuri districts) also display higher abundances of vitrinite-rich coals. Although very limited, investigations such as those conducted by Aboyade et al.49 have provided a means of describing the devolatilization behavior of some South African coals. However, this work did not include the evaluation of any decomposition reactions below 200 °C, therefore neglecting the release of inherent moisture, crystal water, and amorphous phases from the coal particles. An investigation was therefore undertaken to gain a better understanding of the kinetic behavior of four typical South African coals (including for the release of moisture). Evaluation of the devolatilization behavior of the selected coals entailed the determination of the total mass loss behavior of the amount of volatile matter and not of individual species. Mass loss behavior during devolatilization was therefore described at the hand of the pseudocomponent modeling approach as proposed by Alonso et al.28 Kinetic parameters derived using this approach can be ultimately used in the evaluation of large particle devolatilization models (including for heat and mass transport effects) for effectively describing commercial coal conversion processes. This paper therefore aims to provide a comprehensive kinetic description of a variety of South African coal types, ranging from differences in maceral composition to differences in swelling behavior.
constant frequency factor applicable for all activation energies (or reactions) in the distribution.27 This simplification has, however, been criticized by Alonso et al.28 who demonstrated that the isokinetic effect, which involves the mutual relationship between pre-exponential constant and activation energy, cannot be neglected in the calculation procedure. Furthermore, the validity of using a constant pre-exponential factor becomes questionable when the function f(Ea) spreads over a wide range of Ea values.29 The use of continuous distribution curves (i.e., Gaussian), for the description of overall activation energy by a constant average activation energy and a standard deviation of energies (σ), has also raised concerns.29,30 From this perspective, the use of a Gaussian distribution does not necessarily guarantee the correct prediction of the characteristic f(Ea) curve of the substance under investigation. Although a very powerful model for estimating devolatilization behavior, the DAEM requires an extensive numerical discretization procedure for solving the required parameters. Recent advances in the understanding of the coal molecular structure has led to the development of some structural/network models of which the FG-DVC (Functional Group-Depolymerization Vaporization Cross-linking), the FLASHCHAIN, and the percolation lattice theory are the most commonly known.31−33 More comprehensive chemical models have also been formulated for describing char and tar formation.31 These models address the influence of coal molecular structure on volatile evolution rate by assuming that aromatic groups are linked by bridges and peripheral groups.34,35 However, evaluation of the kinetic parameters relevant to the different bridge and peripheral functional groups requires extensive knowledge of the coal molecular structure as determined using advanced characterization techniques such as NMR (Nuclear Magnetic Resonance spectroscopy), etc. The choice of a suitable kinetic model should therefore bear relevance to the process under investigation. The norm for kinetic studies is to conduct reactions under isothermal conditions, especially for fast reactions such as devolatilization at high temperatures. Time-resolved measurements of coal devolatilization are therefore very difficult and present uncertainty due to the fact that the devolatilization process normally completes within a few seconds before the isothermal state is reached.9 Currently, nonisothermal techniques have proven to be more useful than isothermal techniques for deriving the kinetic triplets (activation energy, preexponential factor, and reaction order).9,28 In addition, the process of deconvolution of differential thermogravimetric (DTG) curves into pseudocomponent curves has been found to be much easier for model- and less complex carbon-containing compounds (such as biomass and oil shales) than coals.36−38 This challenge necessitates the need for further elaboration on the behavior of typical DTG curves of coals, in order to formulate and evaluate an appropriate model. In general, a typical coal DTG curve is characterized by a mass loss peak in the low temperature region with a maximum rate occurring between 40 and 100 °C. This corresponds to the initial release of absorbed moisture. In some cases an adjacent peak to the absorbed moisture peak is observed, which has been attributed to either the release of crystal water associated with inherent minerals or chemically bonded moisture.28,39 The main devolatilization zone (>300 °C) constitutes the release of tar, primary gases, secondary gases, and the subsequent formation of char.14,28 The extensive asymmetric nature of a typical DTG curve of coal devolatilization therefore makes the use of a single
2. EXPERIMENTAL SECTION 2.1. Coal Selection. Four coals were selected from South African collieries: three noncaking coals originating respectively from the Witbank no.2 (INY), 4 (UMZ), and 5 (G#5) seams and one coking coal (TSH) originating from the Venda-Pafuri sector of the Soutpansberg coalfields.50 The three noncoking coals were beneficiated samples from the respective mines, while coal TSH was density separated elsewhere.51 The choice of the four coals was based on their (1) similarity in bituminous rank, (2) varying vitrinite content, (3) relatively low ash content ( UMZ > INY > TSH. Further investigation of the respective TG results of each coal revealed the division of the overall TG profile into clear regions of mass loss i.e., the release of moisture, occluded gases, and secondary pyrolytic water at temperatures below 350 °C and a main devolatilization regime where the parent coal structure decomposes to form tars, gases, and char at temperatures exceeding 350 °C. From DTG results it was evident that all four coals exhibited a clear, primary peak of devolatilization confined to the temperature range between 350 and 600 °C, while a subsequent shift in peak temperature occurred for the maximum evolution rate of the main peak of coal TSH. This subsequent shift in peak temperature for coal TSH could be attributed to both its higher aromaticity (rank) as well as its extensive thermoplasticity, which could have resulted in repolymerization reactions becoming more favorable in the first
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors would like to acknowledge and thank the following parties: Sasol for financial support, Anglo Coal and Exxaro for providing the necessary coal samples, and Dr. Marion Carrier (University of Stellenbosch) for her valuable inputs regarding coal devolatilization kinetics. The work presented in this paper is based on the research supported by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa (NRF). Any opinion, finding or conclusion or recommendation expressed in this material is that of the author(s), and the NRF does not accept any liability in this regard.
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NOMENCLATURE
Abbreviations
d.a.f. = dry, ash-free basis d.b. = dry basis FSI = free swelling index m.m.f.b. = mineral matter free basis OBF = objective function QOF = quality of fit 930
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Greek symbols
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β = heating rate σ = standard deviation of DAEM distribution ξi = fractional contribution of component, i
Roman symbols
Ea = activation energy Ea,i = activation energy of component i k0 = pre-exponential constant k0,i = pre-exponential constant of the ith pseudocomponent m0 = initial sample mass mf = final sample mass mt = logged mass at a certain time t Nk = number of heating rates Nm = number of DTG experimental points R = molar gas constant t = time T = temperature X = fractional conversion Xi = fractional conversion of the ith pseudocomponent Xt = overall fractional conversion of volatiles
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dx.doi.org/10.1021/ef402124f | Energy Fuels 2014, 28, 920−933
