A Burnout Prediction Model Based around Char Morphology - Energy

Energy & Fuels 2006, 20, 1175-1183

1175

A Burnout Prediction Model Based around Char Morphology Tao Wu, Edward Lester,* and Michael Cloke School of Chemical, EnVironmental and Mining Engineering, UniVersity of Nottingham, Nottingham, NG7 2RD, U.K. ReceiVed April 12, 2005. ReVised Manuscript ReceiVed January 11, 2006

Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300 °C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300 °C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions.

1. Introduction Predicting how well a coal may burn during pulverized fuel (pf) combustion is vital since poor burnout has significant penalties, whether it is in the form of reduced efficiency,1,2 higher emission in particulates,2 or waste materials that cannot be utilized.1-3 Knowing how a coal is likely to burn in advance of procurement on the spot market, or when considering an unknown coal with little in the way of burnout history, constitutes extremely valuable information. There are many ways to predict burnout using a variety of approaches, including volatile content,4 fuel ratio,5,6 ash content,7 particle size distribution,4,8 rank,6 vitrinite reflectance,6 full maceral reflectance,9,10 macerals,11,12 microlithotypes,13 and kinetic burnout models.14,15 * Corresponding author. Phone: 00 44 (0) 115 951 4974. Fax: 00 44 (0) 115 951 4115. E-mail: [email protected]. (1) Styszko-Grochowiak, K.; Golła×abs´, J.; Jankowski, H.; Kozin´ski, S. Characterization of the coal fly ash for the purpose of improvement of industrial on-line measurement of unburned carbon content. Fuel 2004, 83, 1847-1853. (2) Su, S.; Pohl, J. H.; Holcombe, D.; Hart, J. A. Techniques to determine ignition, flame stability and burnout of blended coals in p.f. power station boilers. Prog. Energy Combust. Sci. 2001, 27, 75-98. (3) Kulaots, I. H.; Robert, H.; Suuberg, E. M. Size distribution of unburned carbon in cola fly ash and its implications. Fuel 2004, 83, 223230. (4) Wells, W. F.; Smoot, L. D. Relation between reactivity and structure for coals and chars. Fuel 1991, 70, 454-458. (5) Skorupska, N. M. Coal specifications - impact on power station performance; IEA Coal Research: London, 1993. (6) Oka, N.; Murayama, T.; Matsuoka, H.; Yamada, S.; Yamada, T.; Shinozaki, S.; Shibaoka, M.; Thomas, C. G. The influence of rank and maceral composition on ignition and char burnout of pulverized coal. Fuel Process. Technol. 1987, 15, 213-224. (7) Jun, X.; Sun, X.; Hu, S.; Yu, D. An experimental research on boiler combustion performance. Fuel Process. Technol. 2000, 68, 139-151. (8) Cloke, M.; Lester, E.; Belghazi, A. Characterisation of the properties of size fractions from ten world coals and their chars produced in a droptube furnace. Fuel 2002, 81, 699-708. (9) Lester, E.; Cloke, M. The characterisation of coals and their respective chars formed at 1300°C in a drop tube furnace. Fuel 1999, 78, 16451658. (10) Cloke, M.; Lester, E.; Gibb, W. Characterization of coal with respect to carbon burnout in p.f.-fired boilers. Fuel 1997, 76, 1257-1267.

Some char burnout models have been developed that model the processes occurring during coal combustion.14-17 A variety of coal properties readily exist and have been included in these models, including proximate and ultimate analysis data,14-17 char intrinsic reactivity,14,15 and even bulk char structure.14-16 Overall, the CBK model15 is the most widely accepted carbon kinetic model that is able to predict char conversion under conditions relevant to pulverized coal combustion. It has been shown18 that the accuracy of CBK model prediction is largely dependent on the input data on distributions of particle size and density. However, none of the char combustion models developed thus far have attempted to consider morphology of individual char particles while burning, though some attempts have been made to include basic char structure in burnout models.16,19 Therefore, how to collect real detailed char size distribution and other morphology data in order to relate char morphology and reactivity under certain operating conditions to char burnout remains an open problem. (11) Su, S.; Pohl, J. H.; Holcombe, D.; Hart, J. A. A proposed maceral index to predict combustion behavior of coal. Fuel 2001, 80, 699-706. (12) Vleeskens, J. M.; Men×abe´ndez, R. M.; Roos, C. M.; Thomas, C. G. Combustion in the burnout stage: the fate of inertinite. Fuel Process. Technol. 1993, 36, 91-99. (13) Bailey, J. G.; Tate, A.; Diessel, C. F. K.; Wall, T. F. A char morphology system with applications to coal combustion. Fuel 1990, 69, 225-239. (14) Hurt, R. H.; Calo, J. M. Semi-global intrinsic kinetics for char combustion modeling. Combust. Flame 2001, 125, 1138-1149. (15) Hurt, R.; Sun, J.-K.; Lunden, M. A Kinetic Model of Carbon Burnout in Pulverized Coal Combustion. Combust. Flame 1998, 113, 181-197. (16) Liu, G.-S.; Rezaei, H. R.; Lucas, J. A.; Harris, D. J.; Wall, T. F. Modelling of a pressurised entrained flow coal gasifier: the effect of reaction kinetics and char structure. Fuel 2000, 79, 1767-1779. (17) Eghlimi, A. L.; Lu, L.; Sahajwalla, V.; Harris, D. Computational modelling of char combustion based on the structure of char particles. In Second International Conference on CFD in the Minerals and Process Industries; CSIRO: Melbourne, Australia, 1999. (18) Maloney, D. J.; Monazam, E. R.; Casleton, K. H.; Shaddix, C. R. Evaluation of char combustion models: measurement and analysis of variability in char particle size and density. Proc. Combust. Inst. 2005, 30, 2197-2204. (19) Backreedy, R. I.; Habib, R.; Jones, J. M.; Pourkashanian, M.; Williams, A. Extended coal combustion model. Fuel 1999, 78, 1745-1754.

10.1021/ef050101o CCC: $33.50 © 2006 American Chemical Society Published on Web 03/16/2006

1176 Energy & Fuels, Vol. 20, No. 3, 2006

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Table 1. Coal Proximate and Petrographic Analyses Data coal origin Collinsville El Cerrejon Guasare Island Creek Kaltim Prima Kellingley Klein Kopje La Jagua La Loma Middelburg Oreganal Pocahontas Reitspruit Stratford Welbeck

size fraction (µm)

volatiles (db)

fixed carbon (db)

ash (db)

VRo %

vitrinite

liptinite

inertinite

% unreactives

53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125 53-75 106-125

19.8 20.1 35.7 33.1 35.1 34.7 16.5 16.1 39.7 40.1 31.6 29.5 24.4 22.6 38.6 39.4 38.7 38.8 26.2 23.2 38.7 38.4 16.9 16.9 28.6 25.0 24.0 24.0 30.8 29.3

61.9 62.5 59.7 55.1 59.6 60.2 79.9 80.4 58.3 57.1 60.0 56.2 60.4 61.3 58.7 58.4 56.8 56.5 60.1 60.8 59.1 58.3 77.6 77.6 57.6 57.4 58.1 58.1 57.1 51.9

18.3 17.3 4.5 11.5 5.3 4.7 3.5 3.5 2.0 2.4 8.4 14.1 15.2 15.3 2.7 1.8 4.5 4.0 13.7 14.7 2.1 3.4 5.4 5.4 13.8 16.6 17.9 17.9 12.2 17.9

1.08 1.04 0.58 0.57 0.70 0.72 1.53 1.42 0.54 0.54 0.67 0.70 0.63 0.65 0.51 0.53 0.49 0.50 0.71 0.71 0.48 0.50 1.04 1.14 0.62 0.58 0.83 0.84 0.69 0.68

59.8 55.4 94.3 92.7 85.8 81.5 92.8 91.1 96.2 94.1 83.9 75.3 42.0 35.4 83.0 80.1 87.1 86.6 34.1 34.5 89.0 91.0 88.1 85.3 50.5 50.2 69.0 73.2 84.2 84.4

1.3 1.6 0.9 1.2 2.4 4.9 0.2 0.5 1.5 3.7 8.4 14.3 2.4 2.3 1.0 1.0 0.8 2.3 7.5 12.6 0.7 1.2 1.4 0.7 4.1 4.6 1.6 6.9 4.1 5.0

38.8 43.1 4.9 6.1 11.9 13.6 7.1 8.4 2.2 2.2 7.7 10.4 55.7 62.3 16.0 18.9 12.2 11.1 58.4 52.9 10.3 7.7 10.5 13.9 45.4 45.2 29.5 19.9 11.7 10.7

60.1 66.3 3.0 3.5 7.1 7.9 70.1 79.3 1.3 0.2 3.4 6.6 33.9 44.8 5.3 6.4 3.6 3.9 40.0 41.5 2.9 2.7 64.2 73.9 20.2 23.0 19.7 25.7 4.7 4.8

In this article, an advanced automated char image-analysis (AChIA) program was used to investigate char morphology of 30 char samples. In addition, an automated coal image analysis technique20 was adopted in this study to predict char morphology on the basis of coal image analysis results.21 These two techniques make it possible for the first time to further improve char combustion prediction under pf combustion conditions by the introduction of detailed information about char size distribution and morphology of individual char in the combustion model. A char burnout (ChB) model was developed to predict the overall performance of burnout in a pulverized coal-fired furnace. The aim of the model is to use realistic char morphological parameters in a conventional char combustion model to predict burnout with higher accuracy.

Microscopic Image Analysis. Char samples were prepared in blocks using a liquid resin (Estratil 2195, provided by Cray Valley Ltd., Spain) mixed with methyl-ethyl-ketone (50% w/w). Coals were mounted in Simplex Rapid acrylic resin powder using methylmethacrylate as a binding agent. Polished char blocks were put under a Zeiss Leitz Ortholux II POL-BK microscope with ×32 magnification oil-immersion objective and ×10 magnification eyepiece. A Zeiss AxioCam was attached to the microscope and was connected to a PC by an optic fiber cable. The computer was a 3 GHz computer with 1 GB of ram and operated with the KS400 version 3.1 image analysis system (Imaging Associates Ltd). The camera was capable of capturing images in 1300 × 1030 pixels, which could then be transferred to the computer via the optic fiber cable at a rate of 200 Mbits/s for processing and analysis.

3. The Char Burnout Model 2. Experimental Section Coal Characterization. For this study, we selected 15 coals that were in a wide rank range and were from different parts of the world. All coal samples were ground and sieved into two size ranges (53-75 µm and 106-125 µm). Table 1 shows the proximate and petrographic analysis results of all 15 coal samples. Refiring Tests in the Drop Tube Furnace. Both size fractions for the 15 coals were pyrolyzed using a drop tube furnace (DTF) operating at 1300 °C, 200 ms, and 1% oxygen. These char samples were then passed through the drop tube again at 1300 °C over 200, 400, and 600 ms residence times with 5% oxygen to evaluate burnout performance. The initial char production has been shown to approximate to the initial stage of devolatilization in a large scale facility,10 and the refire test method is a tested method of determining coal burnout in the early stages of pf combustion.22 (20) Lester, E.; Watts, D.; Cloke, M. A novel automated image analysis method for maceral analysis. Fuel 2002, 81, 2209-2217. (21) Lester, E.; Wu, T. Predicting pyrolysis char types from coal characteristics: An automated image analysis method. Energy Fuels, submitted for review, 2005. (22) Cloke, M.; Lester, E.; Thompson, A. W. Combustion characteristics of coals using a drop-tube furnace. Fuel 2002, 81, 727-735.

The Use of Char Morphology as a Kinetic Model Input. Since a char is the intermediate particle produced during coal combustion, data about its morphology is relatively difficult to obtain. Inclusion in a kinetic model is therefore a potential limitation since the coal must be obtained, crushed to size, and tested at a small scale rig to produce char material that can then be characterized. Although this can be done,8,9,23-29 it is an expensive and slow process, especially if results are required (23) Rosenberg, P.; Petersen, H. I.; Thomsen, E. Combustion char morphology related to combustion temperature and coal petrography. Fuel 1996, 75, 1071-1082. (24) Zheng, Y.; Wang, Z. Distribution and burning modes of char particles during combustion. Fuel 1996, 75, 1434-1440. (25) Alvarez, D.; Borrego, A. G.; Menendez, R. Unbiased methods for the morphological description of char structures. Fuel 1997, 76, 12411248. (26) Barranco, R.; Cloke, M.; Lester, E. Prediction of the burnout performance of some South American coals using a drop-tube furnace. Fuel 2003, 82, 1893-1899. (27) Lester, E.; Cloke, M.; Belghazi, A. Characterisation of the properties of size fractions from ten world coals and their chars produced in a droptube furnace. Fuel 2002, 81, 699-708.

Burnout Prediction Model Based around Char Morphology

Energy & Fuels, Vol. 20, No. 3, 2006 1177

Figure 1. Methods for predicting burnout with and without char morphology data.

quickly. One of the unwritten rules for burnout models, to date, has been the integration of only easily accessible information14,15 or inferred information30 from standard methods. If char morphology is going to be included in a kinetic model, the data have to be generated cheaply and reasonably quickly. Figure 1 shows how char data input can be integrated into methods for predicting burnout. Overall, there are three ways to include char morphology within combustion models. First, with the CBK model,15 chars are assigned with an average porosity, divided by tortuosity.31 Second, collecting char morphology data from actual chars8,13,32 produced in a DTF or other laboratory facility operating under conditions similar to that of pf coal combustion (ChBa model). Third, by predicting char morphology on the basis of coal data and then including the predicted char data in the char burnout model (ChBp), which has been developed. This approach has been developed using image analysis techniques described elsewhere.21 In this article, ChBa and ChBp were investigated. After devolatilization and volatile combustion stages, coal combustion is dominated by the combustion of char. Char is a porous carbonaceous particle associated with mineral matter and a small amount of organic matter. It is obvious that char particle temperature, gas temperature, and oxygen concentration of the gas phase surrounding the char particle changes over time through the chemical reactions of oxygen, carbon dioxide, and water vapor with the surface of the char. However, the dominant reaction is the reaction of carbon with oxygen. Generally, the combustion of char particle consists of three steps; for instance, heat transfer and the diffusion of reactant gases and products through the external layer; the diffusion and heat transfer within (28) Liu, G.; Benyon, P.; Benfell, K. E.; Bryant, G. W.; Tate, A. G.; Boyd, R. K.; Harris, D. J.; Wall, T. F. The porous structure of bituminous coal chars and its influence on combustion and gasification under chemically controlled conditions. Fuel 2000, 79, 617-626. (29) Alonso, M. J. G.; Borrego, A. G.; Alvarez, D.; Mene´ndez, R. Pyrolysis behaviour of pulverised coals at different temperatures. Fuel 1999, 78, 1501-1513. (30) Jones, J. M.; Pourkashanian, M.; Rena, C. D.; Williams, A. Modelling the relationship of coal structure to char porosity. Fuel 1999, 78, 1737-1744. (31) Hurt, R. CBK/E Model; 2003, personal communication. (32) Cloke, M.; Wu, T.; Barranco, R.; Lester, E. Char characterisation and its application in a coal burnout model. Fuel 2003, 82, 1989-2000.

pores of the char particle; and chemisorption of reactant gases, surface reaction, and desorption of the products. The rate of char combustion affects the total residence time necessary to burn the coal completely in a utility boiler. Degree of burnout is therefore dictated directly by the design of the boiler water wall height, the furnace residence time, and heat flux profile. The char burnout model in this work is based on the basic concept of the CBK model15 but incorporates a char morphological submodel. The hierarchical structure of the char burnout model established in this study is shown in Figure 2. The Influence of Char Structure on Reaction Rate. The rate of reaction taking place in char particles is influenced by the oxygen diffusion process, which occurs in the bulk flow surrounding the char particle and inside porous char particles. Char structure determines, to some extent, the aerodynamic and heat transfer properties of the char particle during the course of combustion under pf combustion conditions. If the shape of the char particles is irregular, then diffusion and reaction rates would be a multidimensional problem. Consequently, a gradient in temperature and concentration would exist over the whole surface area of the char particle. Therefore, when considering the char surface, angular gradients of temperature may occur, and during transient operation, radial gradients would also exist due to the finite rates of thermal diffusion. However, in this study, the char particles are assumed to be spherical, allowing the issues of radial and angular variations in temperature and concentration to be ignored. Thus, the char particle is modeled as having a constant temperature over the cross section at any axial location. Morphological Description of Char as a Model Input. The most important characteristics of char structure for a kinetic model are particle size, size distribution, true density, char porosity, pore volume and its distribution, and surface area, because these features have significant impacts on the char combustion under high temperatures. On the basis of char image analysis, char particles sampled from a DTF were classified into six major categories. Table 2 shows the distribution of char morphotypes derived from the 15 coals investigated in this study together with the overall porosity of corresponding char samples. Table 3 shows some


Wu et al.

Figure 2. Schematic of char burnout model hierarchical structure. Table 2. Overall Char Porosity and the Distribution of Different Char Morphotypes coal origin Collinsville El Cerrejon Guasare Island Creek Kaltim Prima Kellingley Klein Kopje La Jagua La Loma Middelburg Oreganal Pocohontas Reitspruit Stratford Welbeck

size fraction (µm)

tenuisphere

crassisphere

tenuinetwork

crassinetwork

fusinoid

solid

overall porosity

+53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125 +53-75 +106-125

14.5 9.8 45.1 8.2 40.9 4.2 21.6 4.9 15.5 9.1 16.4 3.4 16.2 4.5 20.6 9.6 28.7 20.7 24.4 2.2 27.9 43.1 36.2 12.6 14.0 8.2 14.5 9.3 44.1 9.6

27.5 40.1 13.1 58.0 17.7 53.7 26.8 61.1 37.6 31.1 17.8 48.0 10.1 20.5 29.6 55.9 17.5 5.3 9.5 19.1 40.1 18.1 21.6 59.9 14.8 17.7 22.2 53.2 13.3 50.9

42.4 10.4 34.8 11.2 35.4 14.9 39.2 2.0 41.5 49.8 26.2 13.0 54.3 36.1 33.5 21.0 45.1 56.7 55.9 37.3 22.5 34.2 31.0 6.3 49.6 46.5 44.0 12.5 27.3 20.4

6.0 33.6 2.4 17.4 0.6 22.5 5.7 21.9 2.2 8.9 5.5 25.3 6.3 35.5 5.2 12.6 2.2 7.9 2.9 37.3 0.8 3.3 2.6 16.4 6.9 21.3 4.7 21.0 4.3 11.0

9.7 5.9 4.4 4.8 5.3 4.2 6.5 8.7 3.3 0.9 23.9 8.1 11.9 3.5 10.5 0.8 5.7 8.7 6.6 3.9 8.4 1.4 8.6 4.9 13.4 4.8 13.9 3.8 9.4 7.9

0.0 0.2 0.2 0.4 0.0 0.4 0.2 1.4 0.0 0.2 10.2 2.2 1.2 0.0 0.6 0.2 0.8 0.8 0.8 0.2 0.2 0.0 0.0 0.0 1.4 1.5 0.8 0.2 1.6 0.2

60.9 57.5 63.0 64.6 67.6 63.1 56.4 57.2 62.4 73.4 38.5 57.1 53.4 63.3 54.7 70.5 60.0 65.9 61.2 64.1 64.8 77.0 55.1 64.5 51.2 63.6 52.5 63.9 62.9 68.0

examples of individual char types and the features that can be included as inputs to the ChB model. Clearly chars from different coal origins and different size fractions will have distinct morphotype distributions, and the values for each feature (size, wall thickness, porosity) will have a unique effect on final burnout rate of the whole sample. Variations in the proportion of char in each size range is a key parameter, and the size distribution for each char sample is shown in Table 4. The size distributions clearly vary with each different sample despite the coals being presieved into the same nominal size fraction. Char density distribution is obviously associated with the porosity of individual chars. In this study, all chars were grouped into three main categories: thin-walled, thick-walled, and solids. Tenuisphere and tenuinetwork particles (with walls less than 3 µm) were classified as thin-walled particles in a char morphological submodel. Crassisphere and crassinetwork particles, mixed-dense and mixed porous particles, were categorized as thick-walled particles. Inertoid, solid, fusinoid and mineroid

were categorized as solid particles. Char size distribution was described by eight size groups, ranging from 20 to 160 µm. In total, the model uses 24 char groups varying in size and char type. Figure 3 shows how char morphology data are included in the ChBp model. Char size and porosity were predicted on the basis of detailed information collected for individual coal particles. For the ChBa model, the geometric features of chars were collected directly from char samples obtained from the DTF experiment. It is generally accepted that the global carbon-oxygen reaction rate depends on both the rate of diffusion through the gas film surrounding the char particle and into the pores inside the char particle and the intrinsic chemical kinetic rate of carbon combustion. Normally, due to the complexity in char structure, the char pore structure is unknown, although with the present work using automatic image analysis techniques33 detailed information about char morphology is available.



Table 3. Geometric Features of Typical Chars

Table 4. Size Distribution of Chars Collected from DTF coal origin

size fraction

+53-75 +106-125 El Cerrejon +53-75 +106-125 Guasare +53-75 +106-125 Island Creek +53-75 +106-125 Kaltim Prima +53-75 +106-125 Kellingley +53-75 +106-125 Klein Kopje +53-75 +106-125 La Jajua +53-75 +106-125 La Loma +53-75 +106-125 Middelburg +53-75 +106-125 Oreganal +53-75 +106-125 Pocahontas +53-75 +106-125 Reitspruit +53-75 +106-125 Stratford +53-75 +106-125 Welbeck +53-75 +106-125 Collinsville

size bins (µm) 20

40

60

80

100

120 140 160

21.6 0.0 7.1 0.0 14.8 0.0 3.1 0.0 15.0 0.0 3.7 0.0 3.2 0.0 5.8 0.4 5.7 1.0 6.0 0.0 20.6 2.0 6.9 0.0 4.7 0.0 2.9 0.0 19.9 1.5

53.0 5.8 65.9 12.0 46.2 5.4 59.3 6.5 46.5 5.8 64.0 5.9 53.5 8.1 57.3 6.6 62.8 24.9 61.8 5.5 42.3 25.2 64.7 7.7 47.0 14.0 57.2 6.5 50.8 22.0

18.6 25.9 23.2 39.8 26.3 41.6 32.5 31.0 25.4 30.9 28.2 33.2 32.9 28.9 33.1 35.4 26.9 30.0 20.9 25.1 24.4 24.0 25.0 38.5 37.4 36.1 30.8 30.8 25.8 24.7

4.9 31.7 2.4 29.6 7.0 31.3 3.7 31.6 6.9 36.4 3.7 28.1 5.9 29.6 3.2 33.8 4.2 22.1 6.6 29.3 10.5 19.1 2.6 30.0 5.9 28.8 5.3 26.8 3.5 24.1

0.8 21.5 1.0 12.2 2.7 14.1 1.1 18.3 2.2 16.4 0.4 20.2 2.8 18.2 0.6 14.9 0.2 13.0 3.5 20.4 1.5 13.2 0.9 16.0 3.2 12.1 2.4 23.0 0.0 14.1

0.6 9.6 0.2 5.6 2.1 4.6 0.2 6.7 2.2 7.8 0.0 9.0 1.2 8.5 0.0 6.6 0.0 5.1 1.0 7.9 0.6 6.7 0.0 4.9 1.0 5.1 0.8 8.1 0.0 6.0

0.4 3.2 0.2 0.2 1.0 2.2 0.2 2.8 1.0 2.4 0.0 2.0 0.2 2.6 0.0 0.6 0.0 1.2 0.2 6.1 0.0 2.2 0.0 1.6 0.4 1.9 0.4 3.4 0.0 2.7

0.0 2.2 0.0 0.6 0.0 0.8 0.0 3.0 0.8 0.2 0.0 1.6 0.2 4.0 0.0 1.8 0.0 2.8 0.0 5.7 0.0 7.5 0.0 1.4 0.4 1.9 0.2 1.4 0.0 4.8

While modeling, an apparent chemical reaction rate has been employed to combine the intrinsic reaction rate with the pore diffusion rate. This treatment makes the overall reaction rate determined by the rate of diffusion and the apparent chemical reaction rate.

Under high-temperature combustion, mass and heat transfer have a significant impact on the whole process. Oxidants may diffuse through the external boundary layer and into the char particles. The burning rate of the char depends on the chemical rate of the carbon-oxygen reaction at the surfaces, the rate of boundary layer and internal diffusion of oxygen, and also the resistance caused by the ash film formed outside the unreacted char particle. To accurately describe the char burnout process, it is expected that the gas film resistance, ash film resistance, and chemical reaction rate should also be taken into account in the model.34 The critical pore radius at which bulk diffusion equals Knudsen diffusion can be determined by eq 1. 1.17

rP,crit )

Tm 1.543 × 10-4 ‚ x32 ‚ 97.0 P

(1)

Critical pore radii at different temperatures under atmospheric pressure are shown in Table 5. When the random pore model is considered, all pores inside a char can be classified into two categories: macropores and micropores. Effective diffusivity of macropores and micropores can be expressed in eq 2.35

De ) DM ‚ φM +

φµ2(1 + 3φM) ‚ Dµ 1 - φM

(2)

It has been observed that 98% of the char pore volume is occupied by pores larger than 0.1 µm.16 Automated char image (33) Wu, T. Automated Char Image Analysis and the Inclusion of Char Morphology in Char Burnout Modelling. Ph.D. Thesis. Validation of the use of image analysis data as inputs. The University of Nottingham: Nottingham, 2004; p 290.


Wu et al.

Figure 3. Inclusion of char morphology in the ChB model. Table 5. Critical Pore Radii at Different Temperatures temperature (K) critical pore radius (µm)

1000 0.29

1100 0.32

1200 0.36

1300 0.39

analysis observed that primary pores (large pores) dominate the porosity of porous chars. It can be seen that, even under a temperature as high as 1800 K, the critical pore radius is about 0.57 µm. Assuming an ideal crassisphere particle with diameter of 75 µm and wall thickness of 5 µm, the porosity of that particle is 75.1%. The porosity contributed by micropores is 1.5%.16 The primary void can have a radius of 32.5 µm, which is 57 times the critical pore radius. Assuming the radius of micropores is 0.1 µm, 5.7 times less than critical pore radius, the equation yields:

De ) (0.7381DAB)M + (1.542 × 10-3DAB)µ

(3)

It is clear that the contribution made by macropores dominates the overall effective diffusivity. Therefore, the diffusivity contributed by micropores can be neglected. Bulk diffusion is just one-fifty-seventh of Knudsen diffusion, and thus it is a reasonable approximation to ignore Knudsen diffusion for the assumed ideal crassisphere particle. It might be applicable to tenuisphere and crassisphere char particles. However, when network, mixed-dense, mixed-porous, and inertoid particles are encountered, the ratio of pore radius to critical pore radius will not be that much. Bulk diffusion and Knudsen diffusion contributed by macropores are on the similar level. Therefore, Knudsen diffusion contributed by macropores should not be neglected. This implies that, when considering the impacts of char voids on reactants diffusion, it will not bring major errors by only counting primary void(s). In this case, char porosity directly obtained from automated char image analysis, which can detect char voids greater than 0.5 µm in diameter in this study, can be used to determine the diffusion of gas-phase components in the course of char burning. 4. Results and Discussion Burnout History and Char Type. The relationship between coal petrography and subsequent char type is reasonably well (34) Levenspiel, O. Chemical Reaction Engineering, 3rd ed.; Wiley & Sons: New York, 1999; Chapter 25. (35) Smith, J. M. Chemical Engineering Kinetics, 3rd ed.; McGrawHill: Auckland, 1981.

1400 0.43

1500 0.46

1600 0.50

1700 0.53

1800 0.57

1900 0.61

2000 0.65

understood and documented.13,23,33 Porosity is one of the defining characteristics used to classify chars; for example, some chars have porosities over 80% (tenuispheres), and some chars can have porosities less than 5% (solids).36 Char density can be calculated once the porosity of individual char is found (see Table 3). As shown in Table 3, the large variation in char size and its porosity dictates that different chars should follow different trends to be burned out due to the influence of its structure on heat and mass transfer during the course of char combustion. The burnout histories for different char types in different size ranges are shown in Figure 4. It is obvious that different char types have unique burnout histories. Generally, thin-walled chars take the shortest time to burn out (of the three types adopted in this model). The comparison between the burnout curves of thinwalled chars of El Cerrejon, Kellingley, and Welbeck demonstrates that even for chars of the same type, the burnout histories can still be different (Figure 4). One explanation is that those chars have different carbon conversion kinetics caused by the structures of chars being different from each other in aspects such as particle size and its distribution and porosity. It is expected that some solid particles will not be completely burned out due to their dense structure. Normally, chars of dense structure have a low available reaction surface to oxygen. The diffusion of oxygen from bulk flow to the inner side of the particle is relatively more difficult than for other char types. It therefore takes a longer time to burn out. It seems logical that the more reliable and detailed the char morphology model, the more accurate char burnout prediction. In this study, the main purpose of char burnout modeling is to test the possibility and approach to include char morphology into a burnout model. Only three major char typessthin-walled, thick-walled, and solid charsswere used to represent chars of different morphology in this study. Char Burnout Modeling Results. Coal proximate/ultimate analysis data were used to simulate a pf boiler gas and temperature environment. Table 6 shows the model predictions together with experimental burnout data collected from a DTF. (36) Char Atlas; Commission III. International Committee for Coal and Organic Petrology: 2003.



Figure 4. Comparison of burnout histories of different char types.

Figure 5a shows the comparison between experimental burnout data and the ChBa model predictions (using actual char morphology). Figure 5b shows plots of burnout data against the ChBp model predictions (using predicted char morphology). It is clear that both ChBa and ChBp are in reasonable agreement with experimental data collected from DTF experiments. However, there are not too many differences between ChBa and ChBp. It seems that ChBa and ChBp generally have better agreement with experimental data than when using the CBK model using the same conditions as the ChB model but excluding the char morphology input (shown in Figure 5c). The predicted char burnout rate appears to be faster at the early stages, which leads to higher burnout than the experimental data at residence time of 200 ms. At the late stage of char combustion, the rate of char burnout is slightly lower than the actual rate observed in DTF. For most coals, the carbon burnout kinetic model (CBK) overpredicted the burnout efficiency, but for La Jagua coal, it gives a better prediction than the model developed in this study. Generally, the ChB developed in this study is able to improve the accuracy of char burnout prediction compared to other combustion models with char data input. However, some discrepancies do exist, and there is clear room for the development of the model. Dependence of Char Burnout on Char Chemical Kinetics. As show in Table 6, predictions for most coals are in good agreement with experimental data, such as Guasare and Oreganal coal. Predicted carbon conversion for some other coals, such as Collinsville, Island Creek, and Stratford, is generally lower than observed values, while the predictions for the rest of the coals are normally greater than experimental results. The introduction of char morphology may be one of the reasons to explain the deviations. Another possible explanation is that the universal combustion kinetics37 adopted in this study may not

be able to give a precise prediction of char reaction rate. Coda and Tognotti38 suggested a size-dependent kinetics for char combustion at high temperature. However, the kinetics is based on just one coal, and further tests on a wider range of coals are needed to validate the equation. The introduction of three-step kinetics into the CBK/E14,31 would improve the description of the basic trends in global order, global activation energy, and CO/CO2 ratio over a wide range of combustion conditions. Nevertheless, to improve the prediction of char burnout, much more reliable kinetic expressions for coals in a wide range should be included in the char burnout kinetic model. 5. Conclusions In this study, a char burnout kinetic model, based on the fundamental concepts of the CBK,15 was developed. However, it differs by the inclusion of a submodel to consider the influence of char morphology on mass and heat transfer during the course of char burnout. A carbon burnout kinetic model32 was modified and applied to the coals in this work. The comparison between the carbon burnout kinetic model predicted burnout results, and the DTF experimental data show that the model can be used to predict char burnout, especially the late stage of char combustion. However, discrepancies were found between model predictions and experimental data for some coals. Therefore, it is possible to improve the burnout model by introducing new or by refining (37) Hurt, R. H.; Mitchell, R. E. Unified high-temperature char combustion kinetics for a suite of colas of various rank. In Twenty-Fourth Symposium (International) on Combustion, Sydney, Australia, July 5-10, 1992. (38) Coda, B.; Tognotti, L. The prediction of char combustion kinetics at high temperature. Exp. Therm. Fluid Sci. 2000, 21, 79-86.


Wu et al.

Table 6. ChBa and ChBp Predicted Burnout versus DTF Experiments carbon conversion % (char basis) 53-75 µm coal origin Collinsville

experimental ChBa ChBp El Cerrejon experimental ChBa ChBp Guasare experimental ChBa ChBp Island Creek experimental ChBa ChBp Kaltim Prima experimental ChBa ChBp Kellingley experimental ChBa ChBp Klein Kopje experimental ChBa ChBp La Jagua experimental ChBa ChBp La Loma experimental ChBa ChBp Middelburg experimental ChBa ChBp Oreganal experimental ChBa ChBp Pocahontas experimental ChBa ChBp Reitspruit experimental ChBa ChBp Stratford experimental ChBa ChBp Welbeck experimental ChBa ChBp

106-125 µm

200 ms 400 ms 600 ms 200 ms 400 ms 600 ms 41.0 35.8 39.3 70.0 85.9 94.1 64.0 83.3 84.3 54.0 58.1 70.0 72.0 88.8 90.9 73.0 75.8 88.1 50.0 48.5 53.9 81.0 90.3 96.3 88.0 94.4 93.4 49.0 53.1 58.5 76.0 94.7 94.8 59.0 72.9 71.9 55.0 60.7 74.5 57.0 51.5 65.5 75.0 87.0 90.4

59.0 57.1 54.8 95.0 93.7 97.0 81.0 94.0 91.1 88.0 81.1 83.7 95.0 95.7 97.4 96.0 93.5 94.2 73.0 73.5 72.8 96.0 95.0 97.5 99.0 96.8 98.0 73.0 75.6 75.0 96.0 96.5 97.9 90.0 89.2 89.0 82.0 84.6 87.1 81.0 78.1 82.1 96.0 92.4 94.6

66.0 69.2 63.0 98.0 95.0 97.4 93.0 95.7 92.0 93.0 89.0 88.4 99.0 96.5 98.8 98.0 97.8 96.6 84.0 83.9 82.0 99.0 97.5 97.9 99.0 98.2 99.0 83.0 85.2 82.0 99.0 97.7 98.3 95.0 93.2 93.3 89.0 91.5 89.8 88.0 88.0 89.5 99.0 95.5 95.5

36.0 24.0 25.4 62.0 79.0 76.0 49.0 45.6 58.5 50.0 40.1 41.1 65.0 84.3 73.3 51.0 78.9 73.4 47.0 46.7 40.1 72.0 94.3 86.0 78.0 89.0 78.0 47.0 38.3 41.9 74.0 80.4 77.5 39.0 78.2 54.2 53.0 64.7 53.8 52.0 53.9 50.7 58.0 90.7 65.2

48.0 43.5 43.4 87.0 93.1 89.7 68.0 67.2 82.7 76.0 67.5 65.1 93.0 94.9 92.2 92.0 90.0 88.6 70.0 72.4 64.0 91.0 97.5 95.9 98.0 94.3 94.1 72.0 62.1 67.5 93.0 92.5 93.1 80.0 93.5 75.9 78.0 82.1 76.9 74.0 80.0 73.0 84.0 92.7 85.8

60.0 57.9 55.9 95.0 94.4 92.1 78.0 78.9 92.5 88.0 81.5 77.5 97.0 97.5 97.8 93.0 93.8 92.7 80.0 83.3 76.2 97.0 97.7 96.6 99.0 96.4 97.9 81.0 75.2 80.1 98.0 95.9 95.1 89.0 94.7 82.7 85.0 88.7 85.0 83.0 91.2 83.6 95.0 94.3 92.4

existing submodels. Improving a combustion model can be achieved by using more reliable chemical kinetics, which has been done to improve the carbon burnout kinetic model by introducing a three-step kinetic model;14 and improving the simulation of mass and heat transfer outside and inside char particle by introducing real char data. The inclusion of different char types in the char burnout model has been tested, and the results show that different chars burn out in markedly different ways due to the significant difference in their geometric structures. It demonstrates that introducing char morphology information can give a better description of mass and heat transfer and, therefore, improve burnout predictions. Two sets of char morphology datasactual char morphology and predicted char morphology dataswere used as inputs to the ChB model. Burnout predictions that consider the impacts of char morphology on heat and mass transfer generally agree well with DTF experiments. There is not much difference between char burnout data predicted using actual char morphology data or predicted char morphology data especially when considering three main char types: thin-walled, thick-walled, and solid. The ChB model developed in this study has the potential to predict coal burnout based on microscopic coal

Figure 5. (a) Comparison between ChBa and experimental data. (b) Comparison between ChBp and experimental data. (c) Comparison between CBK/E and experimental data (model without char morphology input).

analysis by combining automated coal image analysis with its char prediction. However, some further developments are still needed to improve the prediction. Larger scale burnout trials are underway to test the system across the full particle size range and longer burnout times. Acknowledgment. We are grateful for the financial support from BCURA and PowerGen. The views expressed are those of the authors and do not necessarily represent those of the funding bodies. We also acknowledge Professor Robert Hurt at Brown University for his help and generous provision of CBK/E codes.

Nomenclature ChBa ) char burnout model with actual char morphology data as inputs

Burnout Prediction Model Based around Char Morphology ChBp ) char burnout model with predicted char morphology data as inputs DAB ) bulk diffusion coefficient of A in a mixture of A and B, m2/s De ) effective diffusion coefficient in porous structure, m2/s Dk ) Knudsen diffusion coefficient, m2/s P ) pressure, pa r ) radius, m Tm ) mean value of the gas and the particle temperature

Energy & Fuels, Vol. 20, No. 3, 2006 1183 φ ) char porosity Subscripts crit ) critical M ) macropore p ) particle or pore µ ) micropore EF050101O

A Burnout Prediction Model Based around Char Morphology - Energy

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