An extended coal combustion model

An extended coal combustion model

Fuel 78 (1999) 1745–1754 An extended coal combustion model R.I. Backreedy, R. Habib, J.M. Jones, M. Pourkashanian, A. Wi...

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Fuel 78 (1999) 1745–1754

An extended coal combustion model R.I. Backreedy, R. Habib, J.M. Jones, M. Pourkashanian, A. Williams* Department of Fuel and Energy, The University of Leeds, Leeds LS2 9JT, UK Received 11 December 1998; received in revised form 13 May 1999; accepted 26 May 1999

Abstract Current coal combustion models are a useful tool in research but they use simplified coal particle devolatilisation and combustion steps in order to meet computational limitations. The availability of more advanced computers enables the use of more detailed steps for devolatilisation and the use of more realistic char combustion processes. In the present work the devolatilisation rates were calculated using the coal devolatilisation model FG-DVC. In this way devolatilisation rates and the yields of char and volatile were obtained. A drop tube reactor was operated at 1623 K to collect char samples, from Thoresby coal, at different sampling positions or residence times, and proximate and ultimate analysis were conducted on these char samples to confirm the results. The detailed char combustion sub-models being developed for CFD codes require char properties such as densities, surface areas and pore size distributions but a simplified model was used here. In this case the use of a simple global char oxidation model together with an empirical derived ‘volatile’ and FG-DVC predicted devolatilisation rate data seem to give good agreement with the experimental results available for the char burnout. However, there still remains considerable uncertainty in the use of char burnout models including the one used here is not sufficiently accurate in predicting carbon burnout in all conditions. q 1999 Elsevier Science Ltd. All rights reserved. Keywords: Bituminous coal; Devolatilisation; Combustion modelling

1. Introduction One of the main objectives in coal combustion research is the development of comprehensive computer models to help design combustors and gasifiers for the clean utilisation of coal usually in complex burners and combustion chambers. The CFD models need to be solved for fluid flow, turbulence, particle trajectory, heat transfer and chemical reactions of the fuel and the formation of pollutants. Performing these tasks computationally is expensive with respect to processing time and resources, and so the combustion models are greatly simplified. In most current CFD codes the process of modelling coal combustion is usually simplified [1–3] to the following reactions: 1. 2. 3. 4.

Coal ! Char 1 Volatiles Volatiles…HC† 1 O2 ! CO 1 H2 O CO 1 1=2O2 ˆ CO2 fC…char† 1 O2 ! 2…f 2 1†CO 1 …2 2 f†CO2

where f is the equivalence ratio. The first step after the coal particle has been heated to an appropriate temperature is * Corresponding author. Tel.: 1 44-113-233-2508; fax: 1 44-113-2440572. E-mail address: [email protected] (A. Williams)

devolatilisation and the overall kinetic steps can be simplified using a first order global rate with a single Arrhenius expression: dV ˆ kv …V p 2 V† dt


kV ˆ AV exp…2Ev =RTp †



where V is the total volatile yield, Tp the particle temperature, kv the rate constant for devolatilisation and t the time. On integrating Eq. (1) it is possible to obtain the fractional volatile yield as a function of time:  Zt  V ˆ 1 2 exp 2 k dt …3† v Vp 0 In most computational codes the “volatiles” are represented as a single species, commonly propane, for which the combustion rate (steps 2 and 3) is controlled by mixing with an oxidant and is assumed to oxidise CO to CO2. In terms of stoichiometry, ignition and flame temperature this can lead to inaccuracies. So it is necessary to develop a code which allows for the representation and use of more realistic volatile species i.e. one where the inclusion of light, medium and heavy tars, paraffin and olefins is possible. Failing this, an attempt could be made to represent the volatile species as a hydrocarbon (CxHy) or CxHyOn where

0016-2361/99/$ - see front matter q 1999 Elsevier Science Ltd. All rights reserved. PII: S0016-236 1(99)00123-4


R.I. Backreedy et al. / Fuel 78 (1999) 1745–1754

Table 1 Thoresby coal ultimate and proximate analysis Ultimate

Coal (wt% daf) CV (db) ˆ 28 540 kJ/kg







Moisture (wt% ad)

Volatiles (wt% daf)

Fixed carbon (wt% daf)

Ash (wt% ad)










the x : y : n ratios are similar to the parent coal’s volatile (i.e. tars, gases and paraffins and olefins) ultimate analysis. The last step is the combustion of the char. This is dependent on a number of parameters such as temperature, oxygen, concentration, and residence time as well as char reactivity. This is a much slower process than devolatilisation and determines the burnout time of pulverised coal particles in the furnace. The char oxidation can be modelled in two ways. That is either as a first order global reaction or in a way that includes the diffusion of O2 within the pores of the char particle. Char combustion occurs at different rates in three regimes: Chemical control (low temperature, zone 1), where the oxygen concentration within the pores is equivalent to that in the bulk phase; internal diffusion control (zone II) where the oxygen concentration decreases to zero within the particle; and external diffusion control (high temperature, zone III) where the oxygen concentration is zero at the particle surface. Many CFD char combustion sub-models use the Baum and Street model [4] which includes both chemical and diffusion reaction rate terms. In these expressions the rate of mass loss by combustion depends on particle density, diameter, and the ratio of reacting surface to external surface area of the particle: XO2 21 dmc 21 ˆ 2pD2p rRT …R 1 R21 c † dt MO2 diff


where Rc ˆ Af w exp…2Ea =RTp † and Rdiff ˆ

2dDO M c RTO Dp

Tp 1 Tg 2

…5† 0:75


Where mc is the mass of the coal particle, Dp the diameter of the particle, DO is the diffusion coefficient of the oxygen in the gas, r is density of the carbon, R is the universal gas constant, Tp and Tg are the temperatures of the particle and the gas, XO2 is the oxygen mole fraction, M the molecular weight of the species, Rdiff the diffusion reaction rate coefficient, Rc the chemical reaction rate coefficient per unit external surface area, Af the empirical constant for the fuel, w the ratio of reacting surface to external (equivalent sphere) surface area of particle and Ea the activation energy. As porous char formation and char structure influence the combustion properties [5,6] it would be useful to be able to model these. Therefore to refine the char burnout model it

should ideally include parameters that reflect the influence of coal structure and maceral effects on porosity, fragmentation, as well as reactivity of chars produced under different temperature–time histories. 1.1. FG-DVC devolatilisation sub-model There are a number of commercial computer codes, FGDVC, FLASHCHAIN and CPD [7] that predict the rate of the volatile release and the composition of key species. In this investigation the applicability of FG-DVC is studied. FG-DVC (functional group-depolymerisation vaporisation cross linking) is a two stage program designed to predict the rates and yields of all the major gas species and the yields, molecular weight distributions and elemental composition of the tars and char from a coal undergoing pyrolysis [8]. The FG model simulates the product evolution from the various functional groups, and the DVC model predicts the depolymerisation, vaporisation and cross-linking processes occurring in the coal polymer network. Gas evolution from functional group precursors is modelled with parallel first order differential equations, and a distributed activation energy formulation is used to reflect the diversity of the coal structure. A network model is used to model the thermal evolution of the coal polymer matrix. This consists of nodes, which are represented by the polymer clusters, and the two types of connections between them i.e. bonds and cross-links. The most important property of the network is the molecular distribution of the clusters. The heavy molecules remain in the condensed phase to become char, whilst the light ones evaporate to become tar. The vaporisation is calculated based on a mechanism given by Fletcher et al. and the method described in reference [8]. FG-DVC uses a set of well-defined library coals in a van Krevelen diagram and the properties of an unknown coal can be determined by interpolation. In this study chars were prepared from coals in a drop tube furnace operating at about 1623 K. Under pyrolysis conditions, char samples were collected and analysed and the results compared to those from the FG-DVC sub-model [7] for coal pyrolysis. The outputs of the FG-DVC sub-model were used as an input to the CFD coal combustion model. A comparison was made of the outputs from the CFD model of the drop tube furnace (DTF) under combustion conditions with the available experimental data.

R.I. Backreedy et al. / Fuel 78 (1999) 1745–1754


P re Heater

Injec tion P rob e Dia 8mm

N2 1673 K

High Te mp Le ngth 550 mm

1373 K S a mplingP os n. 1

S a mpling Pos n. 2 1223 K

Low Te mp. Le ngth 15 00mm Furnace dia. 39mm

S a mplingP os n. 3

S uppo rting Ba s e S a mpling P rob e Coo ling Wa te r

Fig. 1. Schematic diagram of the drop tube furnace (DTF) showing gas sampling ports and moveable char sampling probe.

2. Experimental method A UK coal, Thoresby, with elemental and proximate analysis as shown in Table 1 and an average particle size of about 60 mm, was used as received in pulverised form in the DTF experiments. The coal was placed in a vertically clamped vibrating fluidiser bed through which nitrogen was passed so as to entrain it through the inner annulus of an 8 mm cylindrical injector and into the top of the 39 mm i.d. drop tube. The high temperature upper zone of the drop tube was maintained at 1623 K. The top of the low temperature zone was set at 1373 K and the bottom of the low temperature zone was set at 1273 K for all conditions investigated and the O2 concentrations were set to 0, 10 or 21 mol% in the DTF atmosphere. A gas sampling silica tube was inserted horizontally through the three ports shown in Fig. 1 and connected to an on-line NOx, CO, CO2, UHC and O2 analysers. The char samples were collected at the three port positions in the DTF, sealed under an N2 atmosphere and analysed in the laboratory for proximate (using TGA instrument) and ultimate (C,H,N) compositions.

3. Modelling method CFD models use one of two-combustion modelling approach [9]. The first uses a generalised finite rate chemistry formulation that is based on the solution of species transport equations for reactant and product concentrations.


The reaction rate appears as source terms in the species transport equations and is computed from Arrhenius rate expressions or by using the eddy dissipation concept [9]. When using this chemistry model, the volatile matter released by the coal can only be modelled as a single species. For example, the ‘volatile’ species were represented as either ‘C2H6’ or empirically by ‘C4H13O1.4’ from calculations that assumes the char to be 100% carbon (C). That is, the volatiles were assumed to be similar to the parent coal’s H, O, N, S content and the volatile C content was taken as the difference between the coal’s C content and the char’s C content, known from the coals proximate analysis, i.e. the fixed carbon content, on a dry ash free (daf) weight percentage basis. An alternative empirical formula (C7.2H10.2O) for the ‘volatile’ species was calculated using the predicted volatiles (i.e. tar, gas, paraffin and olefins) percentage yields and ultimate composition from FG-DVC which used as a starting point the coal’s ultimate analysis as inputs. When empirical formulas are used in CFD models it is necessary to calculate the heat of formation of the fuel species used to represent the combustibles or volatiles in the continuous or gas phase. This can be donePfrom the known heating value, DH, because DH ˆ p h0p vp 2 P 0 R hR vR where p represents products of volatile combustion and R the reactants or volatiles, v the molar stoichiometry coefficient and h 0 the formation enthalpy. The heating value, DH, of the volatile is computed by the difference in the calorific values of the coal and char on a dry basis. The volatile molecule is assumed to react with the oxidant at a rate determined by the finite rate chemistry model to produce nCO and mH2O. The CO then continues to react to give CO2. A different molecular weight was calculated for each of the volatile species and the remaining char was assumed to be pure carbon. Using this approach means that the volatile molecular composition varies with volatile yield, since the amount of carbon within the molecule depends on how much char is assumed, whilst the hydrogen and oxygen within the volatile molecule are assumed to be the same as parent coal’s hydrogen and oxygen content. The second volatile combustion modelling approach is the mixture fraction/PDF chemical equilibrium modelling method [9]. Transport equations for individual species are not solved in this approach. Instead, individual component concentrations for the species of interest are derived from the predicted mixture fraction distribution. The reacting system uses either chemical equilibrium calculations or infinitely fast chemistry. In a similar manner to the finite rate chemistry model, all the elemental hydrogen and oxygen and the fraction of carbon within the coal is released during devolatilisation when the PDF chemistry model is used, however, these are released as separate elements. Pre-processed lookup tables are used to establish the chemical equilibrium conditions of elements within a particular region of the flame and to establish enthalpy sources. Equilibrium is assumed to


R.I. Backreedy et al. / Fuel 78 (1999) 1745–1754

Table 2 FGDVC’s predicted tar (Thoresby only) and global devolatilisation rates at 10 5 K/s for 150 ms for the eight DTI coals Coal

Thoresby Tar rate ! Pittsburgh Koonfonteine La Jagua Hunter valley Betts Lane Asfordby Kaltim Prima

Global rate

W∞ (daf)

Ea (J/mol)

A (s 21)

6:41 × 104 7:8 × 106 6:97 × 104 4:93 × 104 6:18 × 104 5:86 × 10; 7:12 × 104 6:06 × 104 5:11 × 104

5:95 × 104 8.3 × 104 9:19 × 104 4:55 × 104 4:57 × 104 2:67 × 104 7:38 × 104 2:54 × 104 1:27 × 104

k (s 21) at 1273 K

at 1773 K




46.5 45.4 49.7 46.2 52.5 48.4 50.8

127 115 104 75.5 88.4 82.8 92.6

812 690 500 484 589 416 432

Fig. 2. Total weight loss for devolatilisation at 10 5 K/s–1623 K for 150 ms predicted by FG-DVC. (a) Weight loss vs. time (b) Weight loss vs. temperature. . Betts Lane, Kaltim Prima, Pittsburgh #8, Thoresby, Hunter Valley, La Jagua, Koonfonteine, Asfordby, South Brandon.

R.I. Backreedy et al. / Fuel 78 (1999) 1745–1754


Fig. 3. Total weight loss for devolatilisation at 10 5 K/s–1623 K for 150 ms predicted by FG-DVC. (a) Total rate vs. time and (b) total rate vs temperature. Legend as in Fig. 2.

exist at the molecular level except at the very fuel rich zone where partial equilibrium is assumed. For char combustion the Baum and Street model, described in Eqs. (4)–(6), is used, which includes both chemical and diffusion reaction rate terms. A commercial CFD software package was used to model the drop tube furnace (described in the experimental section), in 2D and with axis-symmetric co-ordinates. The fuel used was Thoresby coal with an average particle size of 60 mm, and air, containing 21, 10 or 0 mol% O2, was the oxidant. A structured grid with 30 × 70 cells was used for the calculations, resolution was found to be independent of cell dimensions, and solutions were grid independent. The global and tar devolatilisation rates inputted, as shown in Table 2, were calculated from FG-DVC’s predicted weight loss time curves for a heating rate of 10 5 K/s with the final particle temperature of 1623 K. Volatile combustion was simulated using either: (1) C2H6; (2) empirical volatile species C4H13O1.4 (referred to as ‘empirical volatile’); or (3) empirical volatile species calculated from FG-DVC’s

output, C7.2H10.2O, (referred to as ‘FG-DVC’s empirical’) to represent the volatiles released from the coal particles; together with the finite rate and PDF/chemical equilibrium chemistry models. In all the cases, as detailed in Table 6, the char kinetic rate constants used in the Baum and Street burnout sub-model [4,9] in CFD package were; A ˆ 4:235 × 1024 kg=m2 =s=Pa and Ea ˆ 56:0 MJ=mol [10].

4. Results and discussion 4.1. Devolatilisation In this study several coals used in a study of power station coals by the UK Department of Trade and Industry (DTI) were modelled using FG-DVC under conditions corresponding to those used in the drop-tube furnace, i.e. from 10 5 K/s to 1623 K at a residence time of 150 ms. The FG-DVC output contains rates of production and yields for a number of species including tar, char, gas


R.I. Backreedy et al. / Fuel 78 (1999) 1745–1754

Table 3 DTF experimental (Thoresby only) and FGVDC’s predicted char yield and ultimate analysis for coals and conditions as in Table 2 (n.m.: not measured) Product

Thoresby coal Char(FG-DVC) DROP TUBE Pittsburgh 8# coal Char(FG-DVC) Koonfonteine coal Char(FG-DVC) La Jagua coal Char(FG-DVC) Hunter valley coal Char(FG-DVC Betts Lane coal Char(FG-DVC) Asfordby coal Char(FG-DVC) Kaltim Prima coal Char(FG-DVC)

(wt% daf) Distribution





S (T)

100 47.4 45 100 46.5 100 45.4 100 49.7 100 46.2 100 52.5 100 48.4 100 50.8

82.30 95.97 93 82.60 96.54 82.49 96.92 80.09 96.55 83.04 96.37 86.80 94.83 80.92 94.65 79.80 94.71

4.79 0.19 0.4 4.94 0.20 5.07 0.29 5.62 1.12 5.42 1.18 5.38 1.40 6.48 2.78 6.14 2.01

8.52 0.00 n.m 8.80 0.00 9.78 0.00 12.00 0.00 9.19 0.01 3.01 0.00 10.08 0.02 10.50 0.00

1.86 2.17 1.9 1.64 1.92 2.02 2.36 1.55 1.82 1.84 2.11 1.70 1.88 1.55 1.89 1.83 2.10

2.53 1.66 n.m 2.02 1.33 0.64 0.43 0.74 0.51 0.52 0.34 3.11 1.89 0.97 0.66 1.73 1.17

(CO, CO2, H2O, CH4, HCN, NH3) as well as total weight loss and rates as shown in Figs. 2 and 3, respectively. The FG-DVC model was used to calculate the global first order rates for all volatile species and the tar first order devolatilisation rates (only for Thoresby coal) for a heating rate of 10 5 K/s–1623 K for 150 ms and these are tabulated in Table 2. In addition the elemental analysis of the chars produced were predicted by FG-DVC and these results are compared in Table 3 with those of Thoresby char samples collected

from drop tube experiments. The volatile species as predicted by FG-DVC are separated into gas …CH4 1 H2 1 CO 1 CO2 1 H2 O 1 HCN 1 NH3 1 COS 1 SO2 1 H2 S†; tar and paraffin and olefins that could be considered as either light tars or gases. These coals volatile compositions are shown in Table 4. From the FG-DVC output it is also possible to calculate an empirical formula for the volatiles, shown in Table 5, from the devolatilisation of each coal. This calculation is based on the volatile yields of tar, gas, paraffin and olefins together with their ultimate compositions. The theoretical models, such as the FG-DVC devolatilisation sub-model, are now an established technology capable of producing accurate estimates of the yields of volatiles (gases and tars) and char (including char ultimate composition as seen in Table 3) and the fuel nitrogen distribution between the volatiles and char as shown in Figs. 4 and 5, respectively, where the predicted char, tar and gas yields for the range of coals are in good agreement with the experimental data obtained under pyrolysis conditions. In addition the prediction of the tar enables an estimate of the soot concentration to be deduced on the basis that the major part of the soot comes from the tar [11]. On the assumption that ‰SOOTŠ ˆ ‰TARŠ and that the number density of the soot is about 1016 particles=m3 enables the soot contribution to the coal flame radiation and the flame temperature to be computed. 4.2. Char burn-out At present, the char oxidation modelling within most

Table 4 FGDVC’s predicted volatile composition (conditions as in Table 2) Coal










Thoresby Pittsburgh #8 Koonfonteine La Jagua Hunter Valley Betts Lane Asfordby Kaltim Prima

1.58 1.68 1.71 1.88 1.58 1.57 1.56 1.88

5.43 5.27 5.06 3.96 4.80 5.99 5.77 3.86

2.92 3.25 3.81 5.53 4.21 1.53 6.96 4.96

0.67 0.77 0.90 1.49 1.09 0.34 2.11 1.35

2.18 2.27 2.36 2.50 2.48 2.00 2.00 2.50

1.66 1.66 1.81 2.13 1.57 1.50 1.50 2.00

7.08 7.1 7.79 8.71 6.72 2.01 5.08 7.46

0.31 0.28 0.38 0.37 0.35 0.27 0.27 0.45

0.05 0.05 0.06 0.05 0.06 0.02 0.02 0.05

Table 5 FG-DVC’s predicted % volatile yield and derived ‘volatile’ empirical formula for conditions as in Table 2 Coal

% volatile yield (daf)

Empirical formula of ‘volatiles’

H/C ratio

Thoresby Pittsburgh #8 Koonfonteine La Jagua Hunter Valley Betts Lane Asfordby Kaltim Prima

52.6 53.5 45.0 55.2 54.4 47.5 51.6 59.5

C7:2 H10:2 O C7:2 H10:3 O C6:6 H9:5 O C4:6 H8:0 O C7:1 H10:1 O C21:3 H29:3 O C5:6 H8:8 O C5:2 H9:3 O

1.4 1.4 1.4 1.7 1.4 1.4 1.6 1.8

Char, Gas and Tar Yield, % daf

60 Char

50 40 Tar

30 Gas

20 10 0 65


75 80 85 % Carbon Content of Coal (daf)


Fig. 4. FG-DVCs predicted and Experimental points [26–28], closed symbols, of char, gas and tar yields for devolatilisation of coals of varying rank. Conditions: heating rate of 10 5 K/s–1623 K for 150 ms.

CFD codes assume that the char is pure carbon and the oxidation reaction occurs at the surface. The reaction rate is limited by the diffusion of oxygen to the particle surface and by the effective char reactivity described by an Arrhenius type temperature dependent expression. The simple global char oxidation models discussed previously cannot predict the low reactivity observed at high carbon conversion and therefore the carbon burnout cannot be predicted accurately [12]. In order to overcome the uncertainties in char combustion modelling, an advanced coal combustion model could include statistical kinetics for carbon burnout analysis developed recently [5,6,13]. This model describes the variation in char reactivity as variations in the single particle global pre-exponential factor with an apparent activation energy and reaction order held constant across the particle population. A gamma distribution function is used for the analytical approximation of the reactivity distribution function [13]. However, despite extensive investigation, the mechanism of the char-oxygen reaction is not yet completely understood because of many factors such as fast reaction due to pore growth and mass transfer effects. Hence an empirical approach has been adopted [14] where the chemical reactivity of the char is estimated based on the external surface N in Char, Gas and Tar, %daf

80 70 60 50 40 30 20 10 0





70 75 80 85 Carbon Content of Coal, (daf)

Char Global Activation Energy,Ea (kJ/mol)

R.I. Backreedy et al. / Fuel 78 (1999) 1745–1754


160 140 120 100 80 60 40 20 0 60







Fig. 6. Global activation energy, Ea, for experimental and literature char oxidation data: V Ea (Shell); B Ea (Tsby_ABB); O Ea(BtsL_ABB); × Ea(Asby_ABB); p Ea(Sandia); Ea(IFRF); X Ea(Pts_Sandia); 1 Ea(Pits_IFRF); —Ea(IFRF_rc); S Ea(TGA_expt) and —— Sandia correlation line. Shell ([16]), Sandia ([6]), IFRF ([17,18]), ABB ([10]). Thoresby coal (Tsby), Betts Lane (BtsL), Asfordby (Asby) and Pittsburgh (Pts), rc (most recent values received, [18]) and extp.(data extrapolated from low temperature TGA experimental, [21]).

area of the char particle. Moreover, Lunden et al. [15] have proposed that the global reactivity, qg is related to the intrinsic reactivity qi by the square root of the available surface in zone II combustion. As the combustion proceeds the concentration of mineral matter on the surface of the char particle increases and there is a decrease in global reactivity as a function of the char carbon conversion. There still remains considerable uncertainty in the use of char burnout models. The value of the activation energy, E, and especially the pre-exponential factor, A, vary enormously in the literature as shown in Fig. 6 and generally the values used for a particular CFD application are based on their experimental determination (or of a similar coal) in a drop tube. This permits reasonably accurate estimates of the main part of char burnout but is insufficiently accurate to predict unburned carbon. The values used here for the char kinetics in the CFD model are based on experimental data for the Thoresby coal [10] and is consistent with data obtained by Sandia [6], Shell [16] and IFRF [17,18] and is plotted in Fig. 6 as a function of coal carbon content (daf). Generally, correlations for the activation energy tend to be of the type where the coal and char properties, such as surface area, porosity, density, C, H or O content (which gives an indication of coal rank) and particle temperature are taken into account. For example, Hurt and Mitchel [6] proposed that the activation energy, E, is; E…kJ=mol† ˆ 224:83 1 1:48…wt: coal carbon daf†


Fig. 5. FG-DVCs predicted and experimental, closed symbols, nitrogen partitioning between char, gas and tar for various coals. Conditions as in Fig. 4.


Carbon Content of Coal (%wt,daf)


and for Thoresby coal this gives a value of about 97 kJ/mol. While Hampartsoumian et al. [19] found that, Rc ˆ exp…289† sa…27:5† Ag…20:5† C…3:5† Tp…9:5†


where Rc is the chemical reaction-rate coefficient per unit


R.I. Backreedy et al. / Fuel 78 (1999) 1745–1754

Fig. 7. CFD predictions of char burnout along the DTF for 10 vol% O2 atmosphere and tar devolatilisation rates. Case 1: Finite rate with C2H6. Case 2: Finite rate with empirical as ‘volatiles’. Case 3: Finite rate with FGDVCs computed empirical as ‘volatiles’. Case 4: Mixture fraction/PDF with C2H6.

external surface area (g/cm 2 s (atm) n), s a the apparent density of the char (g/cm 3), Ag the specific pore surface area of the char (cm 2/g), C the carbon content of the coal on a wt% (daf) basis and Tp the surface temperature of the particle in K; similarly Charpenay et al. [20] suggested that rp ˆ exp…1:4Hchar † exp…0:263Ocoal †S × 8:22exp…215 000=T† …9† where rp is the reaction rate (l/s), Hchar the wt% (daf) hydrogen in the char, Ocoal the wt% (daf) oxygen in the coal, S the internal surface area of remaining char per mass of initial char (m 2/g) and T the particle temperature in K. Additional experimental points were obtained for the low temperature combustion of chars that were produced by our drop tube and burned using TGA yielding activation energies [21], E, for Pittsburgh 8, 178 ^ 10; Asfordby 283 ^ 33; Thoresby 182 ^ 8 and South Brandon 179 ^ 5 KJ=mol: For high temperature combustion conditions, the low temperature activation energy can be extrapolated to yield an apparent high temperature activation energy of 1/2E which leads to values of 89, 142, 91 and 90, respectively, for the above four coals and these values are also plotted in Fig. 6. Clearly the activation energy is an approximate

function of carbon content but it also depends on the density and porosity [1], thus there will be some scatter since these factors are not taken into account in this paper. A further indication of the reaction rate can be obtained from the high temperature data of Smith et al. [22] and Hargrave et al. [23] which gives values for the diffusion corrected (chemical) high temperature activation energies of 179.4 and 161.5 kJ/mol which relates to activation energies here of 89.7 and 80.8 kJ/mol. Al-Sameed et al. [24] also showed that the activation energies for char burnout increased during the period of char burnout over the range of 68–110 kJ/mol. The overall effect of using the tar devolatilisation rate together with either a simple hydrocarbon species or an empirically derived/computed species to represent the ‘volatiles’ are shown in Fig. 7. It is clear that case 3 using the FG-DVC data and case 4 are close to experimental data as shown in Table 6. 4.3. Accuracy of the CFD model As discussed above, the FG-DVC data produces an accurate prediction from the coal properties to the char properties and yields and when used in the CFD model gives good results. This is shown to be true for Case 3 in Table 6 based on 100% carbon content of the char. And despite the fact that there seems to be some discrepancy in the devolatilisation rates, tar and global, predicted between models (as FGDVC predicts faster rates than the others) the CFD char burnout results predicted using the predicted FG-DVC devolatilisation rate data is in good agreement with the experimental results. As yet there is no devolatilisation rates validation in the literature for any of the models and more work is to be done in this area to address the issue of rates variation. As far as the pre-exponential factors, As, and the activation energies, E, are concerned there seems to be moderate agreement about the average value of the E as shown in Fig. 6. As discussed above, it has been shown that the chemical reactivity of char is a function of the surface area, carbon and hydrogen content [5,6,13–15,19,20]. The latter factor represents the degree of ordering of the carbon or the number of active sites. There is considerable scatter of the

Table 6 CFD modelling case matrix of the drop tube furnace using Thoresby coal in 10% or 21% mol O2 concentrations together with predicted and experimental char burnout results

‘Volatile’ species Combustion model used Devolatilisation rate used wt% Carbon Burnout CFD Predicted 10% mol O2 21% mol O2 Exp. Results for DTF at 1623K (at exit) 10% mol O2 21% mol O2

Case 1

Case 2

Case 3

Case 4

C2H6 Finite rate Tar, Global

‘Volatiles’ empirical Finite rate Tar, Global

FG-DVC’s empirical Finite rate Tar, Global

C2H6 PDF/equilibrium chemistry Tar, Global

67, 64 100, 100

80, 77 100, 100

78, 76 100, 100

87, 85 100, 100

80.6 99.3

80.6 99.3

80.6 99.3

80.6 99.3

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activation energies about the mean for a particular carbon content (wt%) and this seems to be dependent on the severity of the heat treatment of the char, i.e. either it has reacted for a significant length of time or has been subject to significant radiant flux. This is similar to the conclusions of Hurt and co-workers [25] on the annealing of unburned carbon in ash and the evidence here is that the activation energies increase as the reaction time increases and there is a distribution of activation energies. Further aspects of this work will be published in due course.

5. Conclusions Modelling coal devolatilisation as a first step in CFD coal combustion simulations can be done more accurately using programmes such as FG-DVC, which can predict accurate estimate of the yields of the volatiles and char and composition of the char, to generate the necessary input data for the CFD model. The char yield and composition and nitrogen partitioning between char and volatiles predicted by FG-DVC were in good agreement with the experimental data obtained under similar pyrolysis conditions. However, there seems to be some discrepancy in the devolatilsation rates predicted by the various models and more work is to be done to address this issue. In this case the use of a simple global char oxidation model together with an empirical derived ‘volatile’ and FG-DVC predicted devolatilisation rate data seem to give good agreement with the experimental results available for the char burnout. However, there still remains considerable uncertainty in the use of char burnout models and the simple global char oxidation models, including the one used here, cannot predict the low reactivity observed at high carbon conversion and therefore is not sufficiently accurate in predicting carbon burnout in all conditions. Advanced char combustion sub-models suggested to overcome these problems are ones that include statistical kinetics for the carbon burnout analysis and incorporates the effect of mineral matter on char combustion. The value of the activation energy, E, and especially the pre-exponential factor, A, vary appreciably in the literature and the values used for a particular CFD application are based on their experimental determination (or of a similar coal) in a drop tube. A number of researchers have proposed empirical relations that link the activation energy to the physical properties, such as porosity, density, surface area and ultimate analysis, of the coal and char and particle temperature. However, there is considerable scatter of the activation energies about the mean for a particular percentage carbon content and this seems to be dependent on the severity of the heat treatment of the char. Further aspects of this work will be published in due course.


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