Development of Sulfur Release and Reaction Model for CFD Modeling

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Development of Sulfur Release and Reaction Model for CFD Modeling in Sub-bituminous Coal Combustion Zhi Zhang, Denggao Chen, Zhenshan Li, Ningsheng Cai, and Junji Imada Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b02867 • Publication Date (Web): 06 Jan 2017 Downloaded from http://pubs.acs.org on January 8, 2017

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Development of Sulfur Release and Reaction Model for CFD Modeling in Sub-bituminous Coal Combustion Zhi Zhang, Denggao Chen, Zhenshan Li*, Ningsheng Cai Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China

Junji Imada Combustion No.1 Laboratory, Research & Innovation Center, Mitsubishi Heavy Industries, Ltd., Japan

*Telephone, 86-10-62789955. Fax, 86-10-62770209. E-mail address, [email protected]

Abstract Pulverized coal-fired boilers applying low-NOx combustion technologies commonly suffer from high-temperature corrosion due to high-concentration H2S. Accurate prediction of sulfur species, especially H2S, is of great importance for the optimized design and operation of boilers and burners to reduce such problems. The sulfur release characteristics from coal and subsequent sulfur species gas-phase reaction mechanism are two critical steps controlling sulfur species evolution. In this study, first, a global sulfur species gas-phase reaction mechanism consisting of ten reactions is proposed based on a detailed mechanism considering hundreds of elementary reactions. Kinetic parameters of the global mechanism are determined via a rigorous mathematical optimization process. Second, the sulfur release characteristics during coal pyrolysis and char burning of five kinds of sub-bituminous coals are investigated in a drop tube furnace (DTF). Equations describing the relationship between sulfur release and coal consumption are proposed and fitted to experimental data. Third, a novel integrated sulfur species prediction model is developed by implementing the global sulfur species gas-phase reaction mechanism and the sulfur release sub-model into CFD software, Fluent. Finally, combustion experiments of three kinds of sub-bituminous coals are conducted in the DTF at different 1

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temperatures with different stoichiometric ratios to validate the developed model. The results show that the prediction errors of sulfur species, including SO2, H2S and COS, are within ±25%, which indicates that the novel sulfur species prediction model is of great assistance for actual engineering applications.

Keywords: pulverized coal combustion; sulfur species prediction; sulfur species gas-phase reaction mechanism; sulfur release characteristics; CFD simulation

1. Introduction Currently, low-NOx combustion technologies, including fuel-staged and air-staged combustion, are widely applied in pulverized coal-fired boilers as a primary method to control NOx emission [1]

. The basic design strategy of low-NOx combustion is to establish fuel-rich zones where O2 is

depleted so that a subset of the formed NOx can be reduced

[2-3]

. However, some of the sulfur

released from coal is converted to H2S under the reducing atmosphere in such fuel-rich zones. It has been widely reported that high-concentration H2S accelerates fireside waterwall corrosion, especially in supercritical or ultra-supercritical boilers where the furnace temperature is extremely high

[4-7]

. For safe boiler operation, it is of urgent necessity to improve the design and operation

strategy to control H2S concentrations and reduce high-temperature corrosion. Therefore, it is of huge significance to accurately predict concentrations of sulfur species, especially H2S, in pulverized coal combustion via CFD simulation. Sulfur species evolution during coal combustion is a complex process controlled by sulfur release characteristics from coal and subsequent sulfur species gas-phase reactions. During pyrolysis, part of the sulfur in coal is released into the gas phase in the form of H2S, SO2, COS and CS2 while the rest of the sulfur remains in the solid phase. Generally, H2S is the main sulfur species generated during pyrolysis, especially when the heating rate is high 2


[8-11]

. During char

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burning, part of the remaining sulfur continues to be released into the gas phase, and the main product relies on the specific atmosphere

[12-14]

: SO2 is the main product during char oxidation

while COS and H2S are remarkable during char gasification. The relative fraction of sulfur species in the gas-phase relies heavily on the local atmosphere: under an oxidizing atmosphere where O2 is abundant, SO2 is the main product; under a reducing atmosphere where O2 is depleted, H2S and COS are the main products instead of SO2 [15, 16]. Regarding sulfur release characteristics, there is no evidence confirming that sulfur content in coal is uniformly distributed in volatile and fixed carbon. On the contrary, some researchers have attempted to build multi-step kinetic sulfur release models with the consideration of different sulfur forms

[17-19]

. A lot of tunable parameters are included in multi-step reactions while how to

determine their values are not clear. Therefore, such complicated sulfur release sub-models are rarely applied in actual CFD simulations. To describe sulfur species gas-phase reactions, a series of detailed mechanisms have been proposed, such as Leeds University sulfur mechanism mechanism

[21]

[20]

, University of Sydney sulfur

and Imperial College London sulfur mechanism

[22]

. The number of elementary

reactions considering in such detailed mechanisms varies from 70 to more than 270. However, despite the improvement in computational capabilities, the direct coupling of CFD simulation and such detailed chemistry is still prohibitive for real furnaces and burners, which are characterized by computational grids of huge number. Thus, in current CFD simulations of coal combustion, only very simple sulfur species gas-phase reaction mechanisms are adopted. For example, Müller etc. just used one global reaction to describe the oxidation of H2S by O2 in his simulation of sulfur species evolution during coal combustion

[23]

. The default sulfur species reaction mechanism in

commercial CFD software, Fluent, is an eight-step reduced mechanism simplified based on Kramlich’s detailed model developed in the 1980s [24]. However, this reduced mechanism is rarely adopted in CFD simulations for H2S prediction during pulverized coal combustion because its 3


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prediction accuracy is not satisfactory especially during low-NOx combustion [25]. The main aim of this study is to develop an integrated sulfur species prediction model that can accurately describe the evolution of sulfur species, especially H2S, during pulverized coal combustion. The developed model should consist of a simple but reliable description of sulfur release characteristics from coal and a global sulfur species gas-phase reaction mechanism. Furthermore, the new model should be easy to implement into commercial CFD software with a small extra computational cost. This study is organized as following sections. First, based on a detailed sulfur species gas-phase reaction mechanism, a global mechanism consisting of only ten reactions is proposed. The kinetic parameters of the global mechanism are determined via a rigorous optimization process. Second, based on experimental data of five kinds of sub-bituminous coals conducted in a drop tube furnace (DTF), equations describing the relationship between sulfur release and coal consumption are developed and validated. Third, a novel integrated sulfur species prediction model is developed by implementing the global sulfur species gas-phase reaction mechanism and the sulfur release sub-model into commercial CFD software, Fluent. Finally, the new model is validated by combustion experiments of three kinds of sub-bituminous coals with different stoichiometric ratios at different temperatures conducted in the DTF.

2. Sulfur species gas-phase reaction mechanism 2.1 Benchmark: a detailed sulfur species gas-phase reaction mechanism In the published literature, a series of detailed sulfur species gas-phase reaction mechanisms have been proposed and validated by necessary fundamental experiments

[20-22]

. These detailed

mechanisms can provide sufficient prediction results at different conditions by conducting hypothetical experiments in numerical tools, such as CHEMKIN, which can function as the benchmark for the development of the global mechanism. 4


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Among published detailed sulfur species gas-phase reaction mechanisms, we choose Leeds University sulfur mechanism

[20]

as the benchmark for the development of the global mechanism

in this study. Leeds University sulfur mechanism was proposed in 2000 and then modified and updated in 2002, 2003 and 2005. More than 100 elementary reactions and approximately 20 sulfur species are considering in its latest version, Leeds_SOx_52. Leeds University sulfur mechanism is one of the few detailed chemistries which consider the evolution of COS, one of the main sulfur-based species during coal combustion. Leeds University sulfur mechanism has been validated by experiments mechanism development

[26]

and widely cited

[27-29]

and adopted as the benchmark for reduced

[30]

. The schematic of Leeds University sulfur mechanism is illustrated

in Fig. 1, while its main elementary reactions are listed in Appendix B.

Fig. 1 Schematic of the detailed sulfur species gas-phase reaction mechanism (Leeds University sulfur mechanism)

As shown in Fig. 1, many sulfur species, including SO2, H2S, COS, S, SH, SO and HSO, are considered in Leeds University sulfur mechanism, and the formation and destruction of these sulfur species is conducted via numerous reversible elementary reactions. Apart from the main combustion species, including O2, CO2, H2O, CO and H2, many active intermediate species, such as O, H, OH, and CHi, are also considered as reactants. Because such intermediate species are mainly generated via gas-phase combustion reactions, Leeds University sulfur mechanism needs 5


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to run together with a detailed gas combustion mechanism. Here, the GRI-Mech 3.0 mechanism is selected [31]. GRI-Mech 3.0 is an optimized mechanism designed to model natural gas combustion, including NO chemistry. This mechanism contains 325 reactions and 53 species, which can provide the good combined modeling predictability of basic combustion properties.

2.2 Target: a global sulfur species gas-phase reaction mechanism Despite the huge advances in mechanism development during past decades, the application of detailed sulfur species gas-phase reaction mechanisms in CFD simulations of actual coal-fired boilers and burners is still hindered. The main reason can be ascribed to two significant difficulties: the huge computational cost resulting from the complicated reactions, and the uncertainty in the calculation of active intermediate species during coal combustion. Therefore it is of great necessity to develop a global sulfur species gas-phase reaction mechanism. The computational cost can be reduced by removing sulfur species of less importance in the global mechanism. In Leeds University sulfur mechanism, approximately 20 sulfur species are considered. These sulfur species can be classified into three groups depending on their relative contents: major sulfur species, including SO2, H2S, COS and CS2; intermediate sulfur species, including SH, SO, HSO, CS, etc.; and minor sulfur species, including HOS, HS2, HSO, and HSOO. Intermediate sulfur species are unstable, and their kinetics parameters are of high uncertainty; minor sulfur species are of a quite limited amount and are difficult to calculation and validation. Thus, both intermediate sulfur species and minor sulfur species are ignored in the global mechanism. Among major sulfur species, SO2 and H2S are widely accepted as the main sulfur-based products during coal combustion; COS is found to be present in the gasification process of coal and is easily converted into H2S with the participation of H2O or H2 [32]; the formation of CS2 is very difficult, and its concentration is usually limited

[33]

. Therefore, in the

global sulfur species gas-phase reaction mechanism, only three main sulfur species, SO2, H2S and COS, are considered. 6


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During coal combustion, active intermediate species, such as O, H, OH, and CHi, are mainly generated from released volatile and the incomplete gas combustion. However, compared with gas combustion with certain initial gas components, the detailed composition of volatiles released from different types of coals under different conditions is difficult to determine. Thus, accurate calculation of the active intermediate species in CFD simulations of coal combustion is prohibitive. Apart from this, it is also quite difficult to measure the amounts of such active intermediate species with high accuracy in coal combustion experiments. On the contrary, the main combustion species, such as O2, CO2, H2O, CO and H2, are easily measured in experiments and calculated in simulations. Therefore, in the global sulfur species gas-phase reaction mechanism, only the main combustion species, O2, CO2, H2O, CO and H2, are used as the reactants in sulfur species related reactions. On the basis of the above discussion, the schematic of the global sulfur species gas-phase reaction mechanism can be expressed via Fig. 2. It can be seen that, compared with the detailed mechanism illustrated in Fig. 1, the global mechanism is quite simple: in the oxidizing atmosphere, sulfur species tends to exist in the form of SO2, and the possible oxidant could be O2, CO2, or H2O; in the reducing atmosphere, sulfur species tend to exist in the form of H2S or COS, and the possible reductant could be CO and H2; there is also a shift between H2S and COS affected by H2/CO and H2O/CO2.

Fig. 2 Schematic of the global sulfur species gas-phase reaction mechanism 7


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To achieve the mechanism function shown above in Fig. 2, ten reactions in total, including two irreversible reactions and four pairs of reversible reactions, are used to constitute the global sulfur species gas-phase reaction mechanism, which is summarized in Table 1.

Table 1 List of reactions in the global sulfur species reaction mechanism

Number R.1

Reactions H2S+1.5O2=>SO2+H2O

R.2

COS+1.5O2=>SO2+CO2

R.3

H2S+CO=>COS+H2

R.4

COS+H2=>H2S+CO

R.5

SO2+3H2=>H2S+2H2O

R.6

H2S+2H2O=>SO2+3H2

R.7

SO2+3CO=>COS+2CO2

R.8

COS+2CO2=>SO2+3CO

R.9

COS+H2O=>H2S+CO2

R.10

H2S+CO2=>COS+H2O

2.3 Strategy to determine kinetic parameters of the global mechanism After the proposal of the global sulfur species gas-phase reaction mechanism as listed in Table 1, the remaining work is to determine the reaction rates of reactions R.1~R.10, i.e., r&1 ~ r&10 , which are written as:

r&i = ki ×[creactant,1 ]n1 [creactant,2 ]n2

(1)

where n1 and n2 are reaction orders representing the effect of the reactant concentration, and the rate constant ki is written in typical Arrhenius form:

ki =Ai × e−Ei / RT

(2)

where Ei is the activation energy representing the effect of temperature, and Ai is the pre-factor. The determination of the kinetic parameters is via an optimization conducted in MATLAB. The parameter optimization of the global sulfur reaction gas-phase mechanism uses the prediction 8


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results of the detailed mechanism as the benchmark. The simulation of the detailed mechanism is conducted in a 0-D reactor with a constant volume and a constant temperature in the CHEMKIN platform. To determine kinetic parameters (the pre-factor, the activation energy and the reaction order) the input parameters, including the temperature and the concentrations of gas species, are changed over a wide range. The temperature varies from 1400 K to 1800 K; the O2 concentration varies from 1 % to 21 %; the CO concentration varies from 1 % to 10 %; the H2 concentration varies from 0.5 % to 5 %; the CO2 concentration varies from 2 % to 20 % and the H2O concentration varies from 1 % to 10 %. Under each simulation condition, the detailed mechanism can output the evolution profiles of SO2, H2S and COS. Because each sulfur species participates in numerous elementary reactions in the detailed mechanism, the expression of its generation rate should be a complicated function including the effects of temperature and the concentrations of sulfur species, main combustion species and active intermediate species:

dcsulfur species, i dt

= y(csulfur species,1,..., cmain combustion species,1,..., cintermidiate species,1,..., T )

( 3)

On the contrary, the global mechanism only considers ten reactions, R1~R.10, and eight gas species: SO2, H2S, COS, O2, CO2, H2O, CO and H2. The evolution profiles of these species can be obtained by solving the system of differential equations consisting of eight equations, Eq. (4) ~Eq. (11):

dcSO2

= r&1 + r&2 − r&5 + r&6 − r&7 + r&8

( 4)

= −r&1 − r&3 + r&4 + r&5 − r&6 + r&9 − r&10

( 5)

dcCOS = −r&2 + r&3 − r&4 + r&7 − r&8 − r&9 + r&10 dt

(6)

dt

dcH2S dt

9


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dcO2 dt

= −1.5r&1 −1.5r&2

dcCO2 dt

dcH2O dt

( 7)

= r&2 + 2r&7 − 2r&8 + r&9 − r&10

( 8)

= r&1 + 2r&5 − 2r&6 − r&9 + r&10

( 9)

dcCO = −r&3 + r&4 − 3r&7 + 3r&8 dt dcH2 dt

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(10)

= r&3 − r&4 + 3r&7 − 3r&8

(11)

The solving of the above system of differential equations is performed by coding in MATLAB. The numerical method is the Runge-Kutta method using the ode45 function. For easier comparison between prediction results from the detailed mechanism and the global mechanism, the entire reaction process is uniformly divided into 100 time points, and each concentration profile of sulfur species is represented by 100 data points. For i from 1 to 100, ti=i×tT/100. For the detailed mechanism applied in CHEMKIN, the data points of concentrations of sulfur species are cSO2 ,d,i ; cH2S,d,i ; cCOS,d,i ; while for the global mechanism applied in MATLAB, the data points of concentrations of sulfur species are cSO2 ,g,i ; cH2S,g,i ; cCOS,g,i . A target function, fT, is proposed to represent the deviation between the prediction results of sulfur species from the detailed mechanism and the global mechanism, which is defined as:

1 100 1 100 1 100 2 2 ( c − c ) + ( c − c ) + ∑ H S,d,i H2S,g,i ∑ COS,d,i COS,g,i ∑(cSO2 ,d,i −cSO2 ,g,i )2 100 i=1 100 i =1 1 100 i =1 2 fT = 3 cH2S,0 + cCOS,0 +cSO2 ,0

(12)

Obviously, the value of fT relies heavily on the kinetic parameters of reactions R.1~R.10. Under each simulation condition, with the fixed values of reaction orders, fT is a function of the vector

K: 10


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fT = fT (K)

(13)

K = [k1, k2 ,..., k10 ]T

(14)

Then, the numerical task is to obtain the best vector Kb to make fT as minimal as possible. In this study, the numerical solution of this question adopts the direct search method, the classical Hooke-Jeeves algorithm

[34, 35]

. Hooke-Jeeves algorithm is a sequential optimization procedure

that was first presented in 1961. There are two types of searches through its steps of iterative calculations: heuristic search and pattern search. In the heuristic search, local behavior of the objective function is determined, while in the pattern search, the orientation of the objective function is designated. The specific steps of this algorithm applied in MATLAB are introduced in detail in the flowchart of Fig. 3.

Fig. 3 Flowchart to optimize the vector K by applying Hooke-Jeeves algorithm 11


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It can be seen that the entire process mainly consists of five steps: S1. Initialization: set the start point of the vector K0 and the first step-length of acceleration △K0:

K0 = (k10 , k20 ,.., k100 ) , and ∆K0 = (∆k10 , ∆k20 ,.., ∆k100 ) . S2. Heuristic search: search 10 variables in the vector K with 10 linearly independent directions,

∆Ki = [0,0,.., ∆ki0 ,...0]T , one-by-one, by K0±△Ki. For i from 1 to 10, if the judgment fT(K0±△Ki)≥ fT(K0) is always true, jump to step 4 to decrease the step-length of acceleration. Otherwise, if fT(K0±△Ki)< fT(K0), then K0 is replaced by K0±△Ki. After this heuristic search for i from 1 to 10, the vector K is changed from K0 to K1; then, jump to step 3 for the pattern search. The vector S=K1-K0 is the direction for pattern searching the vector K in step 3. S3: Pattern search: search the vector K along the direction S determined in the heuristic search in step 2. If fT(Kj+ S)< fT(Kj) is true, then continue the pattern search process by Kj+1= Kj+ S; otherwise jump back to step 2 for a new heuristic search to find the new direction S. S4. Decrease the step-length of acceleration: if, in the heuristic search, no possible direction S could be found, then the step-length of acceleration should be decreased by multiplying by the coefficient ε. In this study, the value of ε is set as 0.5, △Km+1=0.5×△Km. Then, jump back to step 2 for a new heuristic search. S5. End of the direct search: The above process from step 2 to step 4 will be repeated until the m m step-length of acceleration meets the requirement of the convergence: [∆K , ∆K ] < δ .

By applying the Hooke-Jeeves algorithm as above, the best vector K at different conditions can be obtained. By plotting the data at different temperatures in figures with the x-axis as 1/T and y-axis as ln(k), the activation energy, Ei, of each reaction can be obtained; by plotting the data at different concentrations of reactants with the x-axis as the reaction order and the y-axis as k, the reaction order, n1 and n2, of each reaction can be obtained. In the global sulfur species gas-phase reaction mechanism, the main combustion species, O2, 12


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CO2, H2O, CO and H2, are used to represent the active intermediate species, CHi, O, H, OH, etc. in the detailed mechanism. It is found that the reaction rate form of the global mechanism expressed in Eq. (1) fails to satisfactory reproduce the prediction results of the detailed mechanism. For some reactions, the concentration of product species other than the reactants also has a big effect on the reaction rate. Thus, an amending function, g(cproduct) is added to revise the reaction rate which is expressed in Eq. (15). In this study, the amending function adopts the hyperbolic form, and the best values of vector K with different concentrations of product species are plotted to fit its specific parameters.

r&i = ki ×[creactant,1 ]n1 [creactant,2 ]n2 × gi (cproduct )

(15)

After the determination of the activation energy, the reaction order and the amending function, the pre-factor is finally selected to make the average deviation as minimal as possible. The optimized kinetic parameters of the ten reactions in the global mechanism are summarized in Table 2. Table 2 Summary of the kinetic parameters of the global mechanism

Number

A

E

n1

n2

Hyperbolic Function

1 2 3 4 5 6 7 8 9 10

2.1E+10 3.1E+09 5.9E+10 9.9E+08 2.2E+08 1.6E+14 1.3E+05 1.3E+14 3.1E+09 2.6E+07

157.1 129.8 186.8 122.0 213.2 416.5 81.6 354.0 192.2 130.0

1.00 1.00 1.00 1.00 1.00 0.90 1.00 1.00 1.00 1.00

0.75 0.75 0.50 0.25 0.80 1.30 2.00 0.30 0.55 0.30

1 1 0.52exp(-8.0CH2)+2.56exp(-166.8CH2) 1.13exp(-0.93CCO)-0.43exp(-30.4CCO) 0.68exp(-27.5CH2O)+0.67exp(1.41CH2O) 27.69exp(-158.2CH2)+1.30exp(-19.6CH2) 47.36exp(-57.3CCO2)+5.15exp(-11.7CCO2) 5.49exp(-45.9CCO)+0.82exp(-4.4CCO) 1 1

3. Models 3.1. Models for coal combustion The commercial CFD software, ANSYS Fluent is used in the simulations of pulverized coal 13


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combustion experiments. The mathematical model is based on an Eulerian description for the continuum phase, while a stochastic Lagrangian description is used for the coal particles. For turbulence modeling, a steady-state Reynolds-averaged Navier–Stokes (RANS) model is used where turbulence closure is given by the realizable k–ε model. For the homogeneous reaction, a fast chemistry and mixture fraction probability density function (MF-PDF) is chosen. Radiation is the dominant form of heat transfer, and the P-1 model

[36]

is used. The particle emissivity is

assumed to be 0.9, and the Weighted Sum of Gray Gases Model (WSGGM) is applied to calculate the gas adsorption coefficient [37-38]. The motions of particles are simulated by a stochastic particle trajectory model. Coal pyrolysis is described by the single kinetic rate model, and its kinetic parameters follow the recommended values in Fluent Database for coal-hv (coal with high volatile content)

[39]

. A

kinetics/diffusion-limited model is used for the char burning process. Coals used in this study are of high volatile content, which means high reactivity. Thus, if the char burning model ignores char gasification, the predicted coal consumption fraction will be lower than the actual value, especially when the stoichiometric ratio is smaller than 1.0. Apart from this, the reduction of SO2 by CO is considered in the global reaction mechanism. If the char burning model ignores char gasification, the predicted CO concentration will be smaller than the actual value, and thus the accurate prediction of sulfur species is impossible. Based on above discussion, char gasification reactions with CO2 and H2O are added to the char burning model. The activation energy of char oxidation adopts the value in Fluent database for coal with high volatile content, while the activation energies of char gasification with CO2 and H2O are selected based on research from Guizani etc.

[40]

.The pre-factors of char oxidation and gasification are adjusted to fit the

experimental data of coal consumption fraction and concentrations of CO, CO2 and H2. The values of activation energy are also validated by experimental data obtained at different temperatures. Table 3 summarizes all of the kinetic parameters for coal pyrolysis, char oxidation 14


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and gasification. Table 3 Kinetic parameters for coal pyrolysis, char oxidation and gasification models Pyrolysis

Char+O2

Char+CO2

Char+H2O

Coal Type

A (s-1)

E (kJ·mol-1)

A (s·m-1)

E (kJ·mol-1)

A (s·m-1)

E (kJ·mol-1)

A (s·m-1)

E (kJ·mol-1)

Coal A Coal B Coal C

36200 36200 36200

74 74 74

0.001 0.001 0.001

74 74 74

0.03 0.04 0.02

150 150 150

0.024 0.032 0.016

135 135 135

In Fluent, the SIMPLER algorithm of pressure correction is used to address the coupling of velocity and pressure fields. The calculation is conducted by successive under-relaxation iterations until the solution satisfies a pre-specified tolerance.

3.2. Models for sulfur release and reaction The entire sulfur species evolution process during coal combustion is illustrated in Fig. 4. It can be clearly seen that it mainly includes two primary processes: the sulfur release behavior from coal and the subsequent sulfur species gas-phase reactions. Therefore, the integrated sulfur species prediction model requires two sub-models, the sulfur release sub-model and the sulfur species gas-phase reaction mechanism. The latter has been discussed in detail in section 2, and a global sulfur species reaction mechanism has been developed. Thus, this part mainly focuses on the sulfur release sub-model.

Fig. 4 Schematic of the sulfur species evolution process during coal combustion

As shown in Fig. 4, the sulfur release behavior is tightly related with the coal consumption. 15


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During pyrolysis, part of the sulfur in coal, with the fraction ratio of α, is released into the gas-phase in the form of H2S. During subsequent char burning, another part of the sulfur, with the fraction ratio of β, is released. The main products of sulfur released during char burning are SO2, H2S and COS, depending on different reactions, oxidation or gasification

[12-14]

. Finally, there is

still part of the sulfur remaining in the ash, with the fraction ratio of γ. Obviously, the critical aspect of the development of the sulfur release sub-model is determining the specific sulfur distribution fraction in volatile, fixed carbon and ash, i.e., the values of α, β and γ. In the CFD software Fluent, the calculation of sulfur species adopts the post-processed method. It is difficult to distinguish different particle trajectories for the post-processed method because there are numerous coal particles with different combustion histories in one computational cell. Thus, in this study, the sulfur release rate is correlated with the total coal pyrolysis rate and char burning rate in each cell. Thus, the source term of H2S released from pyrolysis ( SH2S,p ) in one computational cell is written as:

SH2S,p =

αSt Svol M w,H S V0

×

2

(18)

M w,SVc

Svol is the source of volatiles releasing from coal particles into the gas phase (kg/s); Vc is the volume of the cell (m3). V0 is the total volatile content (%); St is the total sulfur content (%); and

α St / V0 represents the sulfur content in volatiles (% sulfur/ % volatile). In the same manner, the source terms of SO2, H2S and COS ( SSO2 ,c , SH2S,c and SCOS,c ) released from char oxidation and gasification in one computational cell are, respectively, written as:

SSO2 ,c =

SH2S,c =

β St Sc,oxidation M w,SO

2

FC0

β St Sc,gasification M w,H S 2

FC0

(19)

M w,SVc

M w,SVc

× 0.5

(20)

16


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SCOS,c =

β St Sc,gasification M w,COS FC0

M w,SVc

× 0.5

(21)

Sc,oxidation and Sc,gasification are the sources of fixed carbon releasing from coal particles into the gas phase (kg/s) originating from char oxidation and gasification, respectively. FC0 is the total fixed carbon content (%), and β St / FC0 represents the sulfur content in fixed carbon (% sulfur/ % fixed carbon). As discussed above, the sulfur release behavior is related with rates of coal pyrolysis, char oxidation and gasification. Thus, the remaining problem is to determine the sulfur distribution in volatile, fixed carbon and ash, i.e., the values of α, β and γ, which will be solved by experiments conducted in the DTF.

3.3 Implementation of new models in CFD modeling Based on the above discussion in section 3.1, the new char burning model with the consideration of gasification is programmed in a User Defined Function (UDF) file and implemented in the CFD software, Fluent using the “Define_DPM_LAW” macro. Apart from this, the global sulfur species gas-phase reaction mechanism developed in section 2 is also implemented in Fluent using the “Define_SOX_RATE” macro. The detailed implementation strategy is illustrated in Fig. 5. It can be seen that we only replace the model of char burning in the discrete phase and the sulfur species gas-phase reaction mechanism in the post-calculated process. Additionally, we use Fluent itself to calculate the flow, heat transfer and other gas-phase reactions.

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Fig. 5 Implementation of char burning model and sulfur species gas-phase reaction mechanism in Fluent

4. Experiments 4.1. Coal property To determine the sulfur distribution fraction in volatile, fixed carbon and ash and to collect data to validate the developed sulfur species prediction model, experiments of pyrolysis and combustion are conducted in the DTF. In this study, five kinds of sub-bituminous coals are tested. Their proximate analysis and ultimate analysis follow the standards GB/T-212 and GB/T-31391, respectively, and the results are shown in Table 4. The fuel ratio, defined as the ratio of the fixed carbon amount to the volatile amount (FC/V), is widely used as an indicator of coal rank

[41]

. It

can be seen that the fuel ratios of these five kinds of coal range from 0.79 to 1.16, representing typical sub-bituminous coals of high volatile content. Table 4 Summary of coal properties

Proximate Analysis (wt%, ad )

Ultimate Analysis (wt%, daf)

Coal Type A

M

A

V

FC

FC/V

C

H

N

S

O

7.59

11.16

39.13

42.12

1.08

74.07

4.60

1.49

1.27

18.57

18


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B

11.79

6.84

45.38

35.99

0.79

70.21

4.93

1.14

0.76

22.96

C

7.46

22.83

35.21

34.50

0.98

75.23

5.59

2.65

2.71

13.82

D

4.40

21.19

36.64

37.77

1.03

65.33

5.59

1.80

0.58

26.70

E

10.28

13.50

35.24

40.98

1.16

71.07

5.69

1.20

0.49

21.54

4.2. Experimental setup The experimental setup used in this study is a DTF, which has been described in detail in previous works [16, 42]. As illustrated in Fig. 6, the reaction tube is a cylindrical alumina tube with an inner diameter of 60 mm, while the maximum length of the heated region is 2.0 m. The designed temperature is supported by 24 silicon carbide rods uniformly distributed around the tube and controlled by 10 thermocouples. Pulverized coal particles are fed by a scraper micro feeder and gas-transported into a water-cooled injector located at the top of the furnace. A movable vertical probe equipped with a water-cooled jacket is used to freeze the combustion at any wanted location. On leaving the probe, the samples are collected in a cyclone and a filter, while the flue gas is pumped into the gas analyzers. Pure N2 was used as a carrier gas in pyrolysis tests, and the temperature range was from 623 K to 1573 K. All five kinds of sub-bituminous coals are used for pyrolysis tests. Combustion tests are conducted at two temperatures, 1473 K and 1573 K, and the stoichiometric ratio range is from 0.55 to 1.05. Three kinds of sub-bituminous coals, coals A, B and C, are tested. During experiments, the concentrations of CO, CO2, SO2 are measured using Fourier transform infrared spectroscopy (FTIR); the concentrations of H2S and COS are measured by gas chromatography (GC). The coal consumption fraction is obtained by a thermo-gravimetric analyzer (TGA), and the residual sulfur content is measured by a total sulfur analyzer (TSA).

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Fig. 6 Schematic of the drop tube furnace (DTF) platform

4.3. Data evaluation The coal consumption fraction, Xt, during pyrolysis and combustion is calculated by using the ash-tracer method: X t =(1 −

A − A0 × A 1/ A − 1 ) × 100%=(1 − 0 ) ×100% 1/ A 0 − 1 A − A0 × A

(22)

A0 and A represent the initial and current ash content in samples. During pyrolysis and combustion, the relative remaining mass of char, mchar, is calculated as:

mchar =1 − X t × (V0 + FC0 )

(23)

V0 and FC0 represent the initial volatile content and initial fixed carbon content. With the residual sulfur content in char, Schar, the sulfur released fraction, Xsr, is calculated as: 20


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X sr =(1 −

mchar × Schar ) × 100% St

(24)

St represents the initial total sulfur content in coal.

5. Results and Discussion 5.1. Sulfur distribution in volatile, fixed carbon and ash As shown in Fig.4, the sulfur release behavior from coal pyrolysis and char burning is the first step for sulfur species evolution process, and the source terms of sulfur species in coal combustion are described by Eqs. (18) ~ (20). To clarify the sulfur release behavior during the entire combustion process, pyrolysis tests and combustion tests are conducted in the DTF. Relationships between sulfur release and coal consumption during pyrolysis and char burning are summarized in Fig. 7(a) and (b), respectively. The x-axis represents the coal consumption fraction defined as Eq. (22), while the y-axis represents the sulfur released fraction defined as Eq. (24).

Fig. 7 Relationship between sulfur release and coal consumption: (a). pyrolysis (b). char burning

As shown in Fig. 7(a), the sulfur release behaviors of five kinds of sub-bituminous coals during pyrolysis demonstrate a similar tendency: with the volatile release, the sulfur released fraction first increases and then decreases slightly. The final decrease of the sulfur released fraction should be explained by the interaction of sulfur species with char and ash at high temperatures. It was 21


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reported that char and some ash content can capture part of the sulfur species

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[43-45]

. To describe

the sulfur release behavior during pyrolysis for five kinds of sub-bituminous coals, a simple uniform fitting equation, y=1.1x, was adopted. Sulfur release behaviors during char burning of three kinds of sub-bituminous coals are shown in Fig. 7(b). It can be seen that the sulfur release from fixed carbon should be divided into two significantly different stages: in the first stage, the sulfur released fraction increases linearly with the coal consumption fraction; in the second stage, there is a sharp sulfur release in the final char burnout stage. In principle, the sulfur release behavior during char burning should be described by a piecewise function. However, only one linear relationship could be applied in Fluent due to the limitation of the sulfur species post-calculated method. The main purpose of this study is to accurately predict the H2S concentration in the fuel-rich zones. The final sulfur fast release usually occurs in the final burnout stage where O2 is abundant, and the related sulfur-based product is SO2. Thus, in this study, the final 10% of sulfur is assumed to remain in the ash, such that the linear fitting of the sulfur release behavior during the char burning process could be close to the experimental data. The overall relationship between sulfur release and coal consumption during the entire combustion process is summarized in Fig. 8. For coals A, B and C, the shift values of the coal consumption fraction from pyrolysis to char burning are 47.58%, 55.78% and 49.34%, respectively, based on their relative contents of volatile and fixed carbon. For coals A, B and C, the sulfur distribution fraction in volatile, α, should be:

αA = 47.58%×1.1 = 0.5234

(25)

αB = 55.78%×1.1 = 0.6136

(26)

αC = 49.34%×1.1 = 0.5427

(27)

For all three kinds of sub-bituminous coals, the sulfur distribution fraction in ash, γ, equals 0.1, and thus the sulfur distribution fraction in fixed carbon, β, for coals A, B and C should be:

βA = 1− 0.5234 − 0.1 = 0.3766

(28) 22


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Energy & Fuels

βB = 1− 0.6136 − 0.1 = 0.2864

(29)

βC = 1− 0.5427 − 0.1 = 0.3573

(30)

By substituting Eqs. (25) ~ (30) into Eqs. (18) ~ (21), the description problem of sulfur release behavior during coal pyrolysis and char burning is solved.

Fig. 8 Relationship between sulfur release and coal consumption during the entire combustion process

5.2. Parameter optimization of the global sulfur species gas-phase reaction mechanism As described in above section, after knowing the sulfur release behavior from coal pyrolysis and char burning, the second step for sulfur species evolution is the global sulfur species gas-phase reaction mechanism. The detailed method and steps to optimize the kinetic parameters of the global mechanism have been introduced in section 2, and the typical results are discussed in this section. The activation energy of each reaction, Ei, represents the effect of temperature on the reaction rate. To determine its value, the optimization process is conducted at different temperatures, from 1400 K to 1800 K. By using the numerical method as introduced in section 2, the best vectors, K, at different temperatures are obtained. For each reaction, with its optimal reaction rates, ki, at different temperatures, the classic figure to determine activation energy can be drawn. Here, the typical determination process of one irreversible reaction, R.1, and a pair of reversible reactions, 23


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R.7 and R.8, are displayed as examples in Fig. 9.

Fig. 9 Representative process to determine activation energy: (a). an irreversible reaction, R.1; (b). a pair of reversible reactions, R.5 and R.6

From Fig. 9, it can be seen that the linearity of data points is very high and that the activation energy can be calculated by multiplying the slope of the fitting line with 8.314. The activation energy of reaction R.1 based on Fig. 9(a) is 157.14 kJ/mol; while the activation energies of reactions R.5 and R.6 determined from Fig. 9(b) are 213.2 kJ/mol and 416.5 kJ/mol, respectively. The reaction orders, n1 and n2, of each reaction represent the effect of reactant concentrations on the reaction rate. To determine its value, the optimization process is performed at different concentrations of reactants. In this study, for each reactant, five different initial concentrations are used. Here, we use reaction R.7 as an example to demonstrate the specific process. By using the numerical method as introduced in section 2, the best vectors, K, at five different CO concentrations from 1% to 10% can be obtained with the given values of reaction orders. By varying the value of the reaction order of CO in R.7 from 0.2 to 3.0, the best vector, K, changes. Profiles of k7 of reaction R.7 with different CO concentrations can be obtained as shown in Fig. 10(a).

24


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Fig. 10 Representative process to determine the reaction order: (a). effect of reactant concentration on rate constant in R.7; (b). effect of reaction order on the deviation of rate constant at different conditions in R.7

The optimal reaction order of CO in R.7 should make these profiles with different CO concentrations as close as possible. To more easily determine the optimal value, parameter dm is proposed to represent the deviation of five profiles, which is defined in Eq. (32), and Fig. 10(a) is converted to Fig. 10(b). It can be clearly seen that the optimal value of the reaction order of CO in R.7 should be 2.0.

k7ave. =

1 5 i ∑k7 5 i=1

(31)

dm =

1 5 i ave. 2 ∑(k7 − k7 ) 5 i=1

(32)

In the parameter optimization process, it is found that only kinetic parameters, including E, n1 and n2, in the global mechanism are not enough to reproduce prediction results from the detailed mechanism. Some reactions need an additional amending function to represent the effect of concentration of product species other than its reactants. Fig. 11 uses reaction R.8 as an example. As shown in Fig. 11(a), apart from the temperature and concentrations of the reactants, the optimal k8 of reaction R.8 is also affected by the concentration of CO. The change of the reaction

25


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order of CO in R.7 has no effect on solving this problem. Thus, for such reaction, an amending function is added to consider the effect of CO concentration. As mentioned in section 2, the amending function adopts the hyperbolic form, and Fig. 11(b) displays the typical fitting process. It can be seen that the amending function can reasonably describe the effect of CO concentration on the reaction rate of R.8.

Fig. 11 Representative process to determine the amending function: (a). effect of product concentration on rate constant in R.8; (b). the fitted amending function for R.8

Fig. 12 demonstrates some typical comparisons between results from the detailed sulfur species gas-phase reaction mechanism and the global mechanism. Fig. 12(a) shows the process of H2S oxidation by O2 at different temperatures; Fig. 12(b) shows the process of SO2 reduction to H2S by different concentrations of H2; Fig. 12(c) shows the process of COS conversion to H2S by H2O and oxidation to SO2 by CO2 and H2O. It can be seen that the global sulfur species gas-phase reaction mechanism with the optimized kinetic parameters can accurately reproduce the prediction results from the detailed mechanism at different temperatures with different gas species concentrations.

26


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Fig. 12 Comparison of the detailed sulfur species gas-phase reaction mechanism and the global mechanism with optimized kinetic parameters (lines represent detailed mechanism results, while scatters represent global mechanism results): (a). H2S oxidation by O2; (b). SO2 reduction by H2; (c). COS conversion to H2S by H2O and oxidation by H2O and CO2

5.3. Effect of CO concentration on sulfur species evolution and its CFD simulation The relationship between the relative fraction of sulfur species and CO concentration obtained from the experiments is displayed in Fig. 13(a). Experimental data of three kinds of sub-bituminous coals at 1473 K with a reaction length of 1.3 m are summarized together. It can be seen that there is an obvious monotonic relationship between relative fraction of sulfur species and the CO concentration: the higher the CO concentration, the higher the H2S/COS fraction and the lower the SO2 fraction. Thus, to accurately predict the sulfur species evolution, accurate 27


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calculation of the CO concentration is the critical premise. Fig. 13(b) shows the relationship between CO concentration and the char gasification ratio. It can be seen that there is an almost linear relationship between CO concentration and the char gasification ratio. This means that the remarkable CO concentration during low-NOx combustion is mainly generated from char gasification. Thus, it is of great importance to include char gasification for sulfur species prediction in the CFD simulation of coal combustion.

Fig. 13 (a). Relationship between sulfur species relative fractions and CO concentration; (b). Relationship between CO concentration and char gasification ratio

Sulfur species simulation is post-calculated in Fluent. Thus, apart from the CO concentration, the accurate calculation of the char conversion ratio is also of great importance for sulfur species prediction. Comparisons of experimental data and simulation results on the char conversion ratio and CO concentration are displayed in Fig. 14. Fig. 14(a) shows a comparison of coal A at 1473 K with the stoichiometric ratio as 0.55 when the reaction length varies from 0.6 m to 1.3 m; Fig. 14(b) shows a comparison of coal C at 1473 K with a reaction length of 1.3 m when the stoichiometric ratio increases from 0.59 to 1.01. It can be seen that, with the consideration of gasification, the char burning sub-model can accurately calculate the char conversion ratio and the 28


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Energy & Fuels

CO concentration along the entire combustion process and with different stoichiometric ratios. Fig. 14(c) summaries the experimental data and predicted value of CO concentration at all conditions. It can be seen that the prediction errors for the CO concentration are within ±15%. Fig. 14(d) summaries the experimental data and predicted value of char conversion ratio at all conditions. It can be seen that the prediction errors for the char conversion ratio are within ±10%. The accurate combustion calculation of the char conversion ratio and the CO concentration provides the basis for sulfur species prediction.

Fig. 14 Comparison of experimental data and simulation results on the char conversion ratio and the CO concentration: (a). coal A with different reaction lengths; (b). coal C with different stoichiometric ratios; (c). Summary of the CO concentration; (d). Summary of the char conversion ratio

5.4. Validation of the novel sulfur species prediction model As discussed above, the sulfur release behavior during coal pyrolysis and char burning are 29


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solved, Eqs. (25) ~ (30), the global sulfur species gas-phase reaction mechanism is obtained, Table 1 and 2, and the coal combustion process especially the CO concentration can be predicted accurately, Fig. 14. This section focuses on the validation of the novel sulfur species prediction model applied in Fluent with experimental data obtained from the DTF. Fig. 15 illustrates the typical comparison between simulation results from the default sulfur species prediction model in Fluent and the new sulfur species prediction model. The default sulfur species gas-phase reaction mechanism in Fluent uses eight pairs of reversible reactions, and the reaction list is provided in Appendix C. Apart from SO2 and H2S, two other sulfur species, SH and SO, are also considered in this mechanism. From Fig. 15, it can be seen that, with the experimental data as the benchmark, compared with the default model in Fluent, the novel sulfur species prediction model can predict concentrations of SO2 and H2S with an obviously higher accuracy. Although COS cannot directly accelerate the high-temperature corrosion of water wall tubes, it is easily converted to corrosive H2S with the presence of H2 or H2O. Thus, it is of the same importance to accurately predict the COS concentration. As shown in Fig. 15(c), the novel sulfur species prediction model developed in this work can also give a reasonable prediction of the COS concentration, while the default model in Fluent fail to calculate the COS concentration. The reason of the prediction results from the default model in Fluent heavily deviate from experimental data can be explained by the common difficulty in calculating the intermediate species accurately in coal combustion simulation. In the default sulfur species gas-phase reaction mechanism in Fluent, some active intermediate species, such as O, OH, are included in the overall sulfur species reaction process. Although Fluent provides some equations, such as the equilibrium method or partial equilibrium method, to estimate their concentrations, its prediction results of sulfur species are still far from the experimental data.

30


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Energy & Fuels

Fig. 15 Typical comparison of experimental data and simulation results from the default model in Fluent and the novel sulfur species prediction model (coal A, stoichiometric ratio=0.55, T=1473 K): (a). SO2 concentration; (b). H2S concentration; (c). COS concentration

Experimental data and simulation results of H2S, COS and SO2 concentrations with different stoichiometric ratios at different temperatures of coal A are compared in Fig. 16 and Fig. 17. It can be seen that, with the increase of the stoichiometric ratio, the concentration of SO2 increases monotonously, while the concentrations of H2S and COS decrease resulting from the decrease of the reducing atmosphere. Especially as shown in Fig.16, when the stoichiometric ratio is extremely small, less than 0.6, most sulfur species exist as H2S or COS, while SO2 is of a limited concentration. Fig. 16(a), (b), and (c) demonstrate comparisons between experimental data and simulation results at different combustion lengths, 0.6 m, 0.9 m and 1.3 m, respectively, at 1473 K; 31


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Fig. 17(a), (b), and (c) demonstrate the comparisons at the higher temperature of 1573 K. It can be seen that the new sulfur species prediction model can reasonably describe the evolution of SO2, H2S and COS at different stoichiometric ratios, combustion lengths and temperatures.

Fig. 16 Comparison of experimental data and simulation results on sulfur species for coal A at 1473 K: (a) 0.60 m; (b) 0.90 m; (c) 1.3 m (lines represent prediction results while scatters represent experimental data)

Fig. 17 Comparison of experimental data and simulation results on sulfur species for coal A at 1573 K: (a) 0.60 m; (b) 0.90 m; (c) 1.3 m (lines represent prediction results while scatters represent experimental data)

To validate the developed sulfur species prediction model via other kinds of sub-bituminous coals, Fig. 18(a) and (b) demonstrate the typical comparison between experimental data and simulation results of coal B and coal C, respectively. It can be seen that the developed sulfur species prediction model can accurately describe the sulfur species evolution for different types of sub-bituminous coals. It should be noted that, for the simulations of three kinds of sub-bituminous coals at different conditions, as shown in Figs. 16, 17 and 18, the sulfur species prediction model adopts the same kinetic parameters. This means that the developed sulfur species prediction model has a wide range of applicability. 32


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Fig. 18 Comparison of experimental data and simulation results on sulfur species evolution for different types of coals: (a) coal B; (b). coal C

Fig. 19 Summary of experimental data and simulation results on sulfur species for three types of sub-bituminous coals at all tested conditions

Figs. 16, 17 and 18 only illustrate some typical prediction results of the new model. Fig. 19 summarizes the experimental data and the simulation results for SO2, H2S and COS for three kinds of sub-bituminous coals at all conditions. It can be seen that the prediction errors for sulfur species are within ±25%. This means that the developed sulfur species prediction model is acceptable in actual engineering applications to help design and operation improvements to 33


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reduce high temperature corrosion problem.

6. Conclusion In this study, a new integrated sulfur species prediction model that can accurately describe the evolution of sulfur species, especially H2S, during pulverized coal combustion is developed. The new model consists of a sulfur release sub-model and a global sulfur species gas-phase reaction mechanism. Sulfur release behavior of the sub-bituminous coal during pyrolysis and char burning is investigated in detail by using a DTF. Experimental data indicate that, during the pyrolysis process, there is a uniform relationship between sulfur release and volatile release; during the char burning process, there is always a fast sulfur release in the last stage of char burnout. Equations describing the relationship between sulfur release and coal consumption during the entire coal combustion process for different types of sub-bituminous coals are developed. The global sulfur species gas-phase reaction mechanism is developed based on the analysis of a detailed mechanism, Leeds University sulfur mechanism. The kinetic parameters of the global mechanism are determined via a rigorous optimization process. Comparison results convince the global mechanism consisting of only ten reactions can accurately reproduce the prediction results of SO2, H2S and COS by the detailed mechanism considering more than 100 elementary reactions. It is found that there is a strong relationship between sulfur species relative fractions and the CO concentration and that the CO concentration depends heavily on char gasification. Char gasification must be described accurately for sulfur species prediction in the CFD simulation of coal combustion. In this study, with the consideration of char gasification, the prediction errors of the CO concentration are within ±15%. 34


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The new sulfur species prediction model is implemented in the CFD software, Fluent and then validated by experimental data. Simulation results verify that the new sulfur species model can accurately predict the formation and destruction characteristics of three types of major sulfur species, H2S, COS and SO2, in the entire combustion process at different temperatures with different stoichiometric ratios for different kinds of sub-bituminous coals. The prediction errors for sulfur species under all conditions are within ±25%. The developed sulfur species prediction model is of great assistance for actual engineering applications.

Acknowledgement: The development of the global sulfur species gas-phase reaction mechanism in section 2 was funded by National Natural Science Foundation of China (51376105, 91434124) and National Key Research and Development Program (2016YFB0600801); the other part of this article was funded by the TSINGHUA-MHI R&D Center.

Appendix A. Nomenclature A, A0

current and initial ash content: %

R

gas constant: 8.314 J/(mol▪K)

V, V0

current and initial volatile matter content: %

T

temperature: K

FC, FC0

current and initial fixed carbon content: %

t

time: s

ci

mole concentration of species i: mol/m3

fT

target function

r&i

reaction order of reaction Ri: mol/(m3s)

Xt

coal consumption fraction: %

n1-2,i

reaction order of reaction Ri

Xsr

sulfur released fraction: %

ki

rate constant of reaction Ri: (mol/m3 )

1-n1,i -n2,i

/s

3 1-n1,i -n2,i

Ai

pre-factor of the reaction Ri: (mol/m )

Ei

activation energy of reaction Ri: kJ/mol

α

fraction of sulfur released with volatiles

Vc

volume of the computational cell: m3

β

fraction of sulfur released with char

S H 2S,p

source term of H2S from pyrolysis: mol/(m3s)

γ

fraction of sulfur remained in ash

/s

Greek symbols

35


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S H2S,c

source term of H2S from char burning: mol/(m3s)

SSO 2 ,c

source term of SO2 from char burning: mol/(m3s)

SCOS,c

source term of COS from char burning: mol/(m3s)

CFD

computational fluid dynamics

Svol

source of volatiles from coal particles: kg/s

DTF

drop tube furnace

Soxidation

source of fixed carbon from char oxidation: kg/s

UDF

user defined function

Abbreviations

Sgasification source of fixed carbon from char gasification: kg/s TGA

thermo-gravimetric analyzer

St

total sulfur content in initial coal: %

GC

gas chromatography

Schar

residual sulfur content in char: %

FTIR Fourier transform infrared spectroscopy

Mw,i

mole mass of species i: kg/mol

TSA

total sulfur analyzer

Appendix B. Main elementary reactions in the Leeds University sulfur mechanism (k = A×Tb×e-E/RT) 1

2 3 4 5 6 7 8 9 10 11 12 13 14

15 16 17 18 19

REACTIONS H2S+MS+H2+M N2 SO2 H2O H2S+HSH+H2 H2S+OSH+OH H2S+OHSH+H2O H2S+S2SH H2S+SHS2+H S+H2SH+H SH+OH+SO SH+OHS+H2O SH+HO2HSO+OH SH+O2HSO+O S+OHH+SO S+O2SO+O HS2+H+MH2S2+M N2 SO2 H2O H2S2+HHS2+H2 H2S2+OHS2+OH H2S2+OHHS2+H2O H2S2+SHS2+SH SO3+HHOSO+O

A 1.60E+24 Enhanced by Enhanced by Enhanced by 1.20E+07 7.50E+07 2.70E+12 8.30E+13 2.00E+13 1.40E+14 1.00E+14 1.00E+13 1.00E+12 1.90E+13 4.00E+13 5.20E+06 1.00E+16 Enhanced by Enhanced by Enhanced by 1.20E+07 7.50E+07 2.70E+12 8.30E+13 2.50E+05 36


b E -2.6 44800 1.500E+00 1.000E+01 1.000E+01 2.1 350 1.8 1460 0 0 0 3700 0 3723.8 0 9700 0 0 0 0 0 0 0 9000 0 0 1.8 -600 0 0 1.500E+00 1.000E+01 1.000E+01 2.1 360 1.8 1460 0 0 0 3700 2.9 25300

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20 21 22

23 24 25 26 27 28

29

30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

SO3+OSO2+O2 SO3+SO2SO2 SO+O(+M)SO2(+M) N2 SO2 H2O SO2+O(+M)SO3(+M) SO2+OH(+M)HOSO2(+M) SO2+OHHOSO+O SO2+OHSO3+H SO2+COSO+CO2 SO+MS+O+M N2 SO2 H2O SO+H+MHSO+M N2 SO2 H2O HOSO(+M)SO+OH(+M) SO+OHSO2+H SO+O2SO2+O 2SOSO2+S HSO+HHSOH HSO+HSH+OH HSO+HS+H2O HSO+HH2SO HSO+HH2S+O HSO+HSO+H2 HSO+O+MHSO2+M HSO+OSO2+H HSO+O+MHOSO+M HSO+OO+HOS HSO+OOH+SO HSO+OHHOSHO HSO+OHHOSO+H HSO+OHSO+H2O HSO+O2SO2+OH HSOHSH+OH HSOHS+H2O HSOHH2S+O H2SOH2S+O H+SO2(+M)HOSO(+M) HOSO+MO+HOS+M

2.00E+12 1.00E+12 3.20E+13 Enhanced by Enhanced by Enhanced by 9.20E+10 5.73E+12 3.90E+08 4.90E+02 2.70E+12 4.00E+14 Enhanced by Enhanced by Enhanced by 5.00E+15 Enhanced by Enhanced by Enhanced by 9.94E+21 1.08E+17 7.60E+03 2.00E+12 2.50E+20 4.90E+19 1.60E+09 1.80E+17 1.10E+06 1.00E+13 1.10E+19 4.50E+14 6.90E+19 4.80E+08 1.40E+13 5.20E+28 5.30E+07 1.70E+09 1.00E+12 2.80E+39 5.80E+29 9.80E+16 4.90E+28 3.12E+08 2.50E+30 37


0 10000 0 5000 0 0 1.500E+00 1.000E+01 1.000E+01 0 1200 -0.3 0 1.9 38200 2.7 12000 0 24300 0 54000 1.500E+00 1.000E+01 1.000E+01 0 0 1.500E+00 1.000E+01 1.000E+01 -2.5 38190 -1.4 0 2.4 1500 0 2000 -3.1 460 -1.9 785 1.4 -170 -2.5 25 1 5230 0 0 -1.7 -25 -0.4 0 -1.6 800 1 2700 0.1 150 -5.4 1600 1.6 1900 1 235 0 5000 -8.8 37800 -5.6 27400 -3.4 43500 -6.7 36000 1.6 3606 -4.8 60000

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55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

HOSO+HSO2+H2 HOSO+HSO+H2O HOSO+OHSO2+H2O HOSO+O2HO2+SO2 HSO2+HSO2+ H2 HSO2+OHSO2+ H2O HSO2+O2HO2+ SO2 H+SO2 (+M)HSO2+(+M) HOSO2HOSO+O HOSO2SO3+H HOSO2+HSO2+H2O HOSO2+OSO3+OH HOSO2+OHSO3+H2O HOSO2+O2HO2+SO3 HOSHOHOSO+H HOSHOSO+H2O HOSHO+HHOSO+H2 HOSHO+OHOSO+OH HOSHO+OHHOSO+H2O C+SO2CO+SO HOSO2+HSO3+H2 S+CH4SH+CH3 H2S+CH3CH4+SH SH+OS+OH C+H2SCH+SH O+COSCO+SO O+CSCO+S COS+M=CO+S+M O+COSCO2+S SH+O2SO+OH CH+SOCO+SH SO3+SSO+SO2 SH+NOSN+OH S+NOSN+O SH+NHSN+H2 N+SONO+S N+SHSN+H SN+NON2+SO SN+O2SO+NO SN+NO2S+NO+NO N+SNN2+S SO2+NO2NO+SO3 SO+NO2SO2+NO SN+OSO+N

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3.00E+13 6.30E-10 1.00E+12 1.00E+12 3.00E+13 1.00E+13 1.00E+13 1.06E+09 5.40E+18 1.40E+18 1.00E+12 5.00E+12 1.00E+12 7.80E+11 6.40E+30 1.20E+24 1.00E+12 5.00E+12 1.00E+12 4.16E+13 1.00E+12 6.00E+14 1.80E+11 6.30E+11 1.20E+14 1.93E+13 1.63E+14 1.43E+14 5.00E+13 1.00E+12 1.00E+13 5.12E+11 1.00E+13 1.00E+12 1.00E+14 6.31E+11 6.31E+11 1.81E+10 3.00E+08 4.07E+15 6.30E+11 4.25E-19 8.43E+12 6.31E+11 38


0 6.3 0 0 0 0 0 1.5 -2.3 -2.9 0 0 0 0 -5.9 -3.6 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0 0 0 0.5 0 0.5 0.5 0 0 -1 0.5 8.9 0 0.5

0 -960 0 500 0 0 0 594.6 53500 27600 0 0 0 330 37100 30000 0 0 0 0 0 12078.4 1177.5 4030.6 4450.3 2328.6 760.2 30700 5530.4 5032.5 0 0 8900.6 17500.6 0 1010.3 4030.6 0 0 0 0 3797.2 0 4030.6

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Energy & Fuels

99 100 101 102

S+NHSH+N NH+SONO+SH HSO+NO2HOSO+NO SO3+H2OH2SO4

1.00E+13 3.01E+13 5.80E+12 7.23E+08

0 0 0 0

0 0 0 0

A 1.820E7 9.376E6 1.380E2 3.105E7 1.622E8 7.691E9 3.548E8 2.985E9 4.365E3 9.886E8 4.467E5 1.663E6 1.096E3 8.670E14 8.710E9 1.905E14

b 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.8 0.0

E 7.484E3 6.254E4 3.742E3 1.219E5 2.566E3 1.187E5 2.687E3 1.695E5 1.380E4 6.036E4 2.703E4 7.614E4 0.0 3.819E5 0.0 5.207E5

A unit: mole-cm-sec-K; E unit: cal/mole

Appendix C. Eight-Step Reduced Mechanism in Fluent (k = A×Tb×e-E/RT) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

REACTIONS H2S+HSH+H2 SH+H2 H2S+H OH+H2SSH+H2O SH+H2OOH+H2S SO+OHH+SO2 H+SO2 SO+OH SH+OSO+H SO+H SH+O O+H2SSH+OH SH+OHO+H2S SO+O2SO2+O SO2+OSO+O2 H+SH+MH2S+M H2S+M H+SH+M SO+O+MSO2+M SO2+M SO+O+M

A unit: mole-m-sec-K; E unit: J/mole

References (1) Förtsch, D.; Kluger, F.; Schnell, U.; Spliethoff, H.; Hein, K.R.G. Proc. Combust. Inst. 1998, 27, 3037–3044. (2) Taniguchi, M.; Kamikawa, Y.; Okazaki, T.; Yamamoto, K.; Orita, H. Combust. Flame 2010, 157 (8), 1456–1466. (3) Taniguchi, M.; Kamikawa, Y.; Tatsumi, T.; Yamamoto, K. Combust. Flame 2011, 158 (11), 2261–2271. 39


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(4) Shim, H.; Valentine, J.R.; Davis, K.; Seo, S.; Kim, T. Fuel 2008, 87 (15–16), 3353–3361. (5) Morinaga, M.; Najima, S.; Wakabayashi, N.; Shirai, H. Evaluation of sulfide corrosion conditions in pulverized coal fired thermal power plant boilers. Springer Berlin Heidelberg, 2013, 1121–1129. (6) Zhou, H.; Yang, Y.; Dong, K.; Liu, H.; Shen, Y.; Cen, K. Fuel 2014, 134 (15), 595–602. (7) Valentine, J.R.; Shim, H.; Davis, K.A. Energy Fuels 2007, 21 (1), 242–249. (8) Zhou, Q.; Hu, H.; Liu, Q.; Zhu, S.; Zhao, R. Energy Fuels 2005, 19 (3), 892–897. (9) Hu, H.; Zhou, Q.; Zhu, S.; Meyer, B.; Krzack, S.; Chen, G. Fuel Process. Technol. 2014, 85 (8–10), 849–861. (10) Baruah, B.P.; Khare, P. Fuel 2007, 21 (6), 3346–3352. (11) Miura, K.; Mae, K.; Shimada, M.; Minami, H. Energy Fuels 2001, 15 (3) 629–636. (12) Chu. X.; Li, W.; Li, B.; Chen, H. Fuel 2008, 87 (2), 211–215. (13) Luo, Q.; Park, C.S.; Raju, A.S.K.; Norberk, J.M. Energy Fuels 2014, 28 (5), 3399−3402. (14) Chen L.; Bhattacharya, S. Environ. Sci. Technol. 2013, 47 (3), 1729−1734. (15) Shirai, H.; Ikeda, M.; Aramaki, H. Fuel 2013, 114, 114−119. (16) Zhang, Z.; Li, Z.; Cai, N. Energy Fuels 2016, 30 (5), 4353−4362. (17) Sugawara, K.; Tozuka, Y.; Sugawara, T.; Nishiyama, Y. Fuel Process. Technol. 1994, 37 (1), 73–85. (18) Chen, C.; Kojima, T. Fuel Process. Technol. 1997, 53 (1−2), 49–67. (19) Maffei, T.; Sommariva, S.; Ranzi, E.; Faravelli, T. Fuel 2012, 91 (1), 213–223. (20) Leeds University, Sulfur mechanism extension to the Leeds Methane Mechanism, May 2002. Available at: http://www.chem.leeds.ac.uk/combustion/mechanisms/leedssox50.dat. 40


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(21) Zhou C.; Sendt, K.; Haynes, B.S. Proc. Combust. Inst. 2013, 34 (1), 625–632. (22) Cerru, F.G.; Kronenburg, A.; Lindstedt, R.P. Combust. Flame 2006, 146 (3), 437–455. (23) Müller, M.; Schnell, U.; Scheffknecht, G. Energy Procedia 2013, 37, 1377–1388. (24) Kramlich, J.C. The fate and behavior of fuel-sulfur in combustion systems, Ph.D. Thesis. Washington State University, Washington, USA. 1980. (25) Qin M. Jiang W. Wu S. Journal of Chinese Society of Power Engineering 2016, 36 (2), 91-98. (in Chinese). (26) Hughes, K.J.; Tomlin, A.S.; Dupont, A.; Pourkashanian, M. Faraday Discuss 2001, 119, 337–352. (27) Mathieu, O.; Deguillaume, F.; Petersen, E.L. Combust. Flame 2014, 161 (1), 23–36. (28) Bongartz, D.; Ghoniem, A.F. Combust. Flame 2015, 162 (3), 544–553. (29) Cerru, F.G.; Kronenburg. A.; Lindstedt, R.P. Combust. Flame 2006, 146 (3), 437–455. (30) Selim, H.; Gupta, A.K.; Sassi, M. Applied Energy 2012, 93, 116–124. (31) Frenklach, M.; Bowman, T.; Smith, G. GRI-Mech 3.0http://www.me.berkeley.edu/gri_mech. (32) Yang, Y.; Shi, Y.; Cai, N. Fuel 2016, 181 (1), 1020–2026. (33) Towler, G.P.; Lynn, S. Ind. Eng. Chem. Res. 1993, 32 (11), 2800–2811. (34) Alkhamis, T.M.; Ahmed, M.A. European Journal of Operational Research 2006, 174 (3), 1802–1815. (35) Bazaraa, M.S.; Sherali, H.D.; Shetty, C.M. Nonlinear programming theory and algorithms, second ed., John Wiley and Sons, New York, 1993. (36) Siegel, R.; Howell, J.R. Thermal radiation heat transfer, Hemisphere Publishing Corporation, Washington DC, 1992. 41


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(37) Coppalle, A.; Vervisch, P. Combust. Flame 1983, 49, 101–108. (38) Smith, T.F.; Shen, Z.F.; Friedman, J.N. J. Heat Transfer 1982, 104, 602–608. (39) ANSYS Fluent, 14.5, Fluent Database Materials, combusting-particle, coal-hv. (40) Guizani, G.; Escudero Sanz, F.J.; Salvador, S. Fuel, 2013, 108, 812–823. (41) Xie, K. Structure and reactivity of coal. Science Press: Beijing, 2002 (in Chinese). (42) Zhang, Z.; Li, Z.; Cai, N. Combust. Flame 2016, 165, 83–96. (43) Sugawara, K.; Enda, Y.; Kato, T.; Sugawara, T.; Shirai, M. Energy Fuels 2003, 17 (1), 204–209. (44) Zhang, D.; Yani, S. Proc. Combust. Inst. 2011, 33 (2), 1747–1753. (45) Garcia-Labian, F.; Hampartsoumian, E.; Williams, A. Fuel 1995, 74 (7), 1072–1079.

42


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Fig.1 Schematic of the detailed sulfur species gas-phase reaction mechanism 73x61mm (300 x 300 DPI)


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Fig. 2 Schematic of the global sulfur species gas-phase reaction mechanism 66x50mm (300 x 300 DPI)


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Fig. 3 Flowchart to optimize the vector K by applying Hooke-Jeeves algorithm 94x99mm (300 x 300 DPI)


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Fig. 4 Schematic of the sulfur species evolution process during coal combustion 203x64mm (300 x 300 DPI)


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Fig. 5 Implementation of char burning model and sulfur species gas-phase reaction mechanism in Fluent 100x98mm (300 x 300 DPI)


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Fig. 6 Schematic of the drop tube furnace (DTF) platform 122x169mm (300 x 300 DPI)


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Fig. 7 Relationship between sulfur release and coal consumption: (a). pyrolysis 63x45mm (300 x 300 DPI)


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Fig. 7 Relationship between sulfur release and coal consumption: (b). char burning 63x45mm (300 x 300 DPI)


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Fig. 8 Relationship between sulfur release and coal consumption during the entire combustion process 73x52mm (300 x 300 DPI)


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Fig. 9 Representative process to determine activation energy: (a). an irreversible reaction, R.1 63x45mm (300 x 300 DPI)


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Energy & Fuels

Fig. 9 Representative process to determine activation energy: (b). a pair of reversible reactions, R.5 and R.6 63x45mm (300 x 300 DPI)


Energy & Fuels

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Fig. 10 Representative process to determine the reaction order: (a). effect of reactant concentration on rate constant in R.7 63x45mm (300 x 300 DPI)


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Fig. 10 Representative process to determine the reaction order: (b). effect of reaction order on the deviation of rate constant at different conditions in R.7 63x45mm (300 x 300 DPI)


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Fig. 11 Representative process to determine the amending function: (a). effect of product concentration on rate constant in R.8 63x45mm (300 x 300 DPI)


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Fig. 11 Representative process to determine the amending function: (b). the fitted amending function for R.8 63x45mm (300 x 300 DPI)


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Fig. 12 Comparison of the detailed sulfur species gas-phase reaction mechanism and the global mechanism with optimized kinetic parameters: (a). H2S oxidation by O2; 42x30mm (300 x 300 DPI)


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Fig. 12 Comparison of the detailed sulfur species gas-phase reaction mechanism and the global mechanism with optimized kinetic parameters: (b). SO2 reduction by H2 42x30mm (300 x 300 DPI)


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Fig. 12 Comparison of the detailed sulfur species gas-phase reaction mechanism and the global mechanism with optimized kinetic parameters : (c). COS conversion to H2S by H2O and oxidation by H2O and CO2 42x30mm (300 x 300 DPI)


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Fig. 13 (a). Relationship between sulfur species relative fractions and CO concentration 63x45mm (300 x 300 DPI)


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Fig. 13 (b). Relationship between CO concentration and char gasification ratio 63x45mm (300 x 300 DPI)


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Fig. 14 Comparison of experimental data and simulation results on the char conversion ratio and the CO concentration: (a). coal A with different reaction lengths 61x42mm (300 x 300 DPI)


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Fig. 14 Comparison of experimental data and simulation results on the char conversion ratio and the CO concentration: (b). coal C with different stoichiometric ratios 61x42mm (300 x 300 DPI)


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Fig. 14 Comparison of experimental data and simulation results on the char conversion ratio and the CO concentration:(c). Summary of the CO concentration 63x45mm (300 x 300 DPI)


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Fig. 14 Comparison of experimental data and simulation results on the char conversion ratio and the CO concentration:(d). Summary of the char conversion ratio 63x45mm (300 x 300 DPI)


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Fig. 15 Typical comparison of experimental data and simulation results from the default model in Fluent and the novel sulfur species prediction model: (a). SO2 concentration 63x45mm (300 x 300 DPI)


Energy & Fuels

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Fig. 15 Typical comparison of experimental data and simulation results from the default model in Fluent and the novel sulfur species prediction model:(b). H2S concentration 63x45mm (300 x 300 DPI)


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Energy & Fuels

Fig. 15 Typical comparison of experimental data and simulation results from the default model in Fluent and the novel sulfur species prediction model: (c). COS concentration 63x45mm (300 x 300 DPI)


Energy & Fuels

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Fig. 16 Comparison of experimental data and simulation results on sulfur species for coal A at 1473 K: (a) 0.60 m; (b) 0.90 m; (c) 1.3 m 46x11mm (300 x 300 DPI)


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Energy & Fuels

Fig. 17 Comparison of experimental data and simulation results on sulfur species for coal A at 1573 K: (a) 0.60 m; (b) 0.90 m; (c) 1.3 m 46x11mm (300 x 300 DPI)


Energy & Fuels

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Fig. 18 Comparison of experimental data and simulation results on sulfur species evolution for different types of coals: (a) coal B 63x45mm (300 x 300 DPI)


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Energy & Fuels

Fig. 18 Comparison of experimental data and simulation results on sulfur species evolution for different types of coals: (b). coal C 63x45mm (300 x 300 DPI)


Energy & Fuels

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Fig. 19 Summary of experimental data and simulation results on sulfur species for three types of subbituminous coals at all tested conditions 82x59mm (300 x 300 DPI)


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Development of Sulfur Release and Reaction Model for CFD Modeling

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