Numerical Studies of the Coal Devolatilization Characteristics with

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Numerical Studies of the Coal Devolatilization Characteristics with Gas Temperature Fluctuation Jiangkuan Xing, Yun Bai, Chunguang Zhao, Zhengwei Gao, and Haiou Wang Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b01361 • Publication Date (Web): 17 Jul 2018 Downloaded from http://pubs.acs.org on July 25, 2018

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Energy & Fuels

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Numerical Studies of the Coal

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Devolatilization Characteristics with Gas

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Temperature Fluctuation

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By

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Jiangkuan Xing, Yun Bai, Chunguang Zhao, Zhengwei Gao,

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Haiou Wang*

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State Key Laboratory of Clean Energy Utilization, Zhejiang

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University, Hangzhou 310027, P.R. China

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Submitted to

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Energy & Fuels

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*Corresponding author, E-mail: [email protected], Tel: 86-0571-87951764

ACS Paragon Plus Environment

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Abstract

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In the present work, the coal devolatilization characteristics in hot gaseous

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environment with gas temperature fluctuation are investigated numerically with the

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chemical percolation devolatilization (CPD) model and the two-step (TS) model in a

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zero-dimensional configuration. The numerical results are first validated against the

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experimental data, showing that the CPD model can reproduce the coal

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devolatilization process well. The effects of mean gas temperature, gas temperature

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fluctuation amplitude and coal particle size on coal devolatilization are then explored.

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The particle temperature is found to fluctuate with the gas temperature fluctuation at

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the same frequency. However, the particle temperature shows a delay in responding to

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the gas temperature fluctuation, with smaller particles having a shorter delay time. It

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is also found that the gas temperature fluctuation facilitates coal devolatilization. In

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particular, higher mean gas temperature and fluctuation amplitude reduce the start

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time of the devolatilization and result in higher total volatile and tar yield. Smaller

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particles are more sensitive to gas temperature fluctuation, and are easier and faster to

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devolatilize, producing more volatile and tar. Compared to the CPD model, the

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devolatilization process predicted by TS model generally starts earlier and the final

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volatile yield is overestimated. Moreover, the TS model predictions have a shorter

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delay time and larger fluctuation amplitude of coal particle temperature compared to

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the CPD model predictions, which indicates that the TS model is more apt to be

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affected by gas temperature fluctuation than the CPD model.

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1. Introduction

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Coal accounts for about 29.2 % of the world's energy consumption [1], and has

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been widely utilized in many coal-fired thermal power plants using pulverized coal

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combustion (PCC) technology. With increasing concern on the environment, clean

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and efficient utilization of coal is becoming more and more critical. The combustion

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and emission characteristics of pulverized coal flames in a pulverized coal-fired

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furnace are strongly affected by the dynamic, thermal and chemical behaviors of

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pulverized coal particles [2]. Thus, it is necessary to gain a deep understanding of the

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thermochemical behaviors of pulverized coal particles to enable the design of

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advanced coal combustion technology.

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In real coal-fired furnaces, multiple physics are involved in pulverized coal

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combustion including turbulence, chemistry, particle transport, heat and mass transfer

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between the particle and gas phase, and radiative heat transfer [3, 4], where pulverized

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coal particles are exposed to a turbulent flow field with gas temperature fluctuation.

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Turbulence affects the instantaneous velocity and position of the pulverized coal

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particles, while the gas temperature fluctuation affects the particle heat transfer

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characteristics [5], which in turn modify the thermochemical behavior of the particles.

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It is well known that pulverized coal particles undergo complex physical and

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chemical processes in coal-fired furnaces, i.e. water evaporation, devolatilization and

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char combustion, among which devolatilization is a key process and plays an essential

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role in coal combustion [6]. Devolatilization has significant influences on flame

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ignition [7], flame stability [8, 9] and pollutant emission [10]. The instantaneous


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release rate of volatiles from coal particles is correlated to the particle temperature

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and instantaneous heating rate [11-13]. The gas temperature fluctuation affects the

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instantaneous temperature of the particles, thus influencing the coal devolatilization

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process. However, the detailed effects of gas temperature fluctuation on the coal

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devolatilization process are not well understood yet.

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Only few studies have explored gas temperature fluctuation effects on coal

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devolatilization in the literature, e.g. the study of Zhang et al. [14], who employed a

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competing two-step (TS) model proposed by Ubhayakar et al. [15]. However, it is

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well known that in the TS model the kinetic parameters are constant without

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considering the effect of the instantaneous particle heating rate, while in practical

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devices of pulverized coal combustion the instantaneous heating rate of the coal

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particle changes frequently in a hot environment of turbulent flows. Moreover, the

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thermal properties of particles and the gas phase were constant in the study of Zhang

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et al, despite the particle and gas temperatures changed in the simulation [14].

79

Therefore, the study in Ref. [14] is only qualitative and preliminary. It is necessary to

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investigate more rigorously the effect of gas temperature fluctuation on the coal

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devolatilization process using a more sophisticated model.

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In the present study, the chemical percolation devolatilization (CPD) model,

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which has been shown to have good performance over a wide range of heating rates,

84

temperature, coal types and pressures [16-18], is employed to investigate the detailed

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effects of gas temperature fluctuation on the coal devolatilization characteristics in a

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zero-dimensional configuration, which hasn’t been reported before. The validity of


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the model is confirmed by comparing the model predictions to the experimental

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measurements. Following this, the effects of mean gas temperature, gas temperature

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fluctuation amplitude and coal particle size on coal devolatilization are explored with

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the CPD and TS models. The CPD and TS model results are presented and compared.

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The remainder of this paper is organized as follows. The numerical methods for

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the coal particle devolatilization process, including the particle energy equations and

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formulas for the TS and CPD models, are provided in Section 2. The detailed solution

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procedure and operating conditions are presented in Section 3. The results and

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discussion regarding the effects of gas temperature fluctuation are presented in

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Section 4. A summary and conclusions are given in Section 5.

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2. Numerical methods

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In the coal combustion process, the release of volatiles mainly occurs when the

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coal particle temperature is in the range of 1000 K to 1200 K [12], when the char

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reaction rate is low compared with the devolatilization rate. Thus, only the coal

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devolatilization process is considered in the present work. In the following, the

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governing equations for particle energy are presented in Section 2.1. The

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devolatilization models are introduced in Section 2.2.

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2.1 Particle energy equations

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The energy equation for a coal particle is written as:

mp C p , p

dTp

dt

= π d pλg Nu p

A (Tg -Tp ) + π d p 2ε pσ b (Tg 4 − Tp 4 ) − m& v ∆hv e −1 A

(1)

The first term on the right hand side is the convective heat transfer between the gas


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and particle phase, the second term is the radiative heat transfer and the third term is

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the heat of the coal devolatilization process. The variables mp , Cp, p , Tp , and d p

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are the instantaneous mass, heat capacity, temperature and diameter of the pulverized

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coal particle, respectively.

113

temperature, respectively.

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Boltzmann constant and the heat of devolatilization process, respectively.

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instantaneous volatile release rate and its value is evaluated through the

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devolatilization models which will be discussed in Section 2.2. Nup is the particle

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Nusselt number and A is a parameter related to the mass transfer rate:

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λg and Tg are the gas thermal conductivity and

ε p , σb and ∆hv are the particle emissivity, Stefan– &v is the m

Nu p = 2 + 0.654 Re p 0.5 Pr 1/ 3

(2)

m& p

(3)

A=

(

π d p Nu p λ g C p,g

)

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& p is the instantaneous particle mass where Pr is the Prandtl number of gas, m

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variation rate, and Cp,g is the gas heat capacity. The particle diameter d p is

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assumed to be constant, and thus the density of the coal particle is expressed as

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ρ p = 6mp / (π d p3 ) . Rep is the Reynolds number of the particle and is written as:

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Re p =

d p ∆V vg

(4)

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where vg is the gas viscosity and ∆V is the relative velocity magnitude between

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the gas and particle phase. In coal combustion applications, the gas temperature

127

varies in time due to turbulence. To account for the influence of gas temperature

128

fluctuation on the coal particle devolatilization process, the present study assumes

129

that the gas temperature fluctuates with time in a harmonic way as [14]:


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Energy & Fuels

Tg = T (1 + AT sin(2π ft ))

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(5)

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where T is the mean gas temperature, AT is the amplitude of gas temperature

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fluctuation, t is the time and f is the frequency of gas temperature fluctuation.

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Through Eqs. (1)-(5), the particle temperature can be determined, which is used as

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the input for particle temperature in the coal devolatilization models.

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2.2 Coal devolatilization models

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2.2.1 The TS model

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In the TS model, there are two competing kinetic steps with varying activation

138

energies [19], as shown in Fig. 1. The first step dominates the reactions at low

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temperature and the second prevails at high temperature. The instantaneous

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devolatilization rate can then be expressed as: − E1 − E1  RT p RT p m& v = − C  α 1 K 1e + α 1 K 1e  

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   

(6)

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where α1 and α2 are the mass stoichiometric coefficients of the two kinetic steps, K1

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and K2 are the pre-exponential factors of the steps, E1 and E2 are the activation

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& c , is energies, and C is the fraction of the raw coal. The coal mass variation rate, m

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written as:

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− E1 − E1  RT p RT p  & m c = − C K 1e + K 1e  

   

(7)

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In Eq. (6), the values of the kinetic parameters, which play an important role in

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the model prediction, are constant. There have been many studies focused on the

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optimization of the kinetic parameters [11, 20, 21]. The values of the kinetic

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parameters from Ubhayakar et al. [15] are commonly used in coal combustion


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simulations, and they are employed in the present study.

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2.2.2 The CPD model

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The CPD model describes the coal devolatilization process under a rapid heating

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condition based on chemical structure. In the CPD model, coal is regarded as a

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macromolecular array composed of aromatic clusters that are interconnected by a

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variety of chemical bridges, and attachments to clusters may also include side chains.

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The CPD model [16-18] characterizes the devolatilization process including

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percolation statistics for a two-dimensional Bethe lattice to relate the distribution of

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clusters that detached from the lattice with the number of broken bridges, the broken

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rate of bridges and the release rate of the side chain, the vapor-liquid equilibrium used

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to determine the size of detached clusters that vaporize to form tar, and crosslinking of

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non-vaporized detached fragments that become parts of the char.

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A schematic for the coal devolatilization process of the CPD model is shown in

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Fig. 2. The symbol ψ represents a labile bridge which decomposes to form a reactive

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bridge intermediate ψ* under a relatively slow step with a rate constant

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reactive bridge intermediate is unstable and reacts quickly in two kinetic pathways. In

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the first pathway, the reactive intermediate ψ* is stabilized to form a stable char

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bridge c and light gases

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reactive bridge intermediate is cleaved with a rate constant

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form side chains δ , which eventually undergo a cracking reaction to form light gas

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with a rate constant kg .

kb . The

g2 with a rate constant kc . In a competing pathway, the

172


kδ , and the two halves

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3. Solution procedures and operating conditions

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3.1 Solution procedures

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In the present work, both the TS model and CPD model are employed for the

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modeling of coal devolatilization. In the original CPD model, the particle temperature

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history needs to be provided before the calculation. Here, the CPD model is coupled

178

with the particle energy equation, from which the coal particle temperature is

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determined. The detailed procedures of the CPD model implementation are shown in

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Fig. 3 and explained in the following:

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Step 1: Provide the initial structural parameters and temperature of the pulverized coal particle for the CPD model.

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Step 2: Obtain the coal devolatilization results of the first time step with the CPD

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model. Update and restore the variables in the CPD model for the calculation of the

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next time step.

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Step 3: Update the coal particle temperature by solving the particle energy equations. Step 4: Provide the updated particle temperature and the variables stored in the previous time step for the CPD model.

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Step 5: Obtain the coal devolatilization results of the current time step based on

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the particle temperature and the restored variables. The variables are then updated and

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restored for the calculation of the next time step.

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Repeat Step 3, 4 and 5 until the calculation is ended.

3.2 Operating conditions


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The energy equations for pulverized coal particle are solved numerically in a

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zero-dimensional configuration. The time step adopted for the numerical simulations

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is sufficiently small, about 1.0×10-6 s, to ensure numerical accuracy. The coal

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compositions in the study of Zhang et al. [14] are unavailable; a new coal, the North

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Dakota Beulah Zap, is studied here and its elemental composition and volatile matter

200

content are listed in Table 1. The density of this coal is 1300 kg/m3 and the particle

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emissivity is 0.85, in line with the recommendation of the CBC workshop [22], and

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the particle specific heat is determined by the particle temperature as in Ref. [23]. The

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oxidizer is the hot air, which consists of nitrogen and oxygen. The gas thermal

204

conductivity

205

as in Refs. [24, 25]. The heat of the coal devolatilization process is about

206

1.06155 kJ/kg calculated from Ref. [26]. The initial particle temperature is 300 K.

λg and heat capacity Cp,g are determined by the gas temperature Tg

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To study the effects of mean gas temperature, gas temperature fluctuation, and

208

particle size on the coal devolatilization process, a series of cases with different coal

209

particle diameters under gas temperature fluctuation conditions with different mean

210

gas temperatures and gas temperature fluctuation amplitudes are investigated. The

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operating conditions are listed in Table 2. The typical size of a pulverized coal particle

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is in the range of 10~100 µm [27]. In the present work, four representative particle

213

sizes are considered, i.e. 10, 30, 50 and 100 µm. The mean gas temperatures are set to

214

1000 K, 1100 K and 1200 K. As stated in Section 2, in the temperature range of

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1000~1200 K, the rate of char combustion can be neglected. The amplitudes of gas

216

temperature fluctuations are set to 0, 0.1 and 0.2 [28, 29]. The fluctuation frequency


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of the gas temperature is set to 100 Hz [30].

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4. Results and discussion

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In this section, the performance of the devolatilization models is first assessed

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through comparing the model predictions with the experimental data. Then, the results

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of a series of cases with varying operating parameters are presented to reveal the

223

effects of the mean gas temperature, gas temperature fluctuation amplitude and

224

particle size on the coal devolatilization characteristics, i.e. the particle temperature,

225

total volatile yield and tar yield.

226

4.1 Validation

227

To verify the results of volatile production, the predictions by the CPD model

228

and the TS model are compared to the experimental data using North Dakota lignite

229

[31] as shown in Fig. 4. In the experiment, the coal particles with a diameter of about

230

60 µm were heated under a gas temperature of 1073K in the heated tube reactor (HTR).

231

The volatiles, including tars and detailed gas components, were measured based on

232

Fourier Transform Infrared Spectroscopy (FT-IR), Gas Chromatography (GC) and

233

elemental analysis. Fig. 4 shows that the predictions with the CPD model are in good

234

agreement with the experimental data. In contrast, the predictions with the TS model

235

overestimate the final volatile yield. It is, therefore, suggested that the CPD model is

236

suitable to describe the devolatilization process of the coal particles. In the following

237

sections, the predictions of volatile production by the CPD model under various

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operating conditions with gas temperature fluctuations are presented and discussed.


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4.2 Effect of mean gas temperature

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Although Section 4.1 shows that the CPD model performs better in predicting

241

the coal devolatilization process, the TS model results are still presented in the

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following for two reasons. First, it is desirable to check if the TS model is able to

243

capture the general trend of the coal devolatilization process in the current

244

configuration as the TS model is widely used in the coal combustion community.

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Second, a quantitative analysis of the discrepancy between the CPD and TS models

246

can be performed by presenting both models.

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Figure 5a shows the comparisons of the particle temperature history predicted

248

with the TS model and the CPD model under gas temperature fluctuations with

249

different mean temperature values. The gas temperature is also shown for reference.

250

Three mean gas temperatures are considered, i.e. 1000K, 1100K and 1200K. The gas

251

temperature fluctuation amplitude is 0.10, and the particle diameter is 50 µm. It is

252

seen that, at initial time, the coal particle temperature increases quickly until it reaches

253

a quasi-steady state (t < 0.05s). The particle temperature then fluctuates with the gas

254

temperature at the same frequency in the quasi-steady state. Both the CPD and TS

255

predictions show a delay in responding to the gas temperature fluctuation, with the

256

delay in the CPD model more significant; the fluctuation amplitude of particle

257

temperature predicted with the TS model is larger than that with the CPD model as

258

shown in the insert of Fig. 5a.

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Concerning the volatile production, Fig. 5b shows that the final total volatiles

260

predicted by the TS model are independent to the mean temperature since there is


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sufficient time for the coal particles to devolatilize completely. In contrast, the

262

predictions with the CPD model show significantly different trends, i.e., the total

263

volatiles increase as the mean gas temperature increases. This is because different

264

mean gas temperatures cause different heating rates for the coal particles, which

265

further influences the volatile production in the CPD model.

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To further understand the volatile production characteristics, the volatile release

267

rate is shown in Fig. 5c. It is seen that the first significant increase of devolatilization

268

rate predicted with the TS model is earlier than that with the CPD model. This is

269

consistent with previous study [21]. Also, it is found that the coal devolatilization

270

process starts earlier when the mean temperature is higher.

271

One advantage of the CPD model is that it can accurately predict the tar yield

272

[12], while the TS model focuses on the total volatiles without providing any

273

information of the volatile components. Thus, only the tar release characteristic by the

274

CPD model is presented in the following. Fig. 6 shows the tar yield predicted under

275

gas temperature fluctuations. It is seen that the final tar yield increases with the mean

276

gas temperature, while the increase is insignificant when the temperature is above

277

1100K. The tar release process finishes earlier under higher mean temperature

278

conditions.

279 280

4.2 Effect of fluctuation amplitude

281

As our previous DNS results showed [23], the gas temperature fluctuation

282

amplitude evolves downstream. This motivates us to study the effect of gas


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temperature fluctuation amplitude on the particle devolatilization behavior.

284

The particle temperatures predicted by the CPD and TS models with different

285

gas temperature fluctuation amplitudes are compared in Fig. 7a. The gas temperature

286

is displayed as well. Three fluctuation amplitudes are considered, i.e. 0, 0.10 and 0.20.

287

The particle diameter is 50 µm, and the mean gas temperature is 1100K. It is

288

interesting to observe that, for both the CPD and TS model predictions, the increasing

289

rate of the particle temperature is higher when the gas temperature fluctuation

290

amplitude is larger. The coal particle temperature predicted with the TS model

291

increases faster than that with the CPD model. Similar as the finding in Fig. 5a, a

292

noticeable delay can be found between the gas and particle temperature for both the

293

CPD and TS predictions, with the delay of CPD predictions more evident. Also

294

consistent with Fig. 5a, the coal particle temperature predicted with the TS model

295

shows a larger fluctuation amplitude than that with the CPD model.

296

Figure 7b indicates that the final volatile yields of the CPD model increase with

297

increasing fluctuation amplitude, while those of the TS model are essentially the same.

298

The devolatilization process predicted with the TS model finishes earlier with

299

increasing fluctuation amplitude.

300

To further understand the volatile production characteristics, the volatile release

301

rate is shown in Fig. 7c. It is found that the release rate also fluctuates with the gas

302

temperature, and its peak value increases with the fluctuation amplitude.

303

Figure 8 shows the comparisons of the tar prediction between the CPD and TS

304

models under gas temperature fluctuations with different amplitudes. Note that the


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mean gas temperature is 1100 K. It is observed that the final tar yield varies only

306

slightly with different gas temperature amplitudes. Tar release rate is more sensitive to

307

the gas temperature amplitude as shown in Fig. 8b.

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4.3 Effect of particle diameter

309

In real coal combustion systems, the pulverized coal particle size varies over a

310

wide range. Thus, the effect of coal particle diameter on the devolatilization process

311

should be understood. In the present study, four values of coal particle diameters are

312

assessed, i.e., 10, 30, 50 and 100 µm. The mean gas temperature is 1200 K, and the

313

fluctuation amplitude is 0.10. Fig. 9a shows the particle temperatures predicted by the

314

TS and CPD models, and the gas temperature is also displayed for reference. When

315

the particle is sufficiently small, i.e. 10 µm, the particle is rapidly heated to the gas

316

temperature. Thus, the particle temperature is nearly the same as the gas temperature

317

without noticeable delay. As for large particles, the heating rate of the particle

318

decreases and more time is needed for larger particles to reach the gas temperature. In

319

general, the particle temperature fluctuation has the same frequency of the gas

320

temperature. A possible explanation is that the gas and particles reach

321

quasi-equilibrium of temperature through heat transfer after the initial stage of the

322

simulation, resulting in a consistent frequency of the gas and particle temperature

323

fluctuations. It can be found that, consistent with Fig. 5a and Fig. 7a, both the CPD

324

and TS model predictions of the particle temperature fluctuation show a delay in

325

responding to the gas temperature fluctuation, with smaller particles having a shorter

326

delay time. The delay of the CPD predictions is found to be more significant, i.e.,


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0.3µs, 2.0µs, 4.0µs and 4.5µs for coal particles with diameters of 10 µm, 30µm, 50µm

328

and 100 µm, respectively. The particle temperature increases faster with the TS model

329

than with the CPD model.

330

The comparisons of total volatiles for coal particles of different diameters are

331

shown in Fig. 9b. It is seen that, for both the TS and CPD model predictions, the

332

devolatilization process occurs later as the particle diameter increases. Also, larger

333

particles have lower final volatile yield and longer devolatilization time. The TS

334

model predictions show the same final volatile yield for various particle diameters,

335

while the CPD model predictions of the final volatile yield depend on the particle size,

336

with smaller particles having a higher final volatile yield.

337

Figure 9c shows the comparisons of the volatile release rates with the CPD and

338

TS models. It is confirmed that the devolatilization process in the TS model is earlier

339

than that of the CPD model. The CPD model predictions provide a high peak value

340

and longer devolatilization time. Particularly, the peak devolatilization rates predicted

341

by the CPD model are 292 s-1, 147 s-1, 53 s-1 and 18 s-1 for coal particles with

342

diameters of 10 µm, 30µm, 50µm and 100 µm, respectively.

343

Finally, the tar yield of coal particle with different particle diameters is shown in

344

Fig. 10. Note that the mean gas temperature is 1200 K. It is seen that smaller particles

345

have higher tar yield, and the devolatilization process for smaller particles is earlier

346

and faster. In contrast, larger particles have lower peak tar release rates, and its

347

devolatilization process is longer.

348


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Page 17 of 34 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

Energy & Fuels

349

5. Conclusions

350

The instantaneous coal devolatilization characteristic under gas temperature

351

fluctuations is numerically studied in a zero-dimensional configuration. First, the coal

352

devolatilization models are validated with the experimental data. The results show

353

that the CPD model provides an accurate prediction on coal devolatilization, while the

354

TS model overestimates the volatile yield. Then, a series of cases with varying

355

operating parameters are performed and analyzed to understand the effect of gas

356

temperature fluctuation on the coal devolatilization characteristics. The main findings

357

are summarized as follows:

358

(1) The particle temperature fluctuates with the gas temperature at the same

359

frequency. The particle temperatures predicted by the CPD and TS models show a

360

delay in responding to the gas temperature fluctuation.

361

(2) The gas temperature fluctuation has a positive effect on the coal

362

devolatilization process, producing more volatile and tar. Gas temperature fluctuation

363

with higher mean temperature and amplitude also facilitates the production of volatile

364

and tar, with the devolatilization process starting earlier.

365

(3) Coal particles with smaller diameters are more sensitive to the gas

366

temperature fluctuation, and are easier and faster to devolatilize, resulting in higher

367

volatile and tar yield. Coal particle with smaller diameters shows a shorter delay in

368

responding to the gas temperature fluctuation. Particularly, the CPD predictions show

369

a delay time (a peak devolatilization rate) of 0.3µs, 2µs, 4.0µs and 5.0µs (292 s-1, 147

370

s-1, 53 s-1 and 18 s-1) for coal particles with diameters of 10 µm, 30µm, 50µm and 100


Energy & Fuels 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

371

µm, respectively.

372

(4) Compared to the predictions of the CPD model, the coal devolatilization

373

process predicted by the TS model usually starts earlier and the TS model

374

overestimates the final volatile yield. The TS model predictions have a shorter delay

375

and a larger fluctuation amplitude of coal particle temperature compared to the CPD

376

model, which indicates that the TS model is more apt to be affected by the gas

377

temperature fluctuation than the CPD model.

378

It is worth noting that the present work focuses on a zero-dimensional

379

configuration without considering complex turbulent flows in realistic situations;

380

some conclusions may be not totally applicable to a realistic situation where coal

381

particles interact with flows [32, 33]. The present study will be extended to

382

comprehensively explore the coal devolatilization characteristics in more realistic

383

situations in future work.

384 385

Acknowledgement

386

The authors are grateful for support from the National Natural Science Foundation of

387

China (Grant 51390493). The authors also thank Bincheng Lin at Zhejiang University

388

for helpful discussions.

389 390 391

References

392

[1] International Energy Agency (IEA), Key World Energy Statistics, IEA, Paris, France, 2016.


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biomass fuels. Prog. Energy Combust. Sci. 2012, 38, 113-137.

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[3] Williams, A.; Pourkashanian, M.; Jones, J. M. Combustion of pulverised coal and biomass.

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[4] Liu, X.; Chen, M. Q.; Wei, Y. H. Combustion behavior of corncob/bituminous coal and

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hardwood/bituminous coal. Renew. Energy 2015, 81, 355-365.

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[5] Smoot, L. D.; Smith, P. J. Coal combustion and gasification, Plenum Press: New York, 1985.

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[6] Anthony, D. B.; Howard, J. B. Coal devolatilization and hydrogasification. AIChE J. 1976, 22,

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625-656.

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[7] Moroń, W.; Rybak, W. Ignition behavior and flame stability of different ranks coals in oxy fuel

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atmosphere. Fuel 2015, 161, 174-181.

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[8] Luo, K.; Bai, Y.; Jin, T.; Qiu, K. Z.; Fan, J. R. Direct numerical simulation study on the

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stabilization mechanism of a turbulent lifted pulverized coal jet flame in a heated coflow. Energy

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Fuels 2017, 31, 8742-8757.

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[9] Chen, L.; Yong, S. Z.; Ghoniem, A. F. Oxy-fuel combustion of pulverized coal:

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characterization, fundamentals, stabilization and CFD modeling. Prog. Energy Combust. Sci. 2012,

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38, 156-214.

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[10] Li, S. Y.; Li, H. Y.; Li, W. Xu, M. X.; Eddings, E. G.; Ren, Q. Q.; Lu, Q. G. Coal

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combustion emission and ash formation characteristics at high oxygen concentration in a 1 MWth

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pilot-scale oxy-fuel circulating fluidized bed. Appl. Energy 2017, 197, 203-211.

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[11] Luo, K.; Xing, J. K.; Bai, Y.; Fan, J. R. Universal devolatilization process model for

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numerical simulations of coal combustion. Energy Fuels 2017, 31, 6525-6540.


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[12] Fletcher, T. H.; Kerstin, A. R.; Pugmire, R. J.; Grant, D.M. Chemical percolation model for

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devolatilization. 2. Temperature and heating rate effects on product yields. Energy Fuels 1990, 4,

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54-60.

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[13] Solomon, P. R.; Hamblen, D. G.; Carangelo, R. M.; Serio, M.A.; Deshpande, G.V. General

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model of coal devolatilization. Energy Fuels 1988, 2, 405-422.

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[14] Zhang, H. T.; Zhang, J. Instantaneous devolatilization of pulverized coal particles in a hot gas

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with fluctuating temperature. Combust. Flame 2008, 153, 334-339.

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[15] Ubhayakar, S. K.; Stickler, D. B.; Rosenberg-Jr, C. W. V.; Gannon, R. E. Rapid

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devolatilization of pulverized coal in hot combustion gases. Symp. (Int.) Combust 1977, 16,

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427-436.

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[16] Fletcher, T. H.; Kerstein, A. R.; Pugmire, R. J.; Solum, M. S.; Grant, D. M. Chemical

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percolation model for devolatilization. 3. Direct use of carbon-13 NMR data to predict effects of

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coal type. Energy Fuels 1992, 6, 414-431.

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[17] Yang, H.; Li, S. F.; Fletcher, T. H.; Dong, M. Simulation of the Swelling of High-Volatile

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Bituminous Coal during Pyrolysis. Part 2: Influence of the Maximum Particle Temperature.

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Energy Fuels 2015, 29, 3953-3962.

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[18] Grant, D. M.; Pugmire, R. J.; Fletcher, T. H.; Kerstein, A. R. Chemical model of coal

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devolatilization using percolation lattice statistics. Energy Fuels 1989, 3, 175-186.

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[19] Kobayashi, H.; Howard, J. B.; Sarofim, A. F. Coal devolatilization at high temperatures. Symp.

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(Int.) Combust. 1977, 16, 411-425.

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[20] Cho, C. P.; Jo, S.; Kim, H. Y.; Yoon, S. S. Numerical studies on combustion characteristics of

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interacting pulverized coal particles at various oxygen concentration. Numer Heat Transfer, Part A:


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Appl 2007, 52, 1101-1122.

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[21] Richards, A. P.; Fletcher, T. H. A comparison of simple global kinetic models for coal

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devolatilization with the CPD model. Fuel 2016, 185, 171-180.

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[22] CBC workshop, Workshop on measurement and simulation of coal and biomass conversion,

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available at: http://www.cbc.uni-due.de/?file=flame-database.

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[23] Luo, K.; Wang, H. O.; Fan. J. R. Direct numerical simulation of pulverized coal combustion

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in a hot vitiated co-flow. Energy Fuels 2012, 26, 6128-6136.

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[24] National Institute of Standards and Technology (NIST), NIST Chemistry WebBook, Available

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at: http://webbook.nist.gov/chemistry/.

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[25] Poling, B. E.; Prausnitz, J. M.; Connell, J. P. O. The properties of gases and liquids (fifth

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edition), McGraw-Hill, 2001.

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[26] Gao, Y. C.; Chan, C. K.; Lau, K. S. Numerical studies of pulverized coal combustion in a

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tubular coal combustor with slanted oxygen jet. Fuel 2003, 82, 893-907.

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[27] Cen K. F.; Yao Q.; Luo Z. Y.; Li X. T. Advance combustion theory; Zhejiang University Press:

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Hangzhou, 2002.

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[28] Hedman, P. O.; Warren, D. L. Turbulent velocity and temperature measurements from a gas

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fueled technology combustor with a practical fuel injector. Combust. Flame 1995, 100, 185-192.

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[29] Medwell, P. R.; Chen, Q. N.; Dally, B. B, Mahmoud, S.; Alwahabi, Z. T.; Nathan, G. J.

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Temperature measurements in turbulent non-premixed flames by two-line atomic fluorescence.

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Proc. Combust. Inst. 2013, 34, 3619-3627.

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[30] Wang, G. H.; Clemens, N. T.; Varghese, P. L.; Barlow, R. S. Turbulent time scales in a

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nonpremixed turbulent jet flame by using high-repetition rate thermometry. Combust. Flame 2018,


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152, 317-335.

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[31] Serio, M. A.; Hamblen, D. G.; Markham, J. R.; Solomon, P. R. Kinetics of volatile product

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evolution in coal pyrolysis experiments and theory. Energy Fuels 1987, 1, 138-152.

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[32] Toth, P.; Draper, T.; Palotas, A. B.; Ring, T. A.; Eddings. E. G. Three-dimensional combined

463

pyrometric sizing and velocimetry of combusting coal particles. I. Velocimetry. Applied Optic

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2015, 54, 4049-4060.

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[33] Toth, P.; Draper, T.; Palotas, A. B.; Ring, T. A.; Eddings. E. G. Three-dimensional combined

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pyrometric sizing and velocimetry of combusting coal particles. II: Pyrometry. Applied Optics

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2015, 54, 4916-4926.

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Page 22 of 34

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Energy & Fuels

469

Tables

470 471

Table 1. Elemental composition and volatile content of the coal studied [25]. Coal Name Beulah Zap

472 473

%C (daf) 66.5

%H (daf) 4.8

% O\ (daf) 26.5

VM (daf) 47.5

daf - dry ash free basis. a – as received.

474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490


%Xash (a) 14.50

%XVM

%XFC

(a) 36.86

(a) 40.64

Energy & Fuels 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

Page 24 of 34

491

Table 2. Calculation set up for particle diameter, time-average and fluctuation amplitude of gas

492

temperature. Particle diameter, dp (µm)

10, 30, 50, 100

Time-averaged gas temperature, T (K)

1000, 1100, 1200

Fluctuation amplitude of gas temperature, AT

0, 0.1, 0.2

493 494 495 496 497 498 499 500 501 502 503 504 505 506


Page 25 of 34 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

Energy & Fuels

507

Figures

508

509 510

Figure 1. Schematic diagram of the reaction paths of the two-step (TS) model

511


Energy & Fuels 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

kδ

ψ 512 513

kb

ψ* kc

Page 26 of 34

2δ

kg

c + 2g 2

Figure 2. Simple kinetic scheme in the CPD model

514


2g1

Page 27 of 34 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

Energy & Fuels

515 516 517 518

Figure 3. Detailed calculation procedures for the coal devolatilization processes employed in the CPD model.


Energy & Fuels 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

519 520

521 522

Figure 4. Comparisons of the experimental results [31] with predictions of the TS and CPD

523

models for volatile production using North Dakota lignite.

524 525


Page 28 of 34

Page 29 of 34

Particle/Gas temperature /K

526

1300 1100 900

1500 Par-CPD-Tave=1000 K Par-CPD-Tave=1100 K Par-CPD-Tave=1200 K Par-TS-Tave=1000 K Par-TS-Tave=1100 K Par-TS-Tave=1200 K Gas-Tave=1000 K Gas-Tave=1100 K Gas-Tave=1200 K

700 500 300

0

0.05

1300 1100 900 700 0.07

0.08

0.1 Time /s (a)

0.09

0.15

0.1

0.2

Total release volatiles

0.6 0.5 0.4 0.3 CPD-Tave=1000 K CPD-Tave=1100 K CPD-Tave=1200 K TS-Tave=1000 K TS-Tave=1100 K TS-Tave=1200 K

0.2 0.1 0

0

50 Volatiles release rate /(1/s)

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

Energy & Fuels

0.05

0.15

0.2

50

40 25

30 00

20

0.01

0.02

0.03

0.04

0.05

CPD-Tave=1000 K CPD-Tave=1100 K CPD-Tave=1200 K TS-Tave=1000 K TS-Tave=1100 K TS-Tave=1200 K

10 0 0

527 528 529

0.1 Time /s (b)

0.05

0.1 0.15 0.2 Time /s (c) Figure 5. Comparisons of the simulation results of the TS and CPD models under gas temperature fluctuation with different time-averaged temperatures: (a) particle and gas temperature; (b) total released volatiles; (c) volatile release rate. Tave is the mean gas temperature.


Energy & Fuels

530

0.15

Tar yield

0.1

0.05 CPD-Tave=1000 K CPD-Tave=1100 K CPD-Tave=1200 K

0

0

0.05

0.1 Time /s (a)

0.15

0.2

30 Tar release rate /(1/s)

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

Page 30 of 34

25 20

CPD-Tave=1000 K CPD-Tave=1100 K CPD-Tave=1200 K

15 10 5 0

531 532

0.02 0.03 0.04 0.05 Time /s (b) Figure 6. Comparisons of the tar prediction with the CPD model under gas temperature fluctuation with different average temperature: (a) tar yield; (b) tar release rate. 0

0.01

533 534


Page 31 of 34

Particle/Gas Temperature /K

535 536

1300 1100 900 Par-CPD-AT=0.0 Par-CPD-AT=0.1 Par-CPD-AT=0.2 Par-TS-AT=0.0 Par-TS-AT=0.1 Par-TS-AT=0.2 Gas-AT=0.0 Gas-AT=0.1 Gas-AT=0.2

700 500 300

0

0.05

1400 1200 1000 800 0.08 0.085 0.09 0.095

0.1 Time /s (a)

0.15

0.1

0.2


0.6 0.5 0.4 0.3 CPD-AT=0.0 CPD-AT=0.1 CPD-AT=0.2 TS-AT=0.0 TS-AT=0.1 TS-AT=0.2

0.2 0.1 0

0

0.05

0.1 Time /s (b)

0.15

0.2

60 Volatile release rate /(1/s)

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

Energy & Fuels

50 40 60

30 40

20 20

10 0

537 538 539

CPD-AT=0.0 CPD-AT=0.1 CPD-AT=0.2 TS-AT=0.0 TS-AT=0.1 TS-AT=0.2

00

0.02

0.04

0.1 0.15 0.2 Time /s (c) Figure 7. Comparisons of the simulation results of the TS and CPD models under gas temperature with different fluctuation amplitudes: (a) particle temperature; (b) total released volatiles; (c) volatile release rate. 0

0.05

540


Energy & Fuels

541

0.15

Tar yield

0.12 0.09 0.06 CPD-AT=0.0 CPD-AT=0.1 CPD-AT=0.2

0.03 0

0

0.01

0

0.01

0.02 0.03 Time /s (a)

0.04

0.05

30 25

Tar release rate (1/s)

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

Page 32 of 34

20 15 10 5 0

542 543

0.02 0.03 0.04 0.05 Time /s (b) Figure 8. Comparisons of the tar prediction with the CPD model under gas temperature fluctuation with different amplitudes: (a) tar yield; (b) tar release rate.

544 545


Page 33 of 34

Particle/Gas temperature /K

546

1300 1100 1400

900

1300

Par-CPD-dp=10 µm Par-CPD-dp=30 µm Par-CPD-dp=50 µm 1200 Par-CPD-dp=100 µm Par-TS-dp=10 µm Par-TS-dp=30 µm 1100 Par-TS-dp=50 µm Par-TS-dp=100 µm 1000 Gas 0.1

700 500 300

0

0.05

0.1 Time /s (a)

0.11

0.1

0.2

0.15

0.6


0.5 0.4 CPD-dp=10 µm CPD-dp=30 µm CPD-dp=50 µm CPD-dp=100 µm TS-dp=10 µm TS-dp=30 µm TS-dp=50 µm TS-dp=100 µm

0.3 0.2 0.1 0

0

0.05

0.1 Time /s (b)

0.15

0.2

300 Volatiles release rate (1/s)

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

Energy & Fuels

80

200 60

150

40

100

20 0 0

50 0

547 548 549

100

250

0

0.01

0.02

0.03

0.04

0.02

0.05

0.04 0.06 0.08 0.1 Time /s (c) Figure 9. Comparisons of simulation results for coal particles with different diameters using the TS and CPD models under gas temperature fluctuation: (a) particle temperature; (b) total released volatiles; (c) volatile release rate.


Energy & Fuels

550 551

0.15 Tar release rate /(1/s)

0.12 0.09 CPD-dp=10 µm CPD-dp=30 µm CPD-dp=50 µm CPD-dp=100 µm

0.06 0.03 0

0

0.02

0.04 0.06 Time /s (a)

0.08

0.1

200 Tar release rate /(1/s)

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

Page 34 of 34

150

100 CPD-dp=10 µm CPD-dp=30 µm CPD-dp=50 µm CPD-dp=100 µm

50

0 0

552 553

0.02

0.04 0.06 Time /s (b) Figure 10. Comparisons of the tar prediction of particle of different diameters with the CPD model under gas temperature fluctuation: (a) tar yield; (b) tar released rate.


Numerical Studies of the Coal Devolatilization Characteristics with

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