Chemical Engineering Science 200 (2019) 1–11
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CFD model of coal pyrolysis in fixed bed reactor Yanan Qian a,b,c, Jinhui Zhan a, Yin Yu d, Guangwen Xu e,⇑, Xiaoxing Liu a,b,⇑ a
State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China University of Chinese Academy of Sciences, Beijing 100049, China c State Key Laboratory of Safety and Control for Chemicals, SINOPEC Research Institute of Safety Engineering, Qingdao 266071, China d National Key Laboratory of Shock Wave and Detonation Physics, Institute of Fluid Physics, CAEP, Mianyang 621900, China e Institute of Industrial Chemistry and Energy Technology, Shenyang University of Chemical Technology, Shenyang 110142, China b
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
Coal pyrolysis performances in four
laboratory-scale fixed-bed reactors were modelled. Quantitative agreements between simulation results and experimental data were achieved. Influences of internals on heat transfer behaviour were analysed. Mechanisms for facilitating tar generation through mounting internals were discussed.
a r t i c l e
i n f o
Article history: Received 26 August 2018 Received in revised form 19 November 2018 Accepted 31 December 2018 Available online 23 January 2019 Keywords: Coal pyrolysis Fixed bed Heat transfer Internals Secondary cracking of tar CFD
a b s t r a c t A three dimensional transient model was developed to simulate the pyrolysis process of coal in fixed bed reactor. The model considered a number of aspects associated with coal pyrolysis such as the evaporation and condensation of physical water, the release of volatiles, secondary cracking of pyrolysis tar and the variation of porosity. Conductive, radiative and convective types of heat transfer were all taken into account in the model. The phase transformation of physical water was modeled using the Lee model (Lee, 1980) available in the commercial CFD software Fluent. The multiple independent parallel reaction (MIPR) model was applied to predict the generations of volatile products. The secondary cracking of tar was described using the model proposed by Wurzenberger et al. (2002). The model was implemented in Fluent and validated through modeling the pyrolysis process of low-rank sub-bituminous coal in four laboratory-scale and externally heated fixed-bed reactors with or without internals. The predicted temperature evolutions of coal charge were in all quantitative agreements with experimental results. And the influences of internals on the yield of tar were also successfully captured by the simulations. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction Coal is the major primary energy resource of China by taking about 70% in both energy production and consumption (China ⇑ Corresponding authors at: State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China (X. Liu); Institue of Industrial Chemistry and Energy Technology, Shenyang University of Chemical Technology, Shengyang 110142, China (G. Xu). E-mail addresses: [email protected]
(G. Xu), [email protected]
(X. Liu). https://doi.org/10.1016/j.ces.2018.12.064 0009-2509/Ó 2019 Elsevier Ltd. All rights reserved.
statistical yearbook, 2016). Of Chinese total coal reserve, more than 50% is low-rank coal including lignite and subbituminous coal (Bai, 2010). Thus, effective utilization of low-rank coal is crucial to China’s energy supply and security. Low-rank coal contains high volatile contents, and its direct combustion or gasification wastes such inherent oil and gas components. Pyrolysis can extract high-value coal tar and gas under relatively mild conditions, while its solid char can be utilized for combustion or gasification. Therefore, in the past decades great attentions have been paid to develop
Y. Qian et al. / Chemical Engineering Science 200 (2019) 1–11
high-efficiency coal pyrolysis technologies (Xue et al., 2017). Depending on heating strategies, coal pyrolysis technologies developed so far can be roughly divided into two types (Zhang et al., 2013): direct-heating type which uses high-temperature solid or gas as heat carrier, and indirect-heating type for which coal charge is heated through the heat transfer from externally heated walls and the reactors are generally fixed/or moving beds. Comparing with the direct-heating technologies, the indirect-heating technologies can produce liquid fuels with low dust content and fuel gas free of dilution (Zhang et al., 2014a,b). The disadvantage is the relatively lower heating rate of coal particles. Fundamental studies concerning various factors such as coal type (Pretorius et al., 2017), temperature (Pielsticker et al., 2017; Zhang et al., 2016), heating rate (Khan, 1989; Wiktorsson and Wanzl, 2000), coal particle size (Zhu et al., 2008), pyrolysis atmosphere (Jamil et al., 2004; Liu et al., 2017), etc., have been conducted to evaluate how they influence the yields and qualities of pyrolysis products including tar and gas. Up to now, it has been well understood that increasing the heating rate of coal particles can elevate the yield of tar (Hayashi et al., 2000; Liu et al., 1993). It has also been recognized that the rapid cooling of the primary pyrolysis products favors the increase of tar yield (Franklin et al., 1982). Benefiting from such knowledge, in the past years considerable efforts have been contributed in our institute to develop newly configured fixed-bed pyrolyzer (Cheng et al., 2017; Lin et al., 2015b,2016; Siramard et al., 2016; Zhang et al., 2014a,b). Inside the new pyrolyzer, metallic heating plates are mounted to enhance the heat transfer and a specially designed central gas collection pipe is used to regulate the flow of gaseous pyrolysis products from high temperature regions to low temperature zones in the reactor. Experimental results for coal and also oil shale show that the use of such particularly designed internals notably enhanced heat transfer and increased the yield and quality of pyrolysis tar, and it particularly realized high tar yield and quality even at very high heating temperatures such as above 1000 °C (Cheng et al., 2017; Lin et al., 2015b; Zhang et al., 2014a,b). Surely, it is generally difficult to get the precise details of temperature and product generation in the pyrolysis reactor through experimental measurement, even though it is not impossible (Zhang et al., 2014a; Hu et al., 2017a). On the other hand, the better understanding of such information is important and needed for justifying the experimental results and also for further optimization and scale-up of the newly developed pyrolyzer. With the significant improvements in high-performance computers and advances in numerical techniques and algorithms, Computational Fluid Dynamics (CFD) simulations have been increasingly used as a supplementary method of chemical experiments to explore the complex transfer and reactive characteristics of reactors. By incorporating physical and chemical reactions and interphase heat and mass transfer, CFD simulations have been used to model different systems such as biomass fast pyrolysis and combustion (Karim and Naser, 2018a,b; Xiong et al., 2013), coal combustion and gasification (Al-Abbas et al., 2012; Bhuiyan and Naser, 2015; Jeong et al., 2017), and fluid catalytic cracking (Nikolopoulos et al., 2014). Researches focusing on CFD modeling of coal pyrolysis in different types reactors including drop tube furnace (Hart et al., 2013; Meesri and Moghtaderi, 2003), entrainedflow reactor (Tchapda and Pisupati, 2015; Vascellari et al., 2015) and downer reactor (Shu et al., 2016), have been reported in the literature. There are also CFD studies on coal pyrolysis in fixed-bed reactors but they are mainly about coal coking (Guo et al., 2005; Lin et al., 2015a; Polesek-Karczewska et al., 2015; Slupik et al., 2015). And the research attentions are generally paid to the influence of the formed plastic layer on the flow of gaseous products in the coal bed. The correctness of CFD simulations is closely related to the adopted constitutive laws used to close the governing
equations (Shu et al., 2016). Thus the validation of numerical results is necessary before conducting systematical CFD simulations. For CFD modeling of the pyrolysis process of coal in fixed bed, key constitutive laws include heat conduction and radiative models, moisture phase transition model and coal pyrolysis model. In our previous works, we systematically evaluated the existing major heat conduction and radiation models for packed bed reactors and identified the optimal heat transfer models suitable for coal pyrolysis fixed bed system (Qian et al., 2018a). Combing with the proper treatment of moisture phase transition, we then modeled heat transfer behavior of a laboratory-scale fixed-bed coal pyrolyzer without internals and achieved the quantitative prediction of the temperature evolution of coal charge (Qian et al., 2018b). In this work, the pyrolysis processes of low-rank subbituminous coal in the four experimentally tested laboratory-scale fixed-bed pyrolyzors (Zhang et al., 2014b; Lin et al., 2015b) are simulated by considering the secondary reaction of primary pyrolysis products. The basic objective of this work is to evaluate the predictive ability of the built simulation framework about the influences of internals on the heating and pyrolysis behavior. It is found that the predicted temperature profiles of coal charge are in quantitative agreement with experimental results. The influences of internals on the pyrolysis performance experimentally observed are also successfully captured. This work thus lays solid foundations for our future investigations of the transfer and reactive performance of large scale pyrolyzer so as to guide the scale-up and optimization of the newly developed pyrolyzer.
2. Simulation details 2.1. Simulation set-up The simulated systems were experimentally investigated by Zhang et al. (2014b) and Lin et al. (2015b). Fig. 1 gives a schematic diagram of the experimental apparatuses. The four types of cylindrical fixed-bed reactors were all made of stainless steel with an inner diameter of 100 mm and an effective volume of 1500 mL for loading solids material (coal). Of them, the reactor A was a conventional fixed bed without any internals, the reactor B had a central gas collection pipe with a diameter of 10 mm and opening section height of 120 mm connected to the reactor exit so as to adjust the flow direction of gaseous pyrolysis products inside the reactor, the reactor C was mounted with four stainless-steel heating plates (perpendicular to the reactor wall and mutually at an angle of 90°) of 35 mm wide and 120 mm high for enhancing the heat transfer from the heated outside wall of the reactor to the loaded coal charge, and the reactor D was mounted with both a central gas collection pipe (as for the reactor B) and four metallic heating enhancement plates (as for the reactor C). In the experiments, the reactors loaded with coal particles were quickly placed into an electrical heating furnace with a preset temperature. The temperature evolution of the coal charge was measured through a thermocouple having its free end placed at the center of the bed. For the reactors B and D the free end of the thermocouple was placed at a position next to the wall of the central gas collection pipe. The gaseous products of pyrolysis were collected through a production collection system. The charged solids particles in Zhang et al. (2014b) and Lin et al. (2015b) were lowrank sub-bituminous coal (Yilan coal) and oil shale, respectively. More details about the experiments can be found in references (Zhang et al., 2014b; Lin et al., 2015b). In this work, we mainly compare our simulation results with the experimental data of Zhang et al. Table 1 lists the major properties of the tested Yilan coal.
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Fig. 1. Schematic plots for the four fixed-bed reactors A to D.
Table 1 Properties of Yilan coal sample. Proximate analysis (ad, wt%)
Ultimate analysis (daf, wt%)
G-K (d, wt%)
2.2. CFD model The Eulerian Two-Fluid model (TFM) available in the commercial CFD software package Fluent 6.3.26 was used to model the investigated four fixed-bed reactors. Table 2 lists the governing equations and the main constitutive relations used. Note that the solid phase was treated as being stationary. Thus, the momentum conservation equation for solid phase was ignored and the velocity of solids was set to be zero. Based on our previous work (Qian et al., 2018a,b), this work adopted the Zehner-Bauer-Schlunder (ZBS) model (Bauer and Schlünder, 1978) for thermal conductions through solid and gas phases, and the Breitbach and Barthels (BB) model (Breitbach and Barthels, 1980) for the effective radiative conductivity due to solid radiation. The heat transfer due to gas radiation was described through the discrete ordinates (DO) model (Modest, 2003), and the conventional Gunn’s heat transfer model (Gunn, 1978) was used to calculate the convective heat transfer coefficient. 2.2.1. Phase transition of moisture The raw coal particles contained 4.61 wt% physical water (Zhang et al., 2014b). It has been well recognized that the heating progress of coal charge is closely related to water evaporation and condensation (Polesek-Karczewska et al., 2015; Hu et al., 2017b). The phase transformation of physical water content is expressed as
H2 OðlÞ $ H2 Oðg Þ
The evaporation takes place when the liquid water temperature Tl is higher than the saturation temperature Tsat , and condensation occurs when the temperature of water vapor Tv is lower than Tsat . _ H2O can be calculated as The evaporation and condensation rates m (Lee, 1980)
_ H2O m
8 h i > < Cev ql el TlTTsat ; sat h i ¼ > : Ccon qv ev TvTTsat ; sat
Tl Tsat ðev aporationÞ Tv Tsat ðcondensationÞ
where ql and qv are densities, el and ev are volume fractions of liquid water and water vapor, respectively. And Cev and Ccon are coefficients of evaporation and condensation, respectively. Cev (Ccon ) is a function of evaporation (condensation) surface, evaporation (condensation) coefficient, latent heat of water, etc. As stated in Fluent theory guide, the evaporation (condensation) surface and evaporation (condensation) coefficient are usually not very wall know. Thus, Cev and Ccon generally serve as tuning parameters. Based on our previous simulation results (Qian et al., 2018b), in this work Cev and Ccon were set to be 5 and 6, respectively. And the heat source due to the phase transition of moisture was set to be 4.4 104 J/mol (Shu et al., 2016). 2.2.2. Pyrolysis kinetic model Coal pyrolysis is a complicate process and its mechanism is still elusive. Researchers proposed various models to describe this process, such as single-step kinetic model (Badzioch and Hawksley, 1970), competing two-step reaction model (Kobayashi et al., 1976), multiple independent parallel reaction (MIPR) (Suuberg et al., 1978) and network model (Niksa, 1991; Solomon et al., 1988). In this work, the MIPR model was adopted to describe the generation of volatile products (assuming to be H2, CO, CO2, CH4, C2H6, H2O, Tar). In this model the kinetic equation of each volatile product is formulated as
_ coal!speciesi ¼ m
dwi Ei wi wi ¼ Ai exp dt RTs
where Ai is the pre-exponential factor in s1, Ei is the apparent activation energy in J/mol, i is species, wi is the amount of species i pro-
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Table 2 Governing equations and constitutive correlations. Continuity equations for gas (g) and solid (s) phases: ! ! @ @ _ sg @t _ sg ¼m ðes qs Þ þ r es qs us ¼ m @t eg qg þ r eg qg ug P _ coal!speciesi m Momentum conservation equation for gas and solid phase: I¿ ! ! ! ! ! ! ! ! ! @ _ sg ! us ug us ¼ 0 þ r eg qg ug ug ¼ eg rp þ r eg sg þ eg qg g þ bgs us ug þ m @t eg qg ug
eg +es = 1, m_ sg ¼ m_ H2O þ
energy conservation equation for gas and solid phases: I¿ ! ! @p @ @ @t eg qg H g þ r eg qg ug H g ¼ eg @t þ sg : r ug þ r eg kg;e rT g hgs T s T g þ Sradi;g þSH2 O @t ðes qs H s Þ I¿ ! ! þr es qs us Hs ¼ es @p @t þ ss : rus þ r es ks;e rT s hgs T g T s þ Sradi;s SH2 O þ Sreaction Transport equations for species in gas phase (species i) and solid phase (species j): P ! P P ! ! @ @ _ _ 0 M _ 0 m þ M i¼1 ms j g i @t es qs Y s;j þ r es qs us Y s;j ¼ j¼1 ms j g i @t eg qg Y g;i þ r eg qg ug Y g;i ¼ r eg J g;i j –j s;jj Constitutive relations for heat transfer: Effective thermal conductivities of gas and solid phases (Zehner-Bauer-Schlünder (ZBS) model): pﬃﬃﬃﬃﬃﬃﬃﬃ pﬃﬃﬃﬃﬃﬃﬃﬃ 1eg ½xjþð1xÞCkg ð1 1eg Þkg ; ks;e ¼ kg;e ¼ eg 1eg where
C ¼ 12 B
10=9 j1 B ln j B1 1 ðB þ 1Þ B ¼ 1:25 1eg x ¼ 7:26 103 2 B eg 2 1jB ð1BÞ j j
Effective radiative conductivity of solid phase (Breitbach and Barthels (B-B) model): Sradi;s ¼ r q;s , qradi;s ¼ ks;e rT s , ks;e ¼ 4F E rdp T s 3 r
where pﬃﬃﬃﬃﬃﬃﬃﬃ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 1e F E ¼ ð1 1 eg Þeg þ 2=er 1g Bþ1 B 1þ
1 ð2=er 1ÞKf
Kf ¼ 4rdks T 3 p s
Radiation of gas phase (DO model): R rT 4 ! !! ! ! ! ! ! Sradi;g ¼ r 4p Ið r ; s Þ s dIr I r ; s s þ aI r ; s ¼ a pg Gas–solid heat transfer coefficient (Gunn’s model):: hgs ¼
6kg es eg Nus dp 2
Nus ¼ 7 10eg þ 5eg 2 1 þ 0:7Res 0:2 Pr 1=3 þ ð1:33 2:4eg þ 1:2eg 2 ÞRes 0:7 Pr 1=3 Res ¼
dp jug us jqg
, Pr ¼
C pg lg . kg
duced up to time t, and wi is the amount of species i that could potentially be produced. In order to use the MIPR model, the kinetic parameters of Ai and Ei must be determined in prior. Nevertheless, coal pyrolysis is rather complicated and each species may be generated from many different reaction pathways. Thus, the precise determinations of Ai and Ei for each species are nearly impossible. In this work, we referred to the functional-group model of coal pyrolysis proposed by Jüntgen (1984). According to the study of Juntgen, the kinetic parameters of each species are independent of coal type. Based on this assumption, the data published by Suuberg et al. (1978) which have been used by various researchers (Liang et al., 2008; Lin et al., 2015a; Zhang et al., 2015), were adopted in this work, as listed in Table 3. The final volatile product yields, wi , are related to coal type. The potential yield of tar was determined by the GrayKing assay tests, and those of pyrolysis gases (H2, CO, CO2, CH4, C2H6, H2O) were obtained by the fixed-bed experiments of coal pyrolysis at heating temperature of 1100 °C (Zhang et al., 2014b).
Table 3 Kinetic parameters of each volatile products (Suuberg et al., 1978). Species i
1 1 2 1 2 1 2 3 1 1 1
20.0 55.0 2.5 103 550 230 1.7 105 2.8 104 3.0 104 1.7 106 1.67 1.17 109
93,214 75,240 126,236 81,510 96,140 129,580 129,580 147,554 139,612 51,910 160,320
0.90 1.71 1.63 2.78 1.81 0.87 0.98 1.47 0.93 8.82 11.80
C2H6 H2O Tar
reactions of tar. The cracking of tar was described by the following model (Wurzenberger et al., 2002)
vC H 2
C2 H6 þ
v CO CO þ ! v CO
_ tar!speciesðjÞ was described as and the cracking rate of tar m 2.2.3. Cracking of tar In real coal pyrolysis systems the secondary reactions would inevitably take place to primary pyrolysis products. In order to qualitatively evaluate the influences of internals in the reactor on the final yields of pyrolysis products, two series of simulations were conducted by considering or not considering the secondary
E _ tar!speciesðjÞ ¼ ! m v j Acrack exp crack Y g:tar qg RTg
where Y g:tar qg is the amount of tar in the gas phase. Following the work of Jia et al.(2004), the pre-exponential factor Acrack and the
Y. Qian et al. / Chemical Engineering Science 200 (2019) 1–11 Table 4 Stoichiometric coefficients of tar cracking products (Jia et al., 2004).
3. Results and discussion 3.1. The character of heat transfer
C2 H 6
apparent activation energy Ecrack were respectively set to be 6634 s1 and 7.78 104 J/mol, and the stoichiometric coefficients ! of each species v j are listed in Table 4. 2.3. Simulation layout The considered four cylindrical reactors were numerically discretized using the hexahedral grids. The maximal grid size was 2 mm. Supplementary simulations show that the adopted grid size was fine enough to achieve grid independence. The initial temperatures of the packed gas and solid phases were set to be room temperature (27 °C). Constant temperature boundary condition was set for the side wall of the cylindrical reactor. The wall temperature was assumed to be the same as the preset temperature of the heating furnace (700 and 900 °C). The base wall of the reactor was set to be adiabatic. The exit at the top of the reactor was set as pressure outlet (atmosphere pressure). In the simulations the secondorder upwind scheme was used to discretize the equations of momentum, energy and discrete ordinates. The mass conservation equations were solved by the QUICK scheme. Table 5 summarizes the main properties of gas and solid phases and also the main parameter settings used in the simulations of this work. The initial species mass fraction of solid phase was determined by the proximate analysis of Yilan coal as shown in Table 1, where the coal charge consisted of 4.61 wt% H2O (l) and 95.39 wt% coal (including ash). All the required functions describing heat transfer and the source terms that appear in the transport equations but not available in Fluent 6.3.26 were implemented into the software through the user-define function (UDF): The source terms accounting for the generations of species and heat due to chemical reaction were coded by using the UDF DEFINE macros of DEFINE_EXECYTE_AT_END, DEFINE_SOURCE, and DEFINE_MASS_TRANSFER. Parameters related to heat transfer were calculated through the UDF DEFINE macro of DEFINE_PROPERTY. The Gunn’s model was solved through the UDF DEFINE macro of DEFINE_EXCHANGE_PROPERTY. Table 5 Properties of solid and gas phases. Parameter
Average diameter of coal, mm Initial voidage Density of coal, kg/m3 Specific capacity of coal (Agroskin et al., 1970), J/(kgK)
2a 0.4a 1400a 1150 1150 + 2.03 (T-300) 0.00155 (T-300)2 0.19 0.19 + 0.00025 (T-300) Volume-weightedc Mass-weightedd Mass-weightede
Thermal conductivity of coal (Agroskin, 1957), W/(mK) Density of gas phaseb Specific capacity of gas phaseb Thermal conductivity of gas phaseb
T 300 °C T > 300 °C T 300 °C T > 300 °C
Experimentally measured. The physical properties of each species of gas phase were cited from Shu et al. (2016). c The volume-weighted density of the mixture is calculated by q ¼ P1 Y i . b
The mass-weighted specific capacity of the mixture is calculated by P C p ¼ i Y i C p;i . e The mass-weighted thermal conductivity of the mixture is calculated by P k ¼ i Y i ki .
Fig. 2 compares the predicted and experimentally measured temperature evolutions at the centers of the investigated four reactors so as to analyze the thermochemical characteristics of different reactors and also to validate the simulation results. Good agreements between the numerically predicted and experimentally measured results at the tested two furnace temperatures were achieved for all the four reactors. It can be seen that the temperature first increased to the level of 100 °C and maintained at this temperature for a while before it further continued to rise. The constant temperature plateau represents the evaporation stage of coal water. Fig. 2 reveals that mounting the designated internals significantly shortened the time period of constant temperature plateau. It means that the drying process of coal charge was remarkably accelerated by the addition of internals. The drying process can be more clearly observed from the predicted dynamics of coal dehydratation shown in Fig. 3. The Y-axis of Fig. 3 indicates the phase transformation rate of physical moisture, and its positive (negative) value means the evaporation of liquid water (the condensation of steam). Fig. 3 clearly indicates the fronts of water evaporation and condensation inside the coal bed. For example, one can find that for the reactor A at time t = 10 min the steam was mainly generated at the distance to the heating wall x 10 mm. Meanwhile, some of the steam evaporated in the coal layer closer to the heating wall condensed when it flowed through the coal zone remaining at temperatures below 100 °C. This took place at about x 16 mm in Fig. 3 for the reactor A. At t = 10 min, the average evaporation rates (area-average) were 0.0191, 0.0229, 0.0188 and 0.0227 kg/(m3s) for the reactors A to D, respectively. Their average steam-condensation rates were correspondingly 0.0076, 0.0194, 0.0075 and 0.0188 kg/(m3s), respectively. The ratios of condensation rate to evaporation rate for the reactors B and D (0.85 and 0.83) were notably larger than those for the reactors A and C (0.40 and 0.40). This can be ascribed to the fact that for the reactors A and C the generated steam tended to flow towards the heated reactor wall because of the relatively higher voidage in the vicinity of reactor wall. For the reactors B and D the steam had to flow through the inner low-temperature dense coal layer due to the installation of the central gas collection pipe. Note that for reactors B and D, steam was generated in the higher-temperature region. Thus, the inward flow of steam could preheat the inner coal layer. Fig. 4 presents the predicted heating curves monitored at different radial positions for the reactor A. The heating curves for the other three reactors and also at the lower heating furnace temperature of 700 °C are qualitatively similar to those presented in Fig. 4. It can be seen that the heating rates of coal bed varied greatly along the radial direction. The closer to the heating wall, the higher was the heating rate to the particle (coal) layer. In addition, the time durations for the temperature-constant plateau decreased along the radial direction from the heated wall to bed center. Such phenomenon can be attributed to the higher heating rate in the region closer to the reactor wall. Besides, the re-condensation of part of the generated steam in the inner bed zones led to higher moisture content in these regions, and consequently longer evaporation time. Table 6 lists the average heating rates calculated for the temperature rising from 100 °C to 500 °C at each individual position of the simulated reactors. For a given radial position, the heating rate was highest for the reactor D which was mounted with the internals of both metallic heating plates and a central gas collection pipe but lowest for the reactor A without any of such internals. Comparing
Y. Qian et al. / Chemical Engineering Science 200 (2019) 1–11
Fig. 2. Comparisons of predicted and experimentally measured temperature evolutions at the center of coal charge at heating furnace temperatures of (a) 700 °C and (b) 900 °C.
Fig. 3. Steam source distributions inside the coal charge of different reactors at the time of 10 min (at a heating furnace temperature of 900 °C). A to D: simulated evaporation rate of liquid water for reactor A to D; A0 to D0 : simulated condensation rate of steam for reactor A to D.
Fig. 4. The predicted heating curves monitored at different radial distance to the heating wall for the reactor A (at a heating furnace temperature of 900 °C).
to that of the reactor A, the higher heating rate of the reactor B suggests that discernible heat transfer took place between the gaseous pyrolysis products generated in the high-temperature zone and the inner low-temperature coal layers due to the inwards flow of gaseous products. In comparison with the reactor B, the heating was quicker in the reactor C, which means that mounting four metallic heating plates more facilitated the heat transfer to coal particles. The mechanism of heat transfer enhancement by mounting metallic heating plates was to increase the high-temperature surface area inside the particle bed because the heat conduction in the metallic plates was much quicker than in the coal particle bed. The increased high-temperature surface worked on heat transfer to coal bed through radiative and also conductive heat transfers. Fig. 5 will further clarify the dominant heat mechanism in the investigated reactors. Essentially, the coal bed in the reactor gained heat from the heated reactor wall through conduction, radiation and interphase convective heat transfer. Fig. 5(a) compares their relative contributions at a furnace temperature of 900 °C. The results at the heating furnace temperature of 700 °C are qualitatively similar to those presented in Fig. 5(a). It can be found that in the region close to the heating wall the main way of heat transfer to coal particles was radiation, which contributed about 80% heat flux. This is due to the high radiative conductivity of the high-temperature surface of heated reactor wall. In the central region of the bed, the dominant heat transfer mechanism at the early stage was conduction and later radiation, due to the increase of temperature. In comparison with the reactors A and B, the contributions of radiative heat transfer in the central region were clearly larger in the reactors C and D. The results indicate that the metallic heating plates mounted in the reactors C and D notably strengthened the radiative heat transfer, due to the notable increase of hightemperature surface areas in these reactors. Consequently, the temperatures of inner particle layers in the reactor C and D increased more quickly, as evidenced in Fig. 5(b). This in turn further raised the radiative heat transfer due to the fact that hightemperature particles sped up the overall heat transfer to inner particle layer. Adding a central gas collection pipe in the bed manifested a discernible effect on heat transfer as well. For the reactors A and C, the contribution of gas-solid inter-phase heat transfer to the temperature rise of inner particle bed was ignorable, whereas for the reactors B and D it became comparable with the contribution of conductive heat transfer in the central bed zone. This can be
Y. Qian et al. / Chemical Engineering Science 200 (2019) 1–11
Table 6 Predicted heating rates (°C/min) at different radial distances to the heating wall at a heating furnace temperature of (a) 700 °C and (b) 900 °C (Averaged results in the temperature range of (100 °C, 500 °C). Reactor
A B C B
(a) 700 °C
(b) 900 °C
64.7 78.5 85.0 95.1
15.6 16.8 18.2 20.5
5.2 6.6 8.3 10.4
4.0 5.5 8.5 10.6
4.5 5.9 7.6 9.7
130.0 148.9 180.1 210.0
31.0 35.4 38.4 56.2
11.7 17.7 18.2 19.6
8.0 12.8 19.5 20.3
8.9 13.0 15.8 16.2
Fig. 5. (a) Relative contributions of different heat transfer mechanisms at a furnace temperature of 900 °C and (b) temperature contours at t = 50 min.
attributed to the different main flow directions of gaseous pyrolysis products between the reactors mounted with central pipe (B and D) and the reactors without such a pipe (A and C), as demonstrated in Fig. 6.
3.2. Impacts of internals on pyrolysis performance Zhang et al.(2014b) have shown that a char bed generated from pyrolysis is more porous than the original Yilan coal bed. Similar results have also been reported in Lin et al. (2015b) for oil shale fixed-bed pyrolyzers. For the investigated four reactors in present study, this means that the radial distribution of bed voidage is non-uniform during pyrolysis. The closer the particle layer to the heating wall, the higher the particle bed voidage is. In our simulations, the increase of particle bed voidage was automatically realized through the release of gaseous pyrolsis products (Note that in all the simulation cases discussed here the momentum conservation equation for solid phase was ignored and the velocity of solids was fixed to be zero). Since gas always prefers to flow along the direction with lower pressure drop, the non-uniform radial distribution of bed voidage thus notably affected the flow behavior of
the generated gaseous pyrolysis products, as evidenced in Fig. 6 via CFD simulation. For the reactors A and C, the gaseous pyrolysis products tended to flow towards the heated reactor wall (s) before flowing out the system, whereas for the reactors B and D the central gas collection pipe provided the only way to escape the gaseous products from the reactor and thus created the counter flow of gas products into the inner coal layer rather than to the bed wall. These different flow patterns due to the installation of a central gas collection pipe have also been indirectly verified by Hu et al. (2017a) through experiments. Considering the temperature gradient along radial direction, such two different flow patterns mean that in the reactors A and C the gaseous pyrolysis products mainly took heat away to cool the generated high-temperature char near the bed wall, whereas in the reactors B and D the gas product stream had a higher temperature than the inner-region particles and thus preheated them when flowing though the inner bed. The flow direction of gaseous products significantly influences the distribution of pyrolysis products. Zhang et al.(2014b) presented the pyrolysis product distributions for a furnace temperature of 700 °C. Table 7 lists the simulation results for the same experimental conditions. As mentioned before, in this work two
Y. Qian et al. / Chemical Engineering Science 200 (2019) 1–11
Fig. 6. Streamlines of gaseous products in different reactors with color indicating temperature or generation rate of volatiles.
series of simulation cases were conducted by considering or not considering the secondary reaction of tar cracking. When tar cracking reaction was not included, the simulation results show that the
tar yield slightly increased in an order of reactor from A to D. This variation trend is consistent with the variation trend in heating rate. Because of the larger apparent activation energy of tar gener-
Y. Qian et al. / Chemical Engineering Science 200 (2019) 1–11 Table 7 Product yield distributions (wt%, d) in reactors A to D at a heating furnace temperature of (a) 700 °C and (b) 900 °C. Reactor
(a) 700 °C Charpa
A B C D
74.91 75.78 76.03 76.58
9.47/7.96 9.95/9.06 10.87/9.12 11.08/9.96
7.67 9.06 9.69 9.82
7.94/9.45 6.97/7.86 5.72/7.47 5.68/6.80
9.62 7.91 8.34 7.13
7.68 7.30 7.38 6.66
A B C D
(b) 900 °C 71.65 72.84 73.72 74.11
10.68/5.42 11.03/10.03 11.46/5.58 11.62/10.42
5.64 – – 10.34
9.40/14.66 8.17/9.17 6.71/12.59 6.44/7.64
14.39 – – 9.99
8.27 7.96 8.11 7.83
p Predicted product yield. eExperimental product yield (Zhang et al., 2014b). a Char is the residual of coal pyrolysis. b The simulated tar or gas yield without/with tar cracking.
ation reactions, quick heating is in favor of tar generation. Accounting for the tar cracking reactions tar yields decreased, and the pyrolysis product generation was closely related to the reactor type and also the heating furnace temperature. At a furnace temperature of 700 °C, the tar yield increased according to the reactor order of A to D and the tar yield for the reactor A was 1–2% lower than those for the other three reactors. The results are qualitatively in agreement with the experimental results of Zhang et al. (2014b). At a higher heating furnace temperature of 900 °C, the tar yields of the reactor A and C significantly decreased and were significantly lower than those of the reactors B and D. For the latter two reactors, including secondary reactions only slightly decreased the tar yields. This result was shown for coal by Zhang et al.(2014b) and also for oil shale by Lin et al. (2015b) experimentally. At the same furnace temperature of 900 °C, Lin et al. (2015b) found that the shale oil yields of the reactors A and C are notably lower than those of the reactors B and D. The simulation results presented in Table 7 thus suggest that for relatively low heating furnace temperatures (e.g., 700 °C) mounting metallic heating plates is an efficient way to increase the tar yield, whereas for high heating furnace temperatures (e.g., 900 °C) the installation of a central gas collection pipe to regulate the flow of pyrolysis gas products from high-temperature zone to low-temperature zone is essential to attain high tar yield. In the reactors A and C, the generated pyrolysis gas tended to flow into the more porous but higher-temperature char zone close to the reactor wall, which obviously promoted tar cracking and reduced the tar yield. In the reactors B and D, the produced gaseous products had to flow through the lower-temperature coal layer in the core region before escaping from the reactors. Here, the lowtemperature condition in the core obviously alleviated tar cracking when the gas products flow through there. Overall, by considering the heat transfer efficiency from the heated wall to coal bed and also the tar yield of pyrolysis, it can be concluded that mounting both metallic heating plates and a central gas collection pipe in an indirectly heated reactor, i.e., the reactor D, provides the optimal reactor configuration for generating high tar yield and increasing processing efficiency. This thus verifies the experimental finding for the fixed bed reactor with such particularly designed internals.
4. Conclusion In this work, the predictive ability of a two-fluid CFD simulation framework in investigating the heat transfer behavior and pyrolysis performance of coal in fixed bed reactors was evaluated. The four laboratory-scale indirectly heated fixed-bed pyrolyzors
which have been extensively investigated experimentally were modeled and the simulation results were compared with experimental data. For the four investigated reactors, the predicted temperature evolutions at two different furnace temperatures were all in quantitative agreement with experimental results. And the experimentally observed different variation trends of tar yields from reactor A to D were also successfully captured by the simulations. These quantitative and qualitative comparisons demonstrate that the built CFD simulation framework can be used to model our target coal pyrolysis system. This work thus lays solid foundations for our future investigations of the heat transfer and reactive performance of large scale pyrolyzer so as to guide its scale-up and optimization. In real fixed bed, the region close the reactor wall is generally more porous due to the geometrical effect (Arno, 2003). Deriving a general description of this more porous nature is difficult due to the fact that the characteristics of particle packing are related to not only particle properties (shape, roughness, size distribution, etc.) but also packing history (van Hecke, 2010). Thus, in this work the initial non-uniform porosity distribution of coal charge was not taken into account. Nevertheless, the well agreements between the predicted and experimentally measured temperature profiles suggest that for the coal pyrolysis systems investigated here, neglecting the more porous nature of coal zone close to heating wall might be acceptable. The simulation results demonstrate that in the region close to heating wall the dominant heat transfer mechanism was radiation. Around 80% heat flux was contributed by radiation. This could be attributed to the high temperature of heating wall and also the relatively small thermal conductivity of coal. Another simplification of our simulations was assuming the coal charge to be stagnant. It has been found experimentally that coal shrinkage/breakage took place during pyrolysis (Zhang et al., 2014b). Theoretically, this might lead to the formation of gap between reactor wall and coal/char charge (Lin et al., 2015a; PolesekKarczewska et al., 2015). Our simulation results suggest that, even if this phenomenon happens, its influence on heat transfer should be minor since in this region the dominant heat transfer mechanism is radiation. Most of the effective radiative conductivity models show that the effective radiative conductivity increases with the increase of porosity (Qian et al., 2018a; van Antwerpen et al., 2010). For pyrolyzors investigated in this work, the formation of gap would not change the flow direction of gaseous pyrolysis products. As it can be found from the simulation results that in reactors without central gas collection pipe gaseous products always tent to flow towards the heating wall, whereas in reactors with central gas collection pipe gaseous products had to flow inwards before escaping the reactors. Nevertheless, for large-scale fixed bed pyrolyzor, the formation of gap might lead to the short cut of gas flow and
Y. Qian et al. / Chemical Engineering Science 200 (2019) 1–11
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