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Numerical investigation of in situ gasification chemical looping combustion of biomass in a fluidized bed reactor
Renewable Energy xxx (xxxx) xxx
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Numerical investigation of in situ gasification chemical looping combustion of biomass in a fluidized bed reactor Weijie Yin, Shuai Wang*, Kai Zhang, Yurong He School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 January 2019 Received in revised form 2 November 2019 Accepted 4 November 2019 Available online xxx
In-situ gasification chemical looping combustion (iG-CLC) depends on the solid fuel gasification rate and the contact between gasification products and oxygen carriers. In this work, a three-dimensional simulation is carried out to investigate the iG-CLC performance of biomass in a fluidized bed reactor by means of the multi-fluid model with the bubble-based bi-disperse drag model to consider the bubble effect on the interphase drag force. The model can give a reasonable prediction on the experimental results. The result reveals that an increase of operating pressure of 0.2 MPa can promote the CO2 yield by 5%, whereas the change of the feed port height from the reactor bottom also greatly influences the outlet gas compositions. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Chemical looping combustion Fluidized bed Computational fluid dynamics Biomass
1. Introduction Chemical Looping Combustion (CLC) is a promising combustion technology owing to its inherent CO2 separation with zero energy penalty via oxygen carriers as chemical intermediates to achieve the oxygen transportation [1,2]. In contrast to gaseous fuels, solid fuels have been successfully applied in a chemical looping system owing to their advantages of abundance and environmental friendliness [3e5]. Zheng et al. [6] conducted an experimental study of the coal-fuelled CLC process using CaSO4eFe2O3 as oxygen carriers. It was pointed out that the integration of CaSO4 and Fe2O3 could facilitate the fuel conversion and improve the combustion efficiency with a low sulfur emission. Linderholm et al. [7] found that the manganese ore as an oxygen carrier showed a high reactivity with sufficient lift time. Among various solid fuels, biomass as one kind of renewable resources has attracted a great deal of concerns [8,9]. Mendiara et al. [10] compared different types of biomass in the CLC process under the iG-CLC mode. The result revealed that the increase of reaction temperature could achieve the almost 100% CO2 capture efficiency. The effect of CO2 and steam as gasification agents on the carbon conversion rate and CO2 capture efficiency was examined via a thermodynamic assessment. The CO2 was found to be a
* Corresponding author. E-mail address: [email protected] (S. Wang).
promising gasification agent [11]. The co-combustion of coal and biomass in a chemical looping system was experimentally investigated. The result demonstrated that the addition of biomass could reduce the gasification temperature and improve the coal conversion [12]. Computational fluid dynamics (CFD) can provide some detailed information on the gas-solid flow and reaction performance in a fluidized bed reactor, which has been applied in the investigation of a chemical looping system [13,14]. Peng et al. [15] employed the CFD-DEM to evaluate the mixing and segregation behaviors of solid mixture in a fuel reactor. It was found that the decrease of particle size ratio in a binary mixture could promote the mixing degree. The Euler-Euler model was also employed to evaluate the coal-fuelled CLC process by considering the thermochemical reaction mechanism. It was emphasized that the recirculation mass flow of oxygen carriers was very important in the simulation of fuel reactor [16]. A three-dimensional full loop simulation of the CLC system was implemented and the effect of operating parameters on solid circulation rate was analyzed. It was concluded that the increase of gas velocity could weaken the sensitivity of solid circulation rate to operating parameters [17]. The interphase drag model plays an important role in the prediction of the multiphase flow process via CFD approaches. For a large-scale CLC system, a coarse-grid simulation is necessary owing to the limitation of computational efficiency. When coarse grids are employed, the mesoscale structures including clusters and bubbles will result in the non-uniformity of gas-solid flow within the grid,
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which restricts the prediction accuracy using traditional drag models [18]. In recent years, a bubble-based energy-minimization multi-scale (EMMS) drag model has been proposed, resolving the drag into the three components from the emulsion phase, the bubble phase and the interphase [19,20]. By comparing the bed height and the voidage, it was found that the model prediction could give a better agreement with experimental data. In order to better describe the bubble effect in a poly-disperse system, a bidisperse drag model was developed simultaneously considering the effects of the voidage and solid component fraction on the basis of the bubble-based EMMS model [21]. The result demonstrated that the drag heterogeneity in a poly-disperse system greatly depended on solid component fractions. Whereas in a polydisperse reacting system like a solid-fuelled CLC system, few reports about the consideration of the bubble effect can be available. The present work aims to investigate the iG-CLC performance with biomass via CFD approaches incorporating a bubble-based bidisperse drag model to describe the bubble effect in a poly-disperse reacting system. The model prediction can give a good agreement with experimental data. Meanwhile, the interaction mechanism of oxygen carrier and biomass is further analyzed. The impacts of CO2 as a gasifying agent and in-bed fuel feed arrangement are evaluated.
particles is adopted as the model closure [22]. For a high particle concentration, the frictional effect between particles is significant. Here, a modified Savage model is adopted [23]:
0
1n1
pf V,us B C rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ @1 . ffiA pffiffiffi pc n 2sinðfÞ S : S þ q d2s
(1)
8 1024 ε* ε 10 εg < ε* g > > > > > 2 > > < 1 εg εmin s pc ¼ 0:05 ε* εg < 1 εmin 5 sf > * > εg ε > > > > > : 0 εg > 1 εmin sf
(2)
9 10 11=ðn1Þ > 0 8 pffiffiffi > > > = ε 2ps;f sin j < CBps;f C B s ffi n 1 mf ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n @ A A @ . > M > p P c 2: > > S : S þ q ds εm ;
(3)
m¼1
2. Mathematical model
The gas-solid interphase drag model plays a crucial role in the simulation of a fluidized bed, which will be discussed in the next section.
2.1. Multi-fluid model In this work, the multi-fluid model is employed to characterize flow behaviors of oxygen carrier and biomass particles in a CLC system. Here, the assumption is made that each solid phase has a uniform size. The governing equations mainly comprise continuity equations, momentum and energy conservation equations as well as species transportation equations. The detailed descriptions are summarized in Table 1. Granular temperature is introduced to describe the fluctuating energy of particles and the transportation equation is shown in eq (T1-9). The kinetic theory of polydisperse
2.2. A bubble-based bi-disperse drag model Bubbles as mesoscale structures in a bubbling fluidized bed influence the gas-solid interaction. Here, a bubble-based bidisperse drag model is employed in a bi-disperse particle system, simultaneously considering the effect of voidage and solid component fraction. The drag coefficients are given as below [21]:
Table 1 Governing equations used in the multi-fluid model. 1. Continuity equations v v εg rg þ εg rg ugi ¼ Sg vt vxi v v ðεm rm Þ þ ðεm rm umi Þ ¼ Sm vt vxi 2. Momentum conservation equations # " vPg vtgij v v εg rg ugi þ εg rg ugj ugi ¼ εg þ þ εg rg gi bgm ugi umi þ Sg ugi vt vxj vxi vxj # " vPg vtmij v v ðεm rm umi Þ þ εm rm umj umi ¼ εm þ þ εm rm gi þ bgm ugi umi vt vxj vxi vxj X þ blm ðuli umi Þ þ Sm umi
(T1-1) (T1-2)
(T1-3) (T1-4)
lsm
3. Energy conservation equations ! vqgi PM vTg vTg þ ugj þ m¼1 ggm Tm Tg DHg ¼ εg rg Cpg vt vxj vxi ! vTm vTm vq þ umj εm rm Cpm ¼ mi ggm Tm Tg DHm vt vxj vxi 4. Species transportation equations vXgn v v v εg rg Xgn þ εg rg Xgn ugi ¼ Dgn þ Sg;n vt vxi vxi vxi v v v vXmn ðεm rm Xmn Þ þ ðεm rm Xmn umi Þ ¼ Dmn þ Sm;n vt vxi vxi vxi 5. Granular temperature transportation equation " # ! Q 3 vQm vQm v vQ vU εm rm þ umj km m þ tmij mi þ m Jm ¼ 2 vxj vt vxj vxj vxj
(T1-5) (T1-6)
(T1-7) (T1-8)
(T1-9)
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b1 ¼
ε2g
"
Uslip
# 3 ð1 db Þεes1 2 C rg U se1 þ db rp1 rg εes1 ðg þ ab Þ 4 de1 dp1
Table 3 Ultimate and proximate analysis of biomass.
(4)
b2 ¼
ε2g
"
Uslip
# 3 ð1 db Þεes2 2 C rg U se2 þ db rp2 rg εes2 ðg þ ab Þ 4 de2 dp2 (5)
It is assumed that there is no particle in the bubble. From the above expressions, it can be seen that local structural parameters are required for the solution. Therefore, a series of equations relating to these parameters are required, as shown in Table 2. In addition, one stability criterion of the minimum suspending energy consumption is introduced as displayed in eq (T2-5). The SyamlalO’Brien symmetric model is adopted to calculate the solid-solid drag coefficient, given as follows [24]:
3
3ð1 þ elm Þ p 2 þ Clm 2 ðdl þ dm Þ2 εl rl εm rm g0;lm jvl vm j blm ¼ 2p rm d3m þ rl d3l
p2
(6)
Ultimate analysis
wt%
Proximate analysis
wt%
C H O N
51.3 6.0 38.3 0.3
Ash Volatiles Fixed carbon Moisture
0.4 81.0 14.4 4.2
assumed to directly convert to other light gas species. A single-step first-order Arrhenius reaction is adopted to describe the devolatilization rate and written as below [26]:
1340 Cbiomass r ¼ 1:49 105 exp Tp
(7)
2. Char gasification: The fixed carbon released from biomass pyrolysis reacts with CO2 and H2O to produce combustible gases. The main reactions are expressed as below:
C þ CO2 / 2CO
(R2)
2.3. Reaction kinetic model
C þ H2 O/H2 þ CO
(R3)
In this work, biomass and hematite are chosen as the solid fuel and oxygen carrier. The proximate and ultimate analysis of biomass is listed in Table 3 [25]. The main chemical reactions in a biomassfuelled CLC process include biomass pyrolysis, gasification and metal oxide reduction reactions, described as below:
Following the Everson et al. [27], the corresponding reaction rate takes the following form:
mchar ¼ rchar εchar
S0 r ð1 Xchar Þ2=3 1 ε0 i
(8)
where ri denotes the gasification rate, expressed by:
1. Biomass pyrolysis: In this simulation, the biomass is assumed to include four solid species (volatile, fixed carbon, moisture and ash). When the biomass enters the reactor, pyrolysis compositions are released as below:
Biomass / aCO þ bH2 þ cCH4 þ dCO2 þ eH2 O þ fChar þ gAsh
rCO2 ¼
kCO2 KCO2 PCO2 1 þ KCO2 PCO2 þ KCO PCO
(9)
rH2O ¼
kH2O KH2O PH2O 1 þ KH2O PH2O þ KH2 PH2
(10)
The detailed kinetic parameters in the model can be found in Su et al. [28].
(R1) 3. Water-gas-shift reaction: where the coefficients (a-g) are determined from the proximate and ultimate analysis of biomass. Although the tar species are generated in the pyrolysis process, the tar content is hindered under the condition of high temperature [25]. Here, the tar is
The water-gas-shift reaction is a reversible reaction. According to the study of Bustamante et al. [29], the reaction formula and the related rate are expressed as:
Table 2 Balance equations and correlations in the bubble-based bi-disperse drag model. 1. Force balance equations in the emulsion phase 3 εes1 3 εes2 C r U2 þ C r U 2 ¼ ðrp2 rg Þεes2 ðg þ ae Þ þ ðrp1 rg Þεes1 ðg þ ae Þ 4 de1 dp1 g se1 4 de2 dp2 g se2 2. Force balance equations for bubble phase 3 1 C r U 2 ¼ ðre rg Þðg þ ab Þ 4 db db e sb 3. Mass conversion of gas and particles in the grid Ug ¼ Uge ð1 db Þ þ db Ub ; Up1 ¼ Upe1 ð1 db Þ; Up2 ¼ Upe2 ð1 db Þ 4. Definition of overall gas and solid volume fraction εg ¼ ð1 db Þεe þ db ; εs1 ¼ ð1 db Þεes1 ; εs2 ¼ ð1 db Þεes2 5. Stability criterion of the minimum suspending energy consumption ! ! 1 3 ε 3 ε Cde1 es1 rg U 2se1 þ Cde2 es2 rg U 2se2 *Uge þ fb Ug ðg þ ab /min Ns ¼ ðre rg Þ 4 4 dp1 dp2
(T2-1)
(T2-2)
(T2-3) (T2-4) (T2-5)
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CO þ H2 O4H2 þCO2
(R4)
0:5 r ¼ k0 eE=RT CH2 CCO2
1 eE=RT CH2O CCO expð 4:33 þ 4577:8=TÞ
kmol m3 s (11)
4. Metal oxide reduction: Here, the hematite is adopted as oxygen carriers. As pointed out by Mahalatkar et al. [30], only the reduction to magnetite needs consideration in the chemistry scheme at a large excess of hematite. The main redox reactions are considered as below:
12Fe2 O3 CH4 / 8Fe3 O4 þ CO2 þ2H2 O
(R5)
3Fe2 O3 þ CO/2Fe3 O4 þ CO2
(R6)
3Fe2 O3 þH2 / 2Fe3 O4 þ H2 O
(R7)
Owing to the fast diffusion, the reaction rate is assumed to be solely controlled by chemical kinetics. The corresponding reaction rates from Mahalatkar et al. [30] are adopted and written as:
mCH4 ¼
kCH4 R0 12MFe2 O3 rs εs YFe2 O3 þ YFe3 O4 2MO2 8MFe3 O4 ð1 XÞ
mCO ¼
YCH4 M YCH4 ;TGA CH4
(12)
2 3MFe2 O3 kCO R0 ð1 XÞ3 MCO rs εs YFe2 O3 þ YFe3 O4 2MO2 2MFe3 O4 (13)
mH 2 ¼
2 3MFe2 O3 kH2 R0 ð1 XÞ3 MH2 rs εs YFe2 O3 þ YFe3 O4 2MO2 2MFe3 O4
(14)
where X and R0 represent the conversion degree and carrying capacity of oxygen carriers. The kinetic parameter kgi can be calculated by Ref. [30]:
kgi ¼
3bk0i eEi =RT n Cgi rm r0
(15)
where the detailed parameters are listed in Table 4.
2.4. System description and model implementation Based on the experimental setup of Mendiara et al. [25], a threedimensional simulation of a biomass-fuelled CLC process is carried out. The reactor has an inner diameter of 0.05m and a bed height of 0.2m, as shown in Fig. 1. The oxygen carrier has a mean density of
Table 4 Kinetic parameters for gas compositions. Parameters
H2
CH4
CO
k0 (mol1n m3n2 s1) E (KJ mol1) b n
2.3 103 24 3 0.8
8 104 49 12 1.3
6.2 104 20 3 1
Fig. 1. Schematic diagram of a fluidized bed fuel reactor.
4462 kg/m3 and an averaged diameter of 0.2 mm (doc ¼ 0.2 mm), belonging to Geldart-B particle. The pine sawdust is chosen as the biomass fuel with the mean diameter of 0.75 mm and the density of 800 kg/m3. The steam enters the bottom of the reactor at the velocity of 0.1 m/s. The biomass is continuously fed into the reactor at the feed rate of 95 g/h and the height of the biomass feed port is 0.02 m. The pressure outlet is specified at the top of the reactor with an atmospheric pressure. The no-slip wall condition is set for both gas and solid phases with a constant temperature. The detailed operating parameters, physical properties and boundary conditions are summarized in Table 5. The model is implemented on the platform of the MFIX CFD code [22]. In order to realize the non-rectangle geometry simulation, the cut-cell technology is adopted. The total variation diminishing (TVD) scheme and the implicit Eularian scheme are chosen for the spatial discretization and the temporal discretization. The time-step adjustment is done by monitoring the total iteration number in each time step. The favorable reduction of the iteration number will lead to the increase of time step whereas the poor convergence will cause the decrease of time step. Before the simulation, a grid-independence analysis is carried out with different grids. By comparing the axial distribution of gas pressure, the discrepancy of the simulated results using medium grids (25doc 25doc 30doc) and fine grids (16doc 16docp 20doc) is not evident, as shown in Fig. 2. The corresponding total grid number is 10000 and 33750, costing about 5 days and 8 days, respectively. With consideration of computational cost, the medium grid is selected as the final computational size. The simulation lasts for 20s with an auto-adjustable time step of 106-104s. The time-averaged variables are calculated from 10s to 20s after reaching the quasi-steady state.
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Table 5 Main physical parameters and operating conditions. Description
Simulation
Unit
Reactor diameter Bed height Oxygen carrier density Oxygen carrier diameter Biomass density Biomass diameter Biomass stream inlet composition Biomass inlet Temperature Biomass feed rate Velocity at the bottom inlet Pressure at the outlet Initial temperature
0.05 0.2 4462 0.0002 800 0.00075 biomass (0.954), moisture (0.042), ash (0.004) 293 95 0.1 1.013 105 1188
m m kg/m3 m kg/m3 m e K g/h m/s Pa K
Fig. 2. Effect of grid size on axial profile of gas pressure.
3. Results and discussion
Fig. 3. Heterogeneous index with voidage at different solid component volume fractions.
In order to characterize the non-uniformity of drag coefficient, the heterogeneity index is defined as the ratio of the multi-scale drag coefficient to the homogenous drag coefficient [31]. Fig. 3(a) and (b) display the variations of the heterogeneity index with voidage and solid component fractions for oxygen carriers and biomass. It can be seen that the increase of the voidage will enlarge the heterogeneity index for the two kinds of particles, which means that the system tends to be more uniform. In addition, we can clearly notice that the heterogeneity index depends not only on the voidage but also on the solid component fraction in a binary mixture system. Fig. 3(a) shows that the heterogeneity index has a negative correlation with the volume fraction of oxygen carriers. In other words, the increase of oxygen carrier fraction will strengthen the heterogeneity of the system while the increase of biomass component fraction promotes the homogenous feature of the system, as shown in Fig. 3(b). This is mainly attributed to the fact that the large-size biomass particle weakens the inhomogeneous feature of the interphase drag [21]. In order to verify the ability of the model to predict the flow behaviors of binary mixture, the simulated and measured cumulative masses of fines out of the bed during the elutriation process are displayed in Fig. 4(a) [32]. We can observe that the bubblebased bi-disperse drag model can give a more reasonable prediction on experimental results owing to the consideration of mesoscale structures in contrast to the prediction by the Gidaspow
drag model [31]. Mendiara et al. [25] experimentally evaluated the biomass-fuelled CLC performance with Fe-based oxygen carriers. Here, a comparison study of simulated results and measure data is further carried out to validate the feasibility of the kinetic model, as shown in Fig. 4(b) and (c), where N2 is not included in the gas compositions. We can recognize that the model prediction can give a fair agreement with experimental results although there are still some discrepancies as a result of the uniform particle size. In fact, the particle size has a wide distribution. Moreover, the effect of the distributor and air plenum is neglected in this simulation. There are many compositions in oxygen carriers. Here, the Fe2O3 is only considered, leading to some discrepancies between experiment and simulation. The error bars in the simulation from statistical analysis are also added in the figure. Overall, the model prediction is in an acceptable range. As the biomass particles are fed into the reactor, they will be mixed with oxygen carriers. The instantaneous contour plots of volume fractions of both solid phases are shown in Fig. 5(a) and (b). From Fig. 5(a), it can also be observed that there are the formation of bubbles around the biomass feed port. This is because the biomass pyrolysis will produce a large amount of volatile gases. On the other hand, the inlet gas velocity of 0.1 m/s is too low to form the visible bubble at the bottom of the reactor. Meanwhile, the bubble motion can be noticed with its growth and coalescence.
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Fig. 4. Comparisons of simulated gas compositions and measured data.
Fig. 5. Instantaneous contour plots of volume fractions of oxygen carriers and biomass particles.
From Fig. 5(b), the biomass particles tends to float at the upper part of the owing to their low density. The transient contour distributions of gas compositions at 20s are displayed in Fig. 6. It can be seen that CO2 dominates the reactor owing to the fact that the combustible gases from biomass pyrolysis and gasification are consumed by oxygen carriers, which is a typical feature for the CLC process. We can also observe that there is a noticeably high gas concentration in the vicinity of the biomass feed port. This is because the biomass pyrolysis produces a large amount of volatile gas components. With the metal oxide reduction reaction in progress, these combustible gases are converted to CO2 and/or H2O. Hence, the mixing behavior of biomass and oxygen carriers will influence the CLC performance. When the biomass particles enter the fuel reactor, the pyrolysis process occurs and the volatile gases, fixed char and ash are generated. Fig. 6 also shows the instantaneous distribution of solid fuel species concentrations at 20s. It can be observed that the biomass particles mainly exist
around the biomass feed port, which can be explained by the fact that the biomass devolatilization rate is much higher so as to result in a quick decomposition of biomass. The fixed char can be seen in the whole bed owing to its low gasification rate. Because operating velocity is not high enough, a good mixing of binary mixture cannot be achieved, resulting in the non-uniformity of char distribution. Fig. 7 shows the instantaneous contour plots of reaction rates at 20s. It can be seen that the gasification reaction is dominant in the upper part of the bed. This can be explained by the fact that the biomass particles tend to float on the bed surface owning to their low density. In contrary, the reduction reactions of oxygen carriers are distributed in the whole bed with higher reaction rates near the biomass feed port, which is attributed to the syngas production by biomass pyrolysis near the biomass feed port. In addition, it can be noticed that there is a higher reduction reaction rate near the bubble owing to the contact between pyrolysis products and oxygen carriers, resulting in the non-uniform distribution of oxygen
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Fig. 6. Instantaneous contour plots of gas and solid species concentrations at 20s.
Fig. 7. Instantaneous contour plots of reaction rates at 20s.
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Fig. 8. Variation of the outlet gas compositions with time. Fig. 10. Effect of operating pressure on gas compositions.
carrier reduction reactions. Fig. 8 demonstrates the evolution of the outlet gas compositions with time. As the time proceeds, the gas products from biomass pyrolysis, gasification and combustion arrive at the exit of the reactor so that the concentrations are improved. When a relatively steady flow of oxygen carriers and biomass particles in the bed is reached, the variations of the corresponding gas compositions become insignificant. From the figure, we can recognize that the gas component concentrations fluctuate around a constant after 8s. The utilization of the recirculated CO2 as a gasifying agent can reduce the energy consumption by steam generation. Here, a comparison of CO2 and steam as gasifying agents is conducted, as shown in Fig. 9. We can find that CO has a slight rise with CO2 as a gasifying agent owing to the enhancement of the CeCO2 gasification whereas the H2 concentration is reduced. In addition, the predicted gas compositions using the recirculated CO2 of 50% and the steam of 50% are also shown in Fig. 9. We can realize that the change of the predicted gas species concentrations is not evident using the mixture of CO2 and steam compared to that using pure steam as a gasifying agent. Therefore, the recycled fuel reactor offgas (containing both CO2 and steam) provides a preferred option from an economic point of view. Fig. 10 shows the variation of the outlet gas compositions under
Fig. 9. Effect of gasifying agent on gas compositions.
different operating pressures. It can be noticed that when the operating pressure is changed from 0.1 MPa to 0.3 MPa, the CO2 concentration is increased by 5% whereas the CO concentration is reduced by 2.5%. This is attributed to the fact that a high pressure will reduce the bubble size and enhance the gas-solid contact, leading to a rising reaction degree. This also implies that the increase of operating pressure will facilitate the depletion of fuel gas released from biomass and improve the combustion efficiency. Meanwhile, we can realize that the influence becomes weak as the operating pressure further increases. The change of gas component concentrations is not evident. Thus, it is necessary for the selectivity of operating pressure to consider the tradeoff between the conversion efficiency and the capital cost. In order to examine the influence of biomass feed port locations, three different heights are selected, as shown in Fig. 11. It can be seen that as the feed port height from the reactor bottom increases from 0.02 m to 0.08 m, the CO2 molar fraction has a reduction of 6.4% while the CO concentration has an increase by 3.3% with the rising CH4 and H2 concentrations. From Fig. 5, we can find that the biomass tends to float at the upper section of bed. The gas products from biomass pyrolysis form the bubbles and pass through the bed. When the location of biomass inlet port is higher, the contact between the pyrolysis gas and oxygen carriers is not enough so that
Fig. 11. Effect of biomass feed port location on gas compositions.
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the pyrolysis gas flows into the freeboard, resulting in the reduction of CO2 concentration. Therefore, the in-bed fuel feed arrangement at a lower section of bed is beneficial to the CLC performance. 4. Conclusion A bubble-based bi-disperse drag model is implemented in the multi-fluid model to investigate the iG-CLC performance using biomass as fuel. The simulation result can give a good agreement with experimental data. The mixing behavior of biomass and oxygen carrier as well as the gas composition distribution during the iG-CLC process are well predicted. The impact of operating parameter is discussed. It is found that a rising pressure can increase the product CO2 concentration by 5%, whereas an excessively high operating pressure can not significantly influence the gas compositions. The reuse of CO2 is preferable as a gasification agent instead of H2O. Meanwhile, the effect of feed port location is examined. The results reveal that the arrangement of the fuel feed port at a low section of bed is beneficial for the CLC performance. In the future work, the regeneration and recirculation process of oxygen carriers will be taken into consideration to achieve a complete evaluation of the solid-fuelled CLC system. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research is conducted with financial support from the National Natural Science Foundation of China (51606053). Nomenclature a Cd d e E g J n P Re S0 u U X
acceleration [m s2] drag coefficient of a single particle particle diameter [m] restitution coefficient activation energy [KJ mol1] gravity [m s2] collisional energy dissipation [kg m1 s3] reaction order pressure [Pa] Reynolds number initial surface area[m2 m3] velocity [m s1] superficial velocity [m s1] conversion degree
Greek letters b drag coefficient [kg m3 s1] ε volume fraction q granular temperature [m2 s2] r density [kg m3] tQ stress tensor [Pa] kinetic energy production through slip between phase [kg m1 s3] Subscripts b e g p
bubble phase emulsion phase gas phase particle phase
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References [1] A. Nandy, C. Loha, S. Gu, P. Sarkar, M.K. Karmakar, P.K. Chatterjee, Present status and overview of chemical looping combustion technology, Renew. Sustain. Energy Rev. 59 (2016) 597e619. [2] J. Fan, H. Hong, H. Jin, Biomass and coal co-feed power and SNG polygeneration with chemical looping combustion to reduce carbon footprint for sustainable energy development: process simulation and thermodynamic assessment, Renew. Energy 125 (2018) 260e269. [3] J. Adanez, A. Abad, T. Mendiara, P. Gayan, L.F. de Diego, F. García-Labiano, Chemical looping combustion of solid fuels, Prog Energ Combust 65 (2018) 6e66. € ll, H. Hofbauer, Detailed fluid dynamic in[4] S. Penthor, M. Stollhof, T. Pro vestigations of a novel fuel reactor concept for chemical looping combustion of solid fuels, Powder Technol. 287 (2016) 61e69. [5] J. Ma, X. Tian, C. Wang, X. Chen, H. Zhao, Performance of a 50 kWth coalfuelled chemical looping combustor, Int J Greenh Gas Con 75 (2018) 98e106. [6] M. Zheng, L. Shen, X. Feng, In situ gasification chemical looping combustion of a coal using the binary oxygen carrier natural anhydrite ore and natural iron ore, Energy Convers. Manag. 83 (2014) 270e283. [7] C. Linderholm, M. Schmitz, M. Biermann, M. Hanning, A. Lyngfelt, Chemicallooping combustion of solid fuel in a 100kW unit using sintered manganese ore as oxygen carrier, Int J Greenh Gas Con 65 (2017) 170e181. [8] T. Schulzke, J. Westermeyer, H. Giani, C. Hornsby, Combustion of refined renewable biomass fuel (RRBF) in a bubbling fluidized bed, Renew. Energy 124 (2018) 84e94. [9] L. Stougie, G.A. Tsalidis, H.J. van der Kooi, G. Korevaar, Environmental and exergetic sustainability assessment of power generation from biomass, Renew. Energy 128 (2018) 520e528. rez-Astray, M.T. Izquierdo, A. Abad, L.F. de Diego, F. García[10] T. Mendiara, A. Pe n, J. Ada nez, Chemical looping combustion of different types Labiano, P. Gaya of biomass in a 0.5 kWth unit, Fuel 211 (2018) 868e875. [11] Z. Shen, Z. Huang, High-efficiency and pollution-controlling in-situ gasification chemical looping combustion system by using CO2 instead of steam as gasification agent, Chin. J. Chem. Eng. 26 (2018) 2368e2376. [12] M. Luo, S. Wang, L. Wang, M. Lv, L. Qian, H. Fu, Experimental investigation of co-combustion of coal and biomass using chemical looping technology, Fuel Process. Technol. 110 (2013) 258e267. [13] Y. Shao, Y. Zhang, X. Wang, X. Wang, B. Jin, H. Liu, Three-dimensional full loop modeling and optimization of an in situ gasification chemical looping combustion system, Energy Fuel. 31 (12) (2017) 13859e13870. [14] S. Wang, W. Yin, Z. Li, X. Yang, K. Zhang, Numerical investigation of chemical looping gasification process using solid fuels for syngas production, Energy Convers. Manag. 173 (2018) 296e302. [15] Z. Peng, E. Doroodchi, Y. Alghamdi, B. Moghtaderi, Mixing and segregation of solid mixtures in bubbling fluidized beds under conditions pertinent to the fuel reactor of a chemical looping system, Powder Technol. 235 (2013) 823e837. € hle, B. Epple, Extended Euler-Euler model for [16] F. Alobaid, P. Ohlemüller, J. Stro the simulation of a 1 MWth chemical-looping pilot plant, Energy 93 (2015) 2395e2405. [17] Y. Guan, J. Chang, K. Zhang, B. Wang, Q. Sun, D. Wen, Three-dimensional full loop simulation of solids circulation in an interconnected fluidized bed, Powder Technol. 289 (2016) 118e125. [18] X. Gao, T. Li, A. Sarkar, L. Lu, W.A. Rogers, Development and validation of an enhanced filtered drag model for simulating gas-solid fluidization of Geldart A particles in all flow regimes, Chem. Eng. Sci. 184 (2018) 33e51. [19] Z. Shi, W. Wang, J. Li, A bubble-based EMMS model for gas-solid bubbling fluidization, Chem. Eng. Sci. 66 (22) (2011) 5541e5555. [20] S. Wang, X. Yang, S. Xu, K. Zhang, B. Li, Evaluation of sorption-enhanced reforming of biodiesel by-product in fluidized beds by means of CFD approach, Fuel 214 (2018) 115e122. [21] S. Wang, K. Zhang, S. Xu, X. Yang, Assessment of a bubble-based bi-disperse drag model for the simulation of a bubbling fluidized bed with a binary mixture, Powder Technol. 338 (2018) 280e288. [22] M. Syamlal, M.F.I.X. Documentation, Numerical Techniques, National Technical Information Service, Springfield, VA, 1998. DOE/MC-31346-5824. NTIS/ DE98002029. [23] A. Srivastava, S. Sundaresan, Analysis of a frictional-kinetic model for gasparticle flow, Powder Technol. 129 (2003) 72e85. [24] M. Syamlal, The Particle-Particle Drag Term in a Multiparticle Model of Fluidization, National Technical Information Service, 1987. nez, [25] T. Mendiara, A. Abad, L.F. de Diego, F. García-Labiano, P. Gay an, J. Ada Biomass combustion in a CLC system using an iron ore as an oxygen carrier, Int J Greenh Gas Con 19 (2013) 322e330. [26] H. Liu, R.J. Cattolica, R. Seiser, C. Liao, Three-dimensional full-loop simulation of a dual fluidized-bed biomass gasifier, Appl. Energy 160 (2015) 489e501. [27] R.C. Everson, H.W.J.P. Neomagus, H. Kasaini, D. Njapha, Reaction kinetics of pulverized coal-chars derived from inertinite-rich coal discards: gasification with carbon dioxide and steam, Fuel 85 (7e8) (2006) 1076e1082. [28] M. Su, H. Zhao, J. Ma, Computational fluid dynamics simulation for chemical looping combustion of coal in a dual circulation fluidized bed, Energy Convers. Manag. 105 (2015) 1e12. [29] F. Bustamante, R. Enick, A. Cugini, R. Killmeyer, B. Howard, K. Rothenberger,
Please cite this article as: W. Yin et al., Numerical investigation of in situ gasification chemical looping combustion of biomass in a fluidized bed reactor, Renewable Energy, https://doi.org/10.1016/j.renene.2019.11.016
10
W. Yin et al. / Renewable Energy xxx (xxxx) xxx
High-temperature kinetics of the homogeneous reverse wateregas shift reaction, AIChE J. 50 (2004) 1028e1041. [30] K. Mahalatkar, J. Kuhlman, E.D. Huckaby, T. O’Brien, CFD simulation of a chemical-looping fuel reactor utilizing solid fuel, Chem. Eng. Sci. 66 (16) (2011) 3617e3627.
[31] D. Gidaspow, Multiphase Flow and Fluidization: Continuum and Kinetic Theory Descriptions, Academic Press Inc., Boston, 1994. [32] E.R. Monazam, R.W. Breault, J. Weber, K. Layfield, Elutriation of fines from binary particle mixtures in bubbling fluidized bed cold model, Powder Technol. 305 (2017) 340e346.
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Numerical investigation of in situ gasification chemical looping combustion of biomass in a fluidized bed reactor Weijie Yin, Shuai Wang*, Kai Zhang, Yurong He School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 January 2019 Received in revised form 2 November 2019 Accepted 4 November 2019 Available online xxx
In-situ gasification chemical looping combustion (iG-CLC) depends on the solid fuel gasification rate and the contact between gasification products and oxygen carriers. In this work, a three-dimensional simulation is carried out to investigate the iG-CLC performance of biomass in a fluidized bed reactor by means of the multi-fluid model with the bubble-based bi-disperse drag model to consider the bubble effect on the interphase drag force. The model can give a reasonable prediction on the experimental results. The result reveals that an increase of operating pressure of 0.2 MPa can promote the CO2 yield by 5%, whereas the change of the feed port height from the reactor bottom also greatly influences the outlet gas compositions. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Chemical looping combustion Fluidized bed Computational fluid dynamics Biomass
1. Introduction Chemical Looping Combustion (CLC) is a promising combustion technology owing to its inherent CO2 separation with zero energy penalty via oxygen carriers as chemical intermediates to achieve the oxygen transportation [1,2]. In contrast to gaseous fuels, solid fuels have been successfully applied in a chemical looping system owing to their advantages of abundance and environmental friendliness [3e5]. Zheng et al. [6] conducted an experimental study of the coal-fuelled CLC process using CaSO4eFe2O3 as oxygen carriers. It was pointed out that the integration of CaSO4 and Fe2O3 could facilitate the fuel conversion and improve the combustion efficiency with a low sulfur emission. Linderholm et al. [7] found that the manganese ore as an oxygen carrier showed a high reactivity with sufficient lift time. Among various solid fuels, biomass as one kind of renewable resources has attracted a great deal of concerns [8,9]. Mendiara et al. [10] compared different types of biomass in the CLC process under the iG-CLC mode. The result revealed that the increase of reaction temperature could achieve the almost 100% CO2 capture efficiency. The effect of CO2 and steam as gasification agents on the carbon conversion rate and CO2 capture efficiency was examined via a thermodynamic assessment. The CO2 was found to be a
* Corresponding author. E-mail address: [email protected] (S. Wang).
promising gasification agent [11]. The co-combustion of coal and biomass in a chemical looping system was experimentally investigated. The result demonstrated that the addition of biomass could reduce the gasification temperature and improve the coal conversion [12]. Computational fluid dynamics (CFD) can provide some detailed information on the gas-solid flow and reaction performance in a fluidized bed reactor, which has been applied in the investigation of a chemical looping system [13,14]. Peng et al. [15] employed the CFD-DEM to evaluate the mixing and segregation behaviors of solid mixture in a fuel reactor. It was found that the decrease of particle size ratio in a binary mixture could promote the mixing degree. The Euler-Euler model was also employed to evaluate the coal-fuelled CLC process by considering the thermochemical reaction mechanism. It was emphasized that the recirculation mass flow of oxygen carriers was very important in the simulation of fuel reactor [16]. A three-dimensional full loop simulation of the CLC system was implemented and the effect of operating parameters on solid circulation rate was analyzed. It was concluded that the increase of gas velocity could weaken the sensitivity of solid circulation rate to operating parameters [17]. The interphase drag model plays an important role in the prediction of the multiphase flow process via CFD approaches. For a large-scale CLC system, a coarse-grid simulation is necessary owing to the limitation of computational efficiency. When coarse grids are employed, the mesoscale structures including clusters and bubbles will result in the non-uniformity of gas-solid flow within the grid,
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which restricts the prediction accuracy using traditional drag models [18]. In recent years, a bubble-based energy-minimization multi-scale (EMMS) drag model has been proposed, resolving the drag into the three components from the emulsion phase, the bubble phase and the interphase [19,20]. By comparing the bed height and the voidage, it was found that the model prediction could give a better agreement with experimental data. In order to better describe the bubble effect in a poly-disperse system, a bidisperse drag model was developed simultaneously considering the effects of the voidage and solid component fraction on the basis of the bubble-based EMMS model [21]. The result demonstrated that the drag heterogeneity in a poly-disperse system greatly depended on solid component fractions. Whereas in a polydisperse reacting system like a solid-fuelled CLC system, few reports about the consideration of the bubble effect can be available. The present work aims to investigate the iG-CLC performance with biomass via CFD approaches incorporating a bubble-based bidisperse drag model to describe the bubble effect in a poly-disperse reacting system. The model prediction can give a good agreement with experimental data. Meanwhile, the interaction mechanism of oxygen carrier and biomass is further analyzed. The impacts of CO2 as a gasifying agent and in-bed fuel feed arrangement are evaluated.
particles is adopted as the model closure [22]. For a high particle concentration, the frictional effect between particles is significant. Here, a modified Savage model is adopted [23]:
0
1n1
pf V,us B C rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ @1 . ffiA pffiffiffi pc n 2sinðfÞ S : S þ q d2s
(1)
8 1024 ε* ε 10 εg < ε* g > > > > > 2 > > < 1 εg εmin s pc ¼ 0:05 ε* εg < 1 εmin 5 sf > * > εg ε > > > > > : 0 εg > 1 εmin sf
(2)
9 10 11=ðn1Þ > 0 8 pffiffiffi > > > = ε 2ps;f sin j < CBps;f C B s ffi n 1 mf ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n @ A A @ . > M > p P c 2: > > S : S þ q ds εm ;
(3)
m¼1
2. Mathematical model
The gas-solid interphase drag model plays a crucial role in the simulation of a fluidized bed, which will be discussed in the next section.
2.1. Multi-fluid model In this work, the multi-fluid model is employed to characterize flow behaviors of oxygen carrier and biomass particles in a CLC system. Here, the assumption is made that each solid phase has a uniform size. The governing equations mainly comprise continuity equations, momentum and energy conservation equations as well as species transportation equations. The detailed descriptions are summarized in Table 1. Granular temperature is introduced to describe the fluctuating energy of particles and the transportation equation is shown in eq (T1-9). The kinetic theory of polydisperse
2.2. A bubble-based bi-disperse drag model Bubbles as mesoscale structures in a bubbling fluidized bed influence the gas-solid interaction. Here, a bubble-based bidisperse drag model is employed in a bi-disperse particle system, simultaneously considering the effect of voidage and solid component fraction. The drag coefficients are given as below [21]:
Table 1 Governing equations used in the multi-fluid model. 1. Continuity equations v v εg rg þ εg rg ugi ¼ Sg vt vxi v v ðεm rm Þ þ ðεm rm umi Þ ¼ Sm vt vxi 2. Momentum conservation equations # " vPg vtgij v v εg rg ugi þ εg rg ugj ugi ¼ εg þ þ εg rg gi bgm ugi umi þ Sg ugi vt vxj vxi vxj # " vPg vtmij v v ðεm rm umi Þ þ εm rm umj umi ¼ εm þ þ εm rm gi þ bgm ugi umi vt vxj vxi vxj X þ blm ðuli umi Þ þ Sm umi
(T1-1) (T1-2)
(T1-3) (T1-4)
lsm
3. Energy conservation equations ! vqgi PM vTg vTg þ ugj þ m¼1 ggm Tm Tg DHg ¼ εg rg Cpg vt vxj vxi ! vTm vTm vq þ umj εm rm Cpm ¼ mi ggm Tm Tg DHm vt vxj vxi 4. Species transportation equations vXgn v v v εg rg Xgn þ εg rg Xgn ugi ¼ Dgn þ Sg;n vt vxi vxi vxi v v v vXmn ðεm rm Xmn Þ þ ðεm rm Xmn umi Þ ¼ Dmn þ Sm;n vt vxi vxi vxi 5. Granular temperature transportation equation " # ! Q 3 vQm vQm v vQ vU εm rm þ umj km m þ tmij mi þ m Jm ¼ 2 vxj vt vxj vxj vxj
(T1-5) (T1-6)
(T1-7) (T1-8)
(T1-9)
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b1 ¼
ε2g
"
Uslip
# 3 ð1 db Þεes1 2 C rg U se1 þ db rp1 rg εes1 ðg þ ab Þ 4 de1 dp1
Table 3 Ultimate and proximate analysis of biomass.
(4)
b2 ¼
ε2g
"
Uslip
# 3 ð1 db Þεes2 2 C rg U se2 þ db rp2 rg εes2 ðg þ ab Þ 4 de2 dp2 (5)
It is assumed that there is no particle in the bubble. From the above expressions, it can be seen that local structural parameters are required for the solution. Therefore, a series of equations relating to these parameters are required, as shown in Table 2. In addition, one stability criterion of the minimum suspending energy consumption is introduced as displayed in eq (T2-5). The SyamlalO’Brien symmetric model is adopted to calculate the solid-solid drag coefficient, given as follows [24]:
3
3ð1 þ elm Þ p 2 þ Clm 2 ðdl þ dm Þ2 εl rl εm rm g0;lm jvl vm j blm ¼ 2p rm d3m þ rl d3l
p2
(6)
Ultimate analysis
wt%
Proximate analysis
wt%
C H O N
51.3 6.0 38.3 0.3
Ash Volatiles Fixed carbon Moisture
0.4 81.0 14.4 4.2
assumed to directly convert to other light gas species. A single-step first-order Arrhenius reaction is adopted to describe the devolatilization rate and written as below [26]:
1340 Cbiomass r ¼ 1:49 105 exp Tp
(7)
2. Char gasification: The fixed carbon released from biomass pyrolysis reacts with CO2 and H2O to produce combustible gases. The main reactions are expressed as below:
C þ CO2 / 2CO
(R2)
2.3. Reaction kinetic model
C þ H2 O/H2 þ CO
(R3)
In this work, biomass and hematite are chosen as the solid fuel and oxygen carrier. The proximate and ultimate analysis of biomass is listed in Table 3 [25]. The main chemical reactions in a biomassfuelled CLC process include biomass pyrolysis, gasification and metal oxide reduction reactions, described as below:
Following the Everson et al. [27], the corresponding reaction rate takes the following form:
mchar ¼ rchar εchar
S0 r ð1 Xchar Þ2=3 1 ε0 i
(8)
where ri denotes the gasification rate, expressed by:
1. Biomass pyrolysis: In this simulation, the biomass is assumed to include four solid species (volatile, fixed carbon, moisture and ash). When the biomass enters the reactor, pyrolysis compositions are released as below:
Biomass / aCO þ bH2 þ cCH4 þ dCO2 þ eH2 O þ fChar þ gAsh
rCO2 ¼
kCO2 KCO2 PCO2 1 þ KCO2 PCO2 þ KCO PCO
(9)
rH2O ¼
kH2O KH2O PH2O 1 þ KH2O PH2O þ KH2 PH2
(10)
The detailed kinetic parameters in the model can be found in Su et al. [28].
(R1) 3. Water-gas-shift reaction: where the coefficients (a-g) are determined from the proximate and ultimate analysis of biomass. Although the tar species are generated in the pyrolysis process, the tar content is hindered under the condition of high temperature [25]. Here, the tar is
The water-gas-shift reaction is a reversible reaction. According to the study of Bustamante et al. [29], the reaction formula and the related rate are expressed as:
Table 2 Balance equations and correlations in the bubble-based bi-disperse drag model. 1. Force balance equations in the emulsion phase 3 εes1 3 εes2 C r U2 þ C r U 2 ¼ ðrp2 rg Þεes2 ðg þ ae Þ þ ðrp1 rg Þεes1 ðg þ ae Þ 4 de1 dp1 g se1 4 de2 dp2 g se2 2. Force balance equations for bubble phase 3 1 C r U 2 ¼ ðre rg Þðg þ ab Þ 4 db db e sb 3. Mass conversion of gas and particles in the grid Ug ¼ Uge ð1 db Þ þ db Ub ; Up1 ¼ Upe1 ð1 db Þ; Up2 ¼ Upe2 ð1 db Þ 4. Definition of overall gas and solid volume fraction εg ¼ ð1 db Þεe þ db ; εs1 ¼ ð1 db Þεes1 ; εs2 ¼ ð1 db Þεes2 5. Stability criterion of the minimum suspending energy consumption ! ! 1 3 ε 3 ε Cde1 es1 rg U 2se1 þ Cde2 es2 rg U 2se2 *Uge þ fb Ug ðg þ ab /min Ns ¼ ðre rg Þ 4 4 dp1 dp2
(T2-1)
(T2-2)
(T2-3) (T2-4) (T2-5)
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CO þ H2 O4H2 þCO2
(R4)
0:5 r ¼ k0 eE=RT CH2 CCO2
1 eE=RT CH2O CCO expð 4:33 þ 4577:8=TÞ
kmol m3 s (11)
4. Metal oxide reduction: Here, the hematite is adopted as oxygen carriers. As pointed out by Mahalatkar et al. [30], only the reduction to magnetite needs consideration in the chemistry scheme at a large excess of hematite. The main redox reactions are considered as below:
12Fe2 O3 CH4 / 8Fe3 O4 þ CO2 þ2H2 O
(R5)
3Fe2 O3 þ CO/2Fe3 O4 þ CO2
(R6)
3Fe2 O3 þH2 / 2Fe3 O4 þ H2 O
(R7)
Owing to the fast diffusion, the reaction rate is assumed to be solely controlled by chemical kinetics. The corresponding reaction rates from Mahalatkar et al. [30] are adopted and written as:
mCH4 ¼
kCH4 R0 12MFe2 O3 rs εs YFe2 O3 þ YFe3 O4 2MO2 8MFe3 O4 ð1 XÞ
mCO ¼
YCH4 M YCH4 ;TGA CH4
(12)
2 3MFe2 O3 kCO R0 ð1 XÞ3 MCO rs εs YFe2 O3 þ YFe3 O4 2MO2 2MFe3 O4 (13)
mH 2 ¼
2 3MFe2 O3 kH2 R0 ð1 XÞ3 MH2 rs εs YFe2 O3 þ YFe3 O4 2MO2 2MFe3 O4
(14)
where X and R0 represent the conversion degree and carrying capacity of oxygen carriers. The kinetic parameter kgi can be calculated by Ref. [30]:
kgi ¼
3bk0i eEi =RT n Cgi rm r0
(15)
where the detailed parameters are listed in Table 4.
2.4. System description and model implementation Based on the experimental setup of Mendiara et al. [25], a threedimensional simulation of a biomass-fuelled CLC process is carried out. The reactor has an inner diameter of 0.05m and a bed height of 0.2m, as shown in Fig. 1. The oxygen carrier has a mean density of
Table 4 Kinetic parameters for gas compositions. Parameters
H2
CH4
CO
k0 (mol1n m3n2 s1) E (KJ mol1) b n
2.3 103 24 3 0.8
8 104 49 12 1.3
6.2 104 20 3 1
Fig. 1. Schematic diagram of a fluidized bed fuel reactor.
4462 kg/m3 and an averaged diameter of 0.2 mm (doc ¼ 0.2 mm), belonging to Geldart-B particle. The pine sawdust is chosen as the biomass fuel with the mean diameter of 0.75 mm and the density of 800 kg/m3. The steam enters the bottom of the reactor at the velocity of 0.1 m/s. The biomass is continuously fed into the reactor at the feed rate of 95 g/h and the height of the biomass feed port is 0.02 m. The pressure outlet is specified at the top of the reactor with an atmospheric pressure. The no-slip wall condition is set for both gas and solid phases with a constant temperature. The detailed operating parameters, physical properties and boundary conditions are summarized in Table 5. The model is implemented on the platform of the MFIX CFD code [22]. In order to realize the non-rectangle geometry simulation, the cut-cell technology is adopted. The total variation diminishing (TVD) scheme and the implicit Eularian scheme are chosen for the spatial discretization and the temporal discretization. The time-step adjustment is done by monitoring the total iteration number in each time step. The favorable reduction of the iteration number will lead to the increase of time step whereas the poor convergence will cause the decrease of time step. Before the simulation, a grid-independence analysis is carried out with different grids. By comparing the axial distribution of gas pressure, the discrepancy of the simulated results using medium grids (25doc 25doc 30doc) and fine grids (16doc 16docp 20doc) is not evident, as shown in Fig. 2. The corresponding total grid number is 10000 and 33750, costing about 5 days and 8 days, respectively. With consideration of computational cost, the medium grid is selected as the final computational size. The simulation lasts for 20s with an auto-adjustable time step of 106-104s. The time-averaged variables are calculated from 10s to 20s after reaching the quasi-steady state.
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Table 5 Main physical parameters and operating conditions. Description
Simulation
Unit
Reactor diameter Bed height Oxygen carrier density Oxygen carrier diameter Biomass density Biomass diameter Biomass stream inlet composition Biomass inlet Temperature Biomass feed rate Velocity at the bottom inlet Pressure at the outlet Initial temperature
0.05 0.2 4462 0.0002 800 0.00075 biomass (0.954), moisture (0.042), ash (0.004) 293 95 0.1 1.013 105 1188
m m kg/m3 m kg/m3 m e K g/h m/s Pa K
Fig. 2. Effect of grid size on axial profile of gas pressure.
3. Results and discussion
Fig. 3. Heterogeneous index with voidage at different solid component volume fractions.
In order to characterize the non-uniformity of drag coefficient, the heterogeneity index is defined as the ratio of the multi-scale drag coefficient to the homogenous drag coefficient [31]. Fig. 3(a) and (b) display the variations of the heterogeneity index with voidage and solid component fractions for oxygen carriers and biomass. It can be seen that the increase of the voidage will enlarge the heterogeneity index for the two kinds of particles, which means that the system tends to be more uniform. In addition, we can clearly notice that the heterogeneity index depends not only on the voidage but also on the solid component fraction in a binary mixture system. Fig. 3(a) shows that the heterogeneity index has a negative correlation with the volume fraction of oxygen carriers. In other words, the increase of oxygen carrier fraction will strengthen the heterogeneity of the system while the increase of biomass component fraction promotes the homogenous feature of the system, as shown in Fig. 3(b). This is mainly attributed to the fact that the large-size biomass particle weakens the inhomogeneous feature of the interphase drag [21]. In order to verify the ability of the model to predict the flow behaviors of binary mixture, the simulated and measured cumulative masses of fines out of the bed during the elutriation process are displayed in Fig. 4(a) [32]. We can observe that the bubblebased bi-disperse drag model can give a more reasonable prediction on experimental results owing to the consideration of mesoscale structures in contrast to the prediction by the Gidaspow
drag model [31]. Mendiara et al. [25] experimentally evaluated the biomass-fuelled CLC performance with Fe-based oxygen carriers. Here, a comparison study of simulated results and measure data is further carried out to validate the feasibility of the kinetic model, as shown in Fig. 4(b) and (c), where N2 is not included in the gas compositions. We can recognize that the model prediction can give a fair agreement with experimental results although there are still some discrepancies as a result of the uniform particle size. In fact, the particle size has a wide distribution. Moreover, the effect of the distributor and air plenum is neglected in this simulation. There are many compositions in oxygen carriers. Here, the Fe2O3 is only considered, leading to some discrepancies between experiment and simulation. The error bars in the simulation from statistical analysis are also added in the figure. Overall, the model prediction is in an acceptable range. As the biomass particles are fed into the reactor, they will be mixed with oxygen carriers. The instantaneous contour plots of volume fractions of both solid phases are shown in Fig. 5(a) and (b). From Fig. 5(a), it can also be observed that there are the formation of bubbles around the biomass feed port. This is because the biomass pyrolysis will produce a large amount of volatile gases. On the other hand, the inlet gas velocity of 0.1 m/s is too low to form the visible bubble at the bottom of the reactor. Meanwhile, the bubble motion can be noticed with its growth and coalescence.
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Fig. 4. Comparisons of simulated gas compositions and measured data.
Fig. 5. Instantaneous contour plots of volume fractions of oxygen carriers and biomass particles.
From Fig. 5(b), the biomass particles tends to float at the upper part of the owing to their low density. The transient contour distributions of gas compositions at 20s are displayed in Fig. 6. It can be seen that CO2 dominates the reactor owing to the fact that the combustible gases from biomass pyrolysis and gasification are consumed by oxygen carriers, which is a typical feature for the CLC process. We can also observe that there is a noticeably high gas concentration in the vicinity of the biomass feed port. This is because the biomass pyrolysis produces a large amount of volatile gas components. With the metal oxide reduction reaction in progress, these combustible gases are converted to CO2 and/or H2O. Hence, the mixing behavior of biomass and oxygen carriers will influence the CLC performance. When the biomass particles enter the fuel reactor, the pyrolysis process occurs and the volatile gases, fixed char and ash are generated. Fig. 6 also shows the instantaneous distribution of solid fuel species concentrations at 20s. It can be observed that the biomass particles mainly exist
around the biomass feed port, which can be explained by the fact that the biomass devolatilization rate is much higher so as to result in a quick decomposition of biomass. The fixed char can be seen in the whole bed owing to its low gasification rate. Because operating velocity is not high enough, a good mixing of binary mixture cannot be achieved, resulting in the non-uniformity of char distribution. Fig. 7 shows the instantaneous contour plots of reaction rates at 20s. It can be seen that the gasification reaction is dominant in the upper part of the bed. This can be explained by the fact that the biomass particles tend to float on the bed surface owning to their low density. In contrary, the reduction reactions of oxygen carriers are distributed in the whole bed with higher reaction rates near the biomass feed port, which is attributed to the syngas production by biomass pyrolysis near the biomass feed port. In addition, it can be noticed that there is a higher reduction reaction rate near the bubble owing to the contact between pyrolysis products and oxygen carriers, resulting in the non-uniform distribution of oxygen
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Fig. 6. Instantaneous contour plots of gas and solid species concentrations at 20s.
Fig. 7. Instantaneous contour plots of reaction rates at 20s.
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Fig. 8. Variation of the outlet gas compositions with time. Fig. 10. Effect of operating pressure on gas compositions.
carrier reduction reactions. Fig. 8 demonstrates the evolution of the outlet gas compositions with time. As the time proceeds, the gas products from biomass pyrolysis, gasification and combustion arrive at the exit of the reactor so that the concentrations are improved. When a relatively steady flow of oxygen carriers and biomass particles in the bed is reached, the variations of the corresponding gas compositions become insignificant. From the figure, we can recognize that the gas component concentrations fluctuate around a constant after 8s. The utilization of the recirculated CO2 as a gasifying agent can reduce the energy consumption by steam generation. Here, a comparison of CO2 and steam as gasifying agents is conducted, as shown in Fig. 9. We can find that CO has a slight rise with CO2 as a gasifying agent owing to the enhancement of the CeCO2 gasification whereas the H2 concentration is reduced. In addition, the predicted gas compositions using the recirculated CO2 of 50% and the steam of 50% are also shown in Fig. 9. We can realize that the change of the predicted gas species concentrations is not evident using the mixture of CO2 and steam compared to that using pure steam as a gasifying agent. Therefore, the recycled fuel reactor offgas (containing both CO2 and steam) provides a preferred option from an economic point of view. Fig. 10 shows the variation of the outlet gas compositions under
Fig. 9. Effect of gasifying agent on gas compositions.
different operating pressures. It can be noticed that when the operating pressure is changed from 0.1 MPa to 0.3 MPa, the CO2 concentration is increased by 5% whereas the CO concentration is reduced by 2.5%. This is attributed to the fact that a high pressure will reduce the bubble size and enhance the gas-solid contact, leading to a rising reaction degree. This also implies that the increase of operating pressure will facilitate the depletion of fuel gas released from biomass and improve the combustion efficiency. Meanwhile, we can realize that the influence becomes weak as the operating pressure further increases. The change of gas component concentrations is not evident. Thus, it is necessary for the selectivity of operating pressure to consider the tradeoff between the conversion efficiency and the capital cost. In order to examine the influence of biomass feed port locations, three different heights are selected, as shown in Fig. 11. It can be seen that as the feed port height from the reactor bottom increases from 0.02 m to 0.08 m, the CO2 molar fraction has a reduction of 6.4% while the CO concentration has an increase by 3.3% with the rising CH4 and H2 concentrations. From Fig. 5, we can find that the biomass tends to float at the upper section of bed. The gas products from biomass pyrolysis form the bubbles and pass through the bed. When the location of biomass inlet port is higher, the contact between the pyrolysis gas and oxygen carriers is not enough so that
Fig. 11. Effect of biomass feed port location on gas compositions.
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the pyrolysis gas flows into the freeboard, resulting in the reduction of CO2 concentration. Therefore, the in-bed fuel feed arrangement at a lower section of bed is beneficial to the CLC performance. 4. Conclusion A bubble-based bi-disperse drag model is implemented in the multi-fluid model to investigate the iG-CLC performance using biomass as fuel. The simulation result can give a good agreement with experimental data. The mixing behavior of biomass and oxygen carrier as well as the gas composition distribution during the iG-CLC process are well predicted. The impact of operating parameter is discussed. It is found that a rising pressure can increase the product CO2 concentration by 5%, whereas an excessively high operating pressure can not significantly influence the gas compositions. The reuse of CO2 is preferable as a gasification agent instead of H2O. Meanwhile, the effect of feed port location is examined. The results reveal that the arrangement of the fuel feed port at a low section of bed is beneficial for the CLC performance. In the future work, the regeneration and recirculation process of oxygen carriers will be taken into consideration to achieve a complete evaluation of the solid-fuelled CLC system. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research is conducted with financial support from the National Natural Science Foundation of China (51606053). Nomenclature a Cd d e E g J n P Re S0 u U X
acceleration [m s2] drag coefficient of a single particle particle diameter [m] restitution coefficient activation energy [KJ mol1] gravity [m s2] collisional energy dissipation [kg m1 s3] reaction order pressure [Pa] Reynolds number initial surface area[m2 m3] velocity [m s1] superficial velocity [m s1] conversion degree
Greek letters b drag coefficient [kg m3 s1] ε volume fraction q granular temperature [m2 s2] r density [kg m3] tQ stress tensor [Pa] kinetic energy production through slip between phase [kg m1 s3] Subscripts b e g p
bubble phase emulsion phase gas phase particle phase
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References [1] A. Nandy, C. Loha, S. Gu, P. Sarkar, M.K. Karmakar, P.K. Chatterjee, Present status and overview of chemical looping combustion technology, Renew. Sustain. Energy Rev. 59 (2016) 597e619. [2] J. Fan, H. Hong, H. Jin, Biomass and coal co-feed power and SNG polygeneration with chemical looping combustion to reduce carbon footprint for sustainable energy development: process simulation and thermodynamic assessment, Renew. Energy 125 (2018) 260e269. [3] J. Adanez, A. Abad, T. Mendiara, P. Gayan, L.F. de Diego, F. García-Labiano, Chemical looping combustion of solid fuels, Prog Energ Combust 65 (2018) 6e66. € ll, H. Hofbauer, Detailed fluid dynamic in[4] S. Penthor, M. Stollhof, T. Pro vestigations of a novel fuel reactor concept for chemical looping combustion of solid fuels, Powder Technol. 287 (2016) 61e69. [5] J. Ma, X. Tian, C. Wang, X. Chen, H. Zhao, Performance of a 50 kWth coalfuelled chemical looping combustor, Int J Greenh Gas Con 75 (2018) 98e106. [6] M. Zheng, L. Shen, X. Feng, In situ gasification chemical looping combustion of a coal using the binary oxygen carrier natural anhydrite ore and natural iron ore, Energy Convers. Manag. 83 (2014) 270e283. [7] C. Linderholm, M. Schmitz, M. Biermann, M. Hanning, A. Lyngfelt, Chemicallooping combustion of solid fuel in a 100kW unit using sintered manganese ore as oxygen carrier, Int J Greenh Gas Con 65 (2017) 170e181. [8] T. Schulzke, J. Westermeyer, H. Giani, C. Hornsby, Combustion of refined renewable biomass fuel (RRBF) in a bubbling fluidized bed, Renew. Energy 124 (2018) 84e94. [9] L. Stougie, G.A. Tsalidis, H.J. van der Kooi, G. Korevaar, Environmental and exergetic sustainability assessment of power generation from biomass, Renew. Energy 128 (2018) 520e528. rez-Astray, M.T. Izquierdo, A. Abad, L.F. de Diego, F. García[10] T. Mendiara, A. Pe n, J. Ada nez, Chemical looping combustion of different types Labiano, P. Gaya of biomass in a 0.5 kWth unit, Fuel 211 (2018) 868e875. [11] Z. Shen, Z. Huang, High-efficiency and pollution-controlling in-situ gasification chemical looping combustion system by using CO2 instead of steam as gasification agent, Chin. J. Chem. Eng. 26 (2018) 2368e2376. [12] M. Luo, S. Wang, L. Wang, M. Lv, L. Qian, H. Fu, Experimental investigation of co-combustion of coal and biomass using chemical looping technology, Fuel Process. Technol. 110 (2013) 258e267. [13] Y. Shao, Y. Zhang, X. Wang, X. Wang, B. Jin, H. Liu, Three-dimensional full loop modeling and optimization of an in situ gasification chemical looping combustion system, Energy Fuel. 31 (12) (2017) 13859e13870. [14] S. Wang, W. Yin, Z. Li, X. Yang, K. Zhang, Numerical investigation of chemical looping gasification process using solid fuels for syngas production, Energy Convers. Manag. 173 (2018) 296e302. [15] Z. Peng, E. Doroodchi, Y. Alghamdi, B. Moghtaderi, Mixing and segregation of solid mixtures in bubbling fluidized beds under conditions pertinent to the fuel reactor of a chemical looping system, Powder Technol. 235 (2013) 823e837. € hle, B. Epple, Extended Euler-Euler model for [16] F. Alobaid, P. Ohlemüller, J. Stro the simulation of a 1 MWth chemical-looping pilot plant, Energy 93 (2015) 2395e2405. [17] Y. Guan, J. Chang, K. Zhang, B. Wang, Q. Sun, D. Wen, Three-dimensional full loop simulation of solids circulation in an interconnected fluidized bed, Powder Technol. 289 (2016) 118e125. [18] X. Gao, T. Li, A. Sarkar, L. Lu, W.A. Rogers, Development and validation of an enhanced filtered drag model for simulating gas-solid fluidization of Geldart A particles in all flow regimes, Chem. Eng. Sci. 184 (2018) 33e51. [19] Z. Shi, W. Wang, J. Li, A bubble-based EMMS model for gas-solid bubbling fluidization, Chem. Eng. Sci. 66 (22) (2011) 5541e5555. [20] S. Wang, X. Yang, S. Xu, K. Zhang, B. Li, Evaluation of sorption-enhanced reforming of biodiesel by-product in fluidized beds by means of CFD approach, Fuel 214 (2018) 115e122. [21] S. Wang, K. Zhang, S. Xu, X. Yang, Assessment of a bubble-based bi-disperse drag model for the simulation of a bubbling fluidized bed with a binary mixture, Powder Technol. 338 (2018) 280e288. [22] M. Syamlal, M.F.I.X. Documentation, Numerical Techniques, National Technical Information Service, Springfield, VA, 1998. DOE/MC-31346-5824. NTIS/ DE98002029. [23] A. Srivastava, S. Sundaresan, Analysis of a frictional-kinetic model for gasparticle flow, Powder Technol. 129 (2003) 72e85. [24] M. Syamlal, The Particle-Particle Drag Term in a Multiparticle Model of Fluidization, National Technical Information Service, 1987. nez, [25] T. Mendiara, A. Abad, L.F. de Diego, F. García-Labiano, P. Gay an, J. Ada Biomass combustion in a CLC system using an iron ore as an oxygen carrier, Int J Greenh Gas Con 19 (2013) 322e330. [26] H. Liu, R.J. Cattolica, R. Seiser, C. Liao, Three-dimensional full-loop simulation of a dual fluidized-bed biomass gasifier, Appl. Energy 160 (2015) 489e501. [27] R.C. Everson, H.W.J.P. Neomagus, H. Kasaini, D. Njapha, Reaction kinetics of pulverized coal-chars derived from inertinite-rich coal discards: gasification with carbon dioxide and steam, Fuel 85 (7e8) (2006) 1076e1082. [28] M. Su, H. Zhao, J. Ma, Computational fluid dynamics simulation for chemical looping combustion of coal in a dual circulation fluidized bed, Energy Convers. Manag. 105 (2015) 1e12. [29] F. Bustamante, R. Enick, A. Cugini, R. Killmeyer, B. Howard, K. Rothenberger,
Please cite this article as: W. Yin et al., Numerical investigation of in situ gasification chemical looping combustion of biomass in a fluidized bed reactor, Renewable Energy, https://doi.org/10.1016/j.renene.2019.11.016
10
W. Yin et al. / Renewable Energy xxx (xxxx) xxx
High-temperature kinetics of the homogeneous reverse wateregas shift reaction, AIChE J. 50 (2004) 1028e1041. [30] K. Mahalatkar, J. Kuhlman, E.D. Huckaby, T. O’Brien, CFD simulation of a chemical-looping fuel reactor utilizing solid fuel, Chem. Eng. Sci. 66 (16) (2011) 3617e3627.
[31] D. Gidaspow, Multiphase Flow and Fluidization: Continuum and Kinetic Theory Descriptions, Academic Press Inc., Boston, 1994. [32] E.R. Monazam, R.W. Breault, J. Weber, K. Layfield, Elutriation of fines from binary particle mixtures in bubbling fluidized bed cold model, Powder Technol. 305 (2017) 340e346.
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