Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

CPFD simulation on particle behaviour in an entrained-flow gasifier

CPFD simulation on particle behaviour in an entrained-flow gasifier A computational particle fluid dynamics simulation model for entrained-flow gasification was established in this study. The simulation results agree with The recirculation zone the experimental data. The detailed particle information and residence-time distribution were obtained by injecting particle tracers in the simulation. The results show that the particles in the gasifier can be classified into three flowing zones, i.e. a fast-flowing zone, a recirculation zone and a spreading zone. The criterion for this classification was also provided. The rapid gas expansion caused by the fast reactions plays a significant role in forming The spreading zone the particle stream into these three zones. It accelerates the particles in the centre of the gasifier while pushing the particles near the expansion edge into the gas recirculation. Also, the concentrated oxygen distribution in the gasifier results in the formation of high- and low-temperature regions. The particles in the fast-flowing zone flow directly through the high-temperature The fast-flowing zone region and most of these particles in this zone were fully reacted with a short residence time. Since particles in the recirculation zone are in a relatively low-temperature region, most of these particles are not fully gasified, although with a long residence time. The rest of particles in the spreading zone show moderate properties between the above two zones. Keywords: CPFD simulation, entrained-flow gasifier, particle behaviour, gasification process [1]. However, the hydrodynamics and reactions in the Introduction entrained-flow gasifier are quite complex and their mech- Entrained-flow gasification is most widely used in the coal anisms still are not fully understood. This limits the fur - chemical industry. The gasifier can process a large volume ther optimization of gasifier design. A further investigation of coal at high temperature with high gasification efficiency Received: 1 September 2019; Accepted: 18 November 2019 © The Author(s) 2020. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any 1 medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 2 | Clean Energy, 2019, Vol. XX, No. XX into their mechanisms is of importance. While the charac- downer [18, 19], where gas–solid behaviour is similar to that teristics of the hydrodynamics and reactions in the gasifier in an entrained-flow reactor. CPFD provides a new way to are difficult to obtain using experimental tests, simulation investigate the entrained-flow gasifier in a transient-state simulation. There are two publications [15 20 , ] concerning using computational fluid dynamics is becoming an im- fluidized-bed gasification using CPFD simulation, but none portant tool to investigate these characteristics. about entrained-flow gasification has been reported. This As one of the earliest entrained-flow gasifiers, the one- stage down-fired gasifier has been extensively studied. Wu study will investigate a full-size entrained-flow gasifier by et  al. [2, 3] established a simulation model for a General using CPFD simulation. A  model for the gasification will Electric (GE) gasifier by using the simplified probability be established. Also, the comparison results of experiment density function (PDF) model. The simulation results agree and simulation will be shown. Most importantly, the spe- with the industrial data. Bi et al. [4] modelled a Gaskombinat cific particle information will be obtained and a classifica- Schwarze Pumpe (GSP) gasifier and obtained its flow field tion of particles in the gasifier will be made based on their and gas composition by using the same model. Ma and hydrodynamics. Zitney [1] simulated both one-stage and two-stage gasifiers by using improved physical and chemical submodels. 1 Establishment of the simulation model Chen et  al. [5] developed a numerical method to predict coal gasification in an entrained-flow gasifier emphasizing The entrained-flow gasifier simulated in this study is the the influence of the injection patterns. Vascellari et al. [6, same as the gasifier established by Brown et  al. [21] in 7] developed advanced submodels for both the pyrolysis their experimental study. As shown in Fig. 1, its diameter process and char reactions. Their simulation results agree is 200 mm and its length is 2.0 m. The symbols x and z in with the experimental data. Then, Richter [8] analysed the Fig. 1 indicate the radial and axial direction, respectively. reacting particles in the entrained-flow gasifier based on The Utah Bituminous coal was used and its properties are their study. Abani and Ghoniem [9] captured the character - shown in Table 1. A single burner was mounted at the top istics of the turbulence in a small entrained-flow gasifier of the gasifier in the experiment with two channels, i.e. through large eddy simulation. Kumar and Ghoniem [10, a primary channel in the centre and an annulus channel. 11] established a multi-scale model for the entrained- Coal particles and oxygen were injected through the pri- flow gasifier then applied it in both the GE and Mitsubishi mary channel while the water vapor was injected in the Heavy Industries (MHI) gasifiers to investigate the effect of annulus channel. The operating conditions and particle- the particle grinding size on carbon conversion [12]. feed composition are shown in Table 2 and the particle- Due to the complexity of the gasification reaction size distribution for the coal particles is presented in and the limitation of the computational power, the geo- Table 3. metric models for some of these studies [2–5] were often A 3D geometric model (circular cylinder) was estab- simplified to 2D or pseudo-3D model. In the pseudo-3D lished in Solidworks software, then meshed using the model, only half or one-quarter of the whole gasifier was mesh generator—a built-in module in Barracuda software. modelled and a periodic boundary condition was applied. The mesh density in the centre of the gasifier was higher Also, most of these studies [2–5] were based on the steady- to match the complexity of the hydrodynamics and reac- state simulation results. Several studies [2–4] assume that tions in this area. Liang et al. [22] indicate that the impact the homogeneous reactions in the gasifier are in a chem- of mesh density on the CPFD-simulation results is limited. ical equilibrium. Furthermore, the particle trajectories and The mesh number applied in this study was 15 372. In the overall particle properties were discussed in only a few CPFD simulation, the number of computational particles previous studies [1, 8]. Little specific particle information, is controlled by a parameter named the ‘number density’ i.e. temperatures, carbon contents and locations for the [23]. It was set to 200–2000 in most of the simulation cases. discrete particles, was provided. Since more computational particles are needed to simu- Recently, computational particle fluid dynamics (CPFD) late a dilute gas–solid reactor, the number density in this was developed by Snider [13] and applied in simulations study was set to 10 000, which corresponds to an injection of gas–solid reactors. CPFD is one of the Discrete Element into the simulated gasifier of 5966 computational particles Methods (DEM) [14] based on the Euler-Lagrange model. per second. Being different from the general DEM, CPFD combines the There are mainly two kinds of inlet boundary conditions particles with the same properties into one particle parcel, (BCs) in CPFD simulation, i.e. flow BCs and injection BCs. i.e. one computational particle [13]. Also, multiphase The injection BCs can be used to simulate the injection particle-in-cell (MP-PIC) is used to simplify the particle– effect of the nozzle with relatively coarser meshes. So, the particle interactions in the CPFD model [13 15 , ]. These two primary channel used this kind of BC to save computing features greatly improve the computing efficiency of CPFD power. The initial speeds for both particles and gas were simulation and enable it to model the 3D gas–solid reactors set to 20 m/s. A particle expansion angle of 40° was used. with numerous discrete particles. It has been successfully However, the actual expansion angle in the simulation is applied in investigating the gas–solid riser [1617 , ] and largely affected by the gas behaviour and it was finally Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 Liang et al | 3 x Primary Secondary Outlet stream stream Fig. 1: The schematic of the gasifier model and CPFD boundary conditions Table 1: The properties of the Utah Bituminous coal [21] Proximate analysis Ash V olatiles Fixed carbon High heating value –1 Moisture (wt. %) (wt. %) (wt. %) (wt. %) (MJ.kg , dry) 2.4 8.3 45.6 43.7 29.8 Elemental analysis C H O N S (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) 71 6 12.7 1.3 0.5 Table 2: The experimental test conditions operated by Brown 2 The numerical model et al. [21] 2.1 The governing equations –1 Primary flow rate, kg.s 0.00729 The governing equations for the CPFD simulations are the Primary particle loading, kg coal/kg primary gas 0.91 general ones described by Snider et al. [13, 15]. Primary stream temperature, K 367 Primary gas composition, mole fraction O 0.85 2 2.2 Chemical reactions Ar 0.126 2.2.1 The devolatilization model H O 0.024 The coal-devolatilization rate is very fast due to the high –1 Secondary flow rate, kg.s 0.00184 temperature in the gasifier [4, 9]. The expression for the Secondary stream temperature, K 450 coal devolatilization [25] is: Secondary gas composition, mole fraction H O 1.0 Coal → a CH + a CO + a CO + a H 1 x 2 3 2 4 2 + a H O + a N + a Char 5 2 6 2 7 (1) Table 3: The particle-size distribution used in this study and where a + a + a + a + a + a + a = 1. 1 2 3 4 5 6 7 the experiment by brown et al. [21] Similarly to the simulation method of devolatilization Weight % Particle-size distribution, μm used by Abani and Ghoniem [9], the inert gas during the devolatilization is neglected in this study and the gas prod- 20% 3 ucts are assumed to be CH, CO, CO , H and H O. 20% 20 2 2 2 2 A one-step model is used to describe the devolatilization 20% 28 rate [26]: 20% 50 Å ã 20% 80 dm E V V (2) = −A exp m v V dt RT where m is the remaining volatile mass in the particle and reshaped to ~20°, as shown in Fig. 4, at 1.018 s in this study. T is the weighted temperature [23] of the particle (0.8) and This angle is consistent with that in a down-fired gasifier 5 –1 the gas (0.2). The activation energy, E , is 2.1 × 10 s [2] and study simulated by Wu et al. [3]. On the other hand, the an- 7 –1 the pre-exponential factor, A , is 3.28 × 10 J.kmol [2]. nular channel was set with the flow BC, as its area is larger. Also, the annular channel was assumed to be square to re- 2.2.2 Heterogenous reaction model duce the number of Cartesian-coordinate-based meshes in After coal devolatilization, the particles mainly consist the CPFD simulation, which can further improve the com- of char and ash. Three main heterogeneous reactions are puting efficiency [24]. Besides, an atmospheric-pressure used to describe the char reaction [9, 27]: boundary was set at the bottom of the gasifier, where both C + 0.5O → CO (R1) gas and particles can leave via this exit. Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 4 | Clean Energy, 2019, Vol. XX, No. XX computation. These models were introduced for this study C + CO → 2CO (R2) and their expressions are listed in Table 5. C + H O → CO + H (R3) 2 2 The studies by Wu et al. [2] and Bi et al. [4] show that the 2.3 The thermal model heterogenous reaction rate is determined by both the in- The general CPFD thermal model [23] was employed in this trinsic chemical rate and the diffusion rate. The expression study. This model includes the models for both gas–solid for the heterogeneous reaction rate in this study is intro- and gas–wall heat transfer. The expressions and their ex- duced from their studies, which is: planations are stated in the literature [23]. Also, the heat R R i,d i,k loss is considerable in the experiment [21] and must be in- R = (3) R + R i,d i,k cluded in the simulation. The wall temperature of the simu- lated gasifier was set as the same value (1100 K) tested by where R is the heterogenous reaction rate for the ith gas Brown et al. [21] using a thermocouple on the gasifier wall with the char particle and R is the diffusion rate, which i,d in their experiment [21]. Besides, the temperature of the can be described as wall adjacent to the primary channel was set to the same 0.75 [ T +T /2] p f (4) value as the feeding-particle temperature. Moreover, the R = C P i,d i i built-in radiation model [23] in Barracuda was also applied 12 –0.75 where C , the diffusion coefficient, is 5  × 10 s.k in this in this study to further improve the simulation results. study; T is the particle temperature; is the g T as tempera- p f ture; and d is the particle diameter. R is the apparent re- p i,k 2.4 The other models action rate and can be described as Å ã Ä ä The drag model used in this study was the Wenyu–Ergun E n R = A exp − P /10 (5) i,k i i correlation. Its expression is described in the literature RT [23]. Besides, the model of large eddy simulation (LES) [36] where n is the order of this reaction model; A is the pre- was applied in this study. Its detailed description is stated exponential factor; E is the activation energy; and P is the i i in the literature [23]. partial pressure for the ith gas. The values [4 2] for e , very parameter in this model are listed in Table 4. 3 Simulation results 2.2.3 Homogeneous reactions 3.1 Validation of the simulated results Based on the same simulation method of Abani and Ghoniem [9] as well as Anderson et al. [31], the hydrocarbons Table 6 shows the comparison between the CPFD- in this study are expressed as CH and the reaction rates of 2 simulation results and the experimental results of Brown CH are adopted for CH. Six main reactions are used to de- 4 2 et  al. [21]. All of the simulation and experimental results scribe the homogeneous reactions in the gasification [27]: were obtained from the central area at a height of 0.23 m above the bottom of the gasifier. The simulation time was CO + 0.5O → CO (R4) 2 2 4 s and the simulation results were averaged results in 4 s. H + 0.5O → H O (R5) 2 2 2 As shown in Table 6, the simulation results agree with the experimental data, which validates the CPFD model estab- CH + 1.5O → CO + H O(R6) 2 2 2 2 lished in this study. CO + H O → CO + H (R7) 2 2 2 CO + H → CO + H O(R8) 2 2 2 3.2 The distributions of gas temperature and velocity in the gasifier CH + H O → CO + 2H(R9) 2 2 2 Yan et al. [32] established a set of reaction models for the Fig. 2a shows the averaged temperature (K) distribution homogeneous reactions of gasification. The expressions in the simulated gasifier. As shown in Fig. 2a, there are of these models are concise, which is beneficial for the Table 5: The expressions for homogeneous reaction rates Table 4: The values used for parameters of the heterogeneous –3 –1 Reactions Reaction rate (kmol.m.s ) reaction rates 0.5 0.5 R4 [33] 1 × 10 exp −15154.25/T C C C . g CO O H O 2 2 A E i i 9 R5 [33] 2.2 × 10 exp −13109.63/T C C g H O –2 –1 –n –1 2 2 Reactions (kg.m .s .Pa ) (J.kmol ) n 0.2 1.3 R6 [34] 2.119 × 10 exp −24379.1/T C C CH O 4 2 R1 [28] 300 1.3 × 10 0.65 R7 [35] 2.5 × 10 exp −16597.5/T C C g CO H O R2 [29] 2224 2.2 × 10 0.6 R8 [35] 9.43 × 10 exp −20563.51/T C C g CO H 2 2 R3 [30] 42.5 1.42 × 10 0.4 R9 [25] 0.312 exp −30000/ 1.987T C g CH 4 Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 Liang et al | 5 Table 6: The data comparison between simulation and experiment CO CO H O H 2 2 2 Data (mol %) (mol %) (mol %) (mol %) Carbon conversion % Outlet gas temperature (K) Simulation 34.1 12.4 28.7 15.3 82.3 1453.2 Experiment [21] 34 16 28 17 82 1350~1400 AB C 4.0000048e+00 O2 0.8 Nodes vect Cells av_Tf mag 0.7 17.5 0.6 0.5 12.5 0.4 0.3 7.5 0.2 0.1 2.5 Fig. 2: The distributions of the time-averaged (t = 0~4.0 s) gas temperature (a), gas speed (b) and oxygen mole fraction (c) in the gasifier two distinct temperature regions in the gasifier: a high- caused by the fast reactions, where the gas temperature temperature region in the upper half of the central gasifier increases sharply from 450 K to >3000 K. The gas expands (red area) and a lower-temperature region near the wall. in both radial and axial directions, with its down-flowing The general temperature difference for these two regions is direction being the most prominent. The central gas speed more than 1000 K. Fig. 2b shows the averaged gas-velocity in this expansion is almost four times that on the edge of (m/s) distribution. As shown in Fig. 2b , there is gas recircu- the expansion. This largely reshapes the particle behaviour lation near the wall at the top of the gasifier. Meanwhile, in the gasifier and will be discussed in Section 4.4. the central gas gradually turns into the plug flow as it flows Fig. 2c shows the averaged oxygen mole fraction dis- downward in the gasifier. These results agree with the re- tribution in the simulated gasifier. The oxygen is highly ports in the literature [4, 27]. concentrated beneath the burner and is consumed quickly As clearly shown in the amplified picture for Fig. 2b , by the fierce oxidation reactions. This forms the high- there exists a rapid gas expansion in the simulated gasifier temperature region shown in Fig. 2a. In other words, the Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 6 | Clean Energy, 2019, Vol. XX, No. XX concentrated oxygen distribution results in the formation Fig. 3b and c show the particle residence time and speed of the high- and low-temperature regions discussed above. distribution at 4.0 s, respectively. As shown in these two The high-temperature region can be seen as an excellent figures, the particles in the central region at the upper- half reaction area, with a sufficient gas reactant of oxygen and gasifier have the lowest residence times due to the highest particles flowing through this region to be consumed rap- downward speed, whereas the particles in the wall region idly. This will be further illustrated in Sections 4.3–4.5. have longer residence times with low speed. The particle- residence times are especially longer for particles en- trained by the gas recirculation, as they first flow upward –1 3.3 The distributions of particle temperature, then downward with speeds below 5 m.s . These results speed, residence time and carbon content can further illustrate the particle carbon-content distribu- tion shown in Fig. 3d. Fig. 3 shows the sectional views of the simulation results for Fig. 3d shows the particle carbon content (mass fraction) particles at 4.0 s. As shown in Fig. 3a, the particle-temperature in the simulated gasifier. As shown in Fig. 3d, the volatile distribution is similar to that for the gas shown in Fig. 2a. The matter was quickly released from the particles. Also, the particle temperatures in the central region are becoming particle carbon contents are generally lower in the cen- lower with particles flowing downward. Meanwhile, the par - tral region and higher in the wall region. The particles in ticles near the wall remain at relatively low temperatures the central region have shorter residence time, but their (1100 K) as they reach a balance of heat transfer to the wall. conversion is higher. This indicates that a long particle- The carbon conversion of the particles is highly related to residence time may not always result in a good particle the temperature and will be discussed later. A Particles Temperat BC Particles ResTime Particles Speed D Particles mf–S–C 2840 4 23 0.84 20.7 0.756 2592.7 3.6 18.4 2345.4 3.2 0.672 2.8 16.1 0.588 2098.1 2.4 13.8 0.504 1850.8 11.5 2 0.42 1603.5 1.6 9.2 0.336 1356.2 1.2 6.9 0.252 1108.9 0.8 4.6 0.168 861.6 0.4 2.3 0.084 614.3 0 0 Fig. 3: The instantaneous distributions of the particle temperature (a), residence time (b), particle speed (d) and carbon content (d) at 4.0 s Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 Liang et al | 7 carbon conversion. The influence of the particle tempera- the particle tracers were first centrally injected from the top ture and the gas-reactant distribution should also be in- of the gasifier then (t = 1.044 s) distributed as an inverted- cluded. For a one-stage down-fired gasifier, the particles cone similar to the gas distribution shown in Fig. 2b . in the central region have more opportunities to achieve a As shown in Fig. 4, at 1.080 s, the axial particle speeds in the higher conversion, as they are not only exposed to a high central region are much faster than in the other regions. temperature, but also get easier access to the gas reactants Meanwhile, the particles closer to the gasifier wall are like oxygen. The detailed particle-reaction process will be turning back and flowing upward after 1.044  s, entrained discussed later by injecting particle tracers. by the gas recirculation shown in Fig. 2b . This indicates that the particle tracers quickly spread in both axial and radial directions due to the rapid gas expansion caused by 3.4 The detailed particle-reaction process the fast reactions shown in Fig. 2b . This rapid gas expan- The detailed particle-reaction process was investigated by sion is the driving force to push one portion of the par - particle tracers. Since the gas–solid flow in the gasifier was ticles into the gas recirculation and keep another portion steady after a simulation time of 1.0 s, the feeding coal par - of the particles with a high down-flowing speed. Also, the ticles from simulation time 1.0 to 1.03  s were marked as carbon contents for particles, flowing directly through the particle tracers in this study. Note that only the particle high-temperature region, are nearly zero, while the carbon identity was changed during this short period of time, contents for particles closer to the wall are still nearly 80%. while the particle properties and flow rate remained the On the other hand, a schematic of the general route for same throughout the entire simulation. The following re- the particles entrained by the gas recirculation is summar - sults were collected from the particle tracers. ized from the simulation results and is shown in Fig. 5. Fig. 4 shows the distribution of carbon contents (mass Note that the particle paths shown in Fig. 5 are in a sequen- fraction) for particle tracers at different times. Note that tial order denoted with numbers from 1 to 5.  As shown only particle tracers can be seen in Fig. 4, which means that in Fig. 5, the recirculated particles cannot enter the high- all the other feeding coal particles coexisting in the gasifier temperature region although they flowed through its edge. –1 were hidden for an easier observation. As shown in Fig. 4, This is because their particle speeds are too small (0.9 m.s Particles mf-S-C 0.85 t = 1.018 s t = 1.044 s t = 1.080 s t = 1.132 s t = 1.198 s t = 1.518 s 0.765 0.68 0.595 0.51 0.425 0.34 0.255 0.17 0.085 Fig. 4: The distribution of particle carbon content for particle tracers at different times Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 8 | Clean Energy, 2019, Vol. XX, No. XX 31 0.25 0.2 0.15 High temperature region 0.1 0.05 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3 Residence time (s) Fig. 6: The probability distribution of the particle-residence time The recirculation zone The spreading zone Fig. 5: The schematic of the general route for particles entrained by the The fast-flowing zone gas recirculation shown in Fig. 4 at t = 1.518 s) while the edge of the central –1 gas speed shown in Fig. 2b is about 10 m.s . These particles instantly flow downward as soon as they approach the edge of the central gas. As a result, no radial momentum is left for them to enter the central high-temperature region; instead, they flow downward along the edge of the central gas. Afterwards, they are pushed towards the wall surface, Fig. 7: The schematic of the three zones for particles in the simulated gasifier driven by the gas expansion, and then flow downward along the wall surface with a temperature of only 1100 K, through the rest of their lifetimes in the simulated gasifier. consists of the fully reacted particles from the central re- Consequently, they have the longest particle-residence gion, whose speeds are much faster due to their much times but the lowest carbon conversions. smaller particle masses and the acceleration of the gas expansion. The ‘long tail’ after 2.0 s shown in Fig. 6 mainly consists 3.5 The probability distribution of the of the particles entrained by the gas recirculation, whose particle-residence time flowing route is much longer. However, their conversion Fig. 6 shows the residence-time probability distribution of is low, since their route provides little access to the high- the particle tracers by counting the particle numbers in temperature region. These particles account for 14.8% of every interval of 0.2 s at the gasifier outlet. As shown in Fig. 6 , all the particles in the gasifier. Besides, the other particles a considerable number (13%) of particle tracers flew out that gradually spread around the entire gasifier contribute of the gasifier during 0.4–0.6 s, forming a shortcut particle to the probability distribution during 0.6–2 s shown in Fig. 6. flow. This result, combined with the results shown in Fig. 4 These particles are the majority, which flow downward at 1.518 s, indicates that the shortcut particle flow mainly with a relatively slow speed and a moderate conversion. Probability distribution Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 Liang et al | 9 Based on the analysis above, the particles in the gasifier Nomenclature can be classified into three zones: a fast-flowing zone, a –2 –1 –n A Pre-exponential factor kg.m.s .Pa recirculation zone and a spreading zone. Fig. 7 shows the A Pre-exponential factor for coal devolatilization, schematic of these three zones for particles in the simu- –1 J.koml lated gasifier. The particle distribution in Fig. 7 is based a Mass fraction on the results shown in Fig. 4 at 1.198  s. As shown in –0.75 C The diffusion coefficient, s.k Fig. 7, the particles with upward axial speed are colored d Particle diameter, m –1 in pink and this is also the criterion for the particles of E Activation energy, J.kmol –1 E Activation energy for coal devolatilization, s the recirculation zone. Its corresponding residence-time m The remaining volatile mass in the particle, kg range is 2.0–3.0  s shown in Fig. 6. On the other hand, n Order of reaction based on the first and distinct peak value shown in Fig. 6 –2 –1 R Heterogenous reaction rate for the ith gas, kg.m .s (0.4–0.6  s), the fast-flowing zone can be identified and P Partial pressure for the ith gas, Pa denoted in Fig. 7. Besides, the rest of the particles in the T Weighted temperature, K gasifier can be classified as the spread zone (0.6~2.0  s T Fluid temperature, K shown in Fig. 6). T Particle temperature, K t Time, s Conflict of Interest 4 Conclusion None declared. A CPFD-simulation model for entrained-flow gasification was established in this study. The simulation results agree References with the experimental data. The detailed particle infor - [1] Ma  J, Zitney  SE. Computational fluid dynamic modeling of mation was obtained. The results show that the particles entrained-flow gasifiers with improved physical and chem- in the gasifier can be classified into three zones, i.e. a fast- ical submodels. Energ Fuel 2012; 26:7195–219. flowing zone, a recirculation zone and a spreading zone. [2] Wu Y, Zhang J, Smith, et al. Three-dimensional simulation for an The criterion for this classification was also provided. entrained flow coal slurry gasifier. Energ Fuel 2010; 24:1156–63. The gas expansion plays a significant role in forming [3] Wu  Y, Zhang  J, Wang  M, et  al. 3D numerical simulation of Texaco gasifier using assumed PDF model. Journal of Chemical the particle stream into these three zones. It accelerates Industry and Engineering (China) 2007; 58:2369–74. the axial speed of particles in the centre of the gasifier [4] Bi  D, Guan  Q, Xuan  W, et  al. Numerical simulation of GSP while it pushes another portion of particles into the gas gasifier based on double-mixture fractions PDF model. Journal recirculation. of Chemical Industry and Engineering (China) 2014; 65:3753–9 Also, the concentrated oxygen distribution in the gasifier [5] Chen  C, Hung  C, Chen  W. Numerical investigation on per - results in the formation of high- and low-temperature re- formance of coal gasification under various injection patterns gions, and particles flowing through the high-temperature in an entrained flow gasifier. Appl Energ 2012; 100:218–28. [6] Vascellari M, Arora R, Pollack M, et al. Simulation of entrained region will have sufficient time to react with the oxygen. flow gasification with advanced coal conversion submodels. Particles in the fast-flowing zone flow directly through the Part 1: Pyrolysis. Fuel 2013; 113:654–69. high-temperature region with a high speed. Most of the [7] Vascellari  M, Arora  R, Hasse  C. Simulation of entrained flow particles in this zone are fully reacted, although their resi- gasification with advanced coal conversion submodels. Part dence times are very short. While particles in the recircula- 2: Char conversion. Fuel 2014; 118:369–84. tion part have little access to the high-temperature region [8] Richter  A, Vascellari  M, Nikrityuk  PA, et  al. Detailed analysis and most of the particles in this zone still have high carbon of reacting particles in an entrained-flow gasifier. Fuel Process Technol 2016; 144:95–108. content, their residence times are very long. The particles [9] Abani G, Ahmed F. Large eddy simulations of coal gasification in the spreading zone gradually spread throughout the en- in an entrained flow gasifier. Fuel 2013; 104:664–80. tire gasifier with a moderate residence time, temperature [10] Kumar M, Ghoniem AF. Multiphysics simulations of entrained and carbon conversion. flow gasification. Part I: Validating the nonreacting flow solver The detailed particle behaviour obtained in this study and the particle turbulent dispersion model. Energ Fuel 2011; can deepen our understanding of the complex reac- 26:451–63. tion process in the gasifier and provide a scientific basis [11] Kumar M, Ghoniem AF. Multiphysics simulations of entrained flow gasification. Part II: Constructing and validating the for better gasifier design. Usually, the geometrical model overall model. Energ Fuel 2012; 26:464–79. of a simulated gasifier needs to be simplified due to the [12] Kumar M, Ghoniem AF. Application of a validated gasification computing-power constraint. However, some of the non- model to determine the impact of coal particle grinding size symmetrically shaped gasifiers are not suitable to be on carbon conversion. Fuel 2013; 108:565–77. simplified as half-size or 2D geometries. So, CPFD provides [13] Snider DM. An incompressible three-dimensional multiphase a promising way to simulate those gasifiers with compli- particle-in-cell model for dense particle flows. J Comput Phys cated geometries. 2001; 170:523–49. Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 10 | Clean Energy, 2019, Vol. XX, No. XX [14] Deen  NG, Annaland  MVS, Hoef  MAVD, et  al. Review of dis- [25] Wen CY, Chaung TZ. Entrainment coal gasification modeling. crete particle modeling of fluidized beds. Chem Eng Sci 2007; Ind Eng Process Des Dev 1979; 18:684–95. 62:28–44. [26] Badzioch  S, Hawksley  PGW, Badzioch  S, et  al. Kinetics of [15] Snider  DM, Clark  SM, O’Rourke  PJ. Eulerian–Lagrangian thermal decomposition of pulverized coal particles. Ind Eng method for three-dimensional thermal reacting flow with ap- Chem Process Des Dev 1970; 9:684–95. plication to coal gasifiers. Chem Eng Sci 2011; 66:1285–95. [27] Sun  Z, Dai  Z, Zhou  Z, et  al. Numerical simulation of indus- [16] Chen  C, Werther  J, Heinrich  S, et  al. CPFD simulation trial opposed multiburner coal–water slurry entrained flow of circulating fluidized bed risers. Powder Technol 2013; gasifier. Ind Eng Chem Res 2012; 51:2560–9. 235:238–47. [28] Monson CR, Germane GJ, Blackham AU, et al. Char oxidation [17] Wang  Q, Niemi  T, Peltola  J, et  al. Particle size distribution in at elevated pressures. Combustion & Flame 1995; 100:669–83. CPFD modeling of gas-solid flows in a CFB riser. Particuology [29] Roberts DG, Harris DJ. Char gasification with O, CO , and H O: 2 2 2 2015; 21:107–17. effects of pressure on intrinsic reaction kinetics. Energ Fuel [18] Abbasi A, Islam MA, Ege PE, et al. CPFD flow pattern simula- 2000; 14:483–9. tion in downer reactors. Aiche J 2013; 59:1635–47. [30] Fang W. Study on reaction kinetic of steam-coal chars gasifi- [19] Lanza  A, Islam  MA, De  Lasa  H. CPFD modeling and experi- cation with TGA.J ournal of China Coal Society 2004; 29:350–3. mental validation of gas–solid flow in a down flow reactor. [31] Andersen J, Rasmussen CL, Giselsson T, et al. Global combus- Comput Chem Eng 2016; 90:79–93. tion mechanisms for use in cfd modeling under oxy-fuel con- [20] Kraft  S, Kirnbauer  F, Hofbauer  H. CPFD simulations of an ditions. Energ Fuel 2009; 23:1379–89. industrial-sized dual fluidized bed steam gasification [32] Yan L, Lim JC, Yue G, et al. Simulation of biomass-steam gas- system of biomass with 8MW fuel input. Appl Energ 2017; ification in fluidized bed reactors: model setup, compari- 190:408–20. sons and preliminary predictions. Bioresour Technol 2016; [21] Brown BW, Smoot LD, Smith PJ, et al. Measurement and pre- 221:625–35. diction of entrained‐flow gasification processes. Aiche J 1988; [33] Gómez-Barea A, Leckner B. Modeling of biomass gasification 34:435–46. in fluidized bed. Progress in Energy & Combustion Science 2010; [22] Liang Y, Zhang Y, Li T, et al. A critical validation study on CPFD 36:444–509. model in simulating gas–solid bubbling fluidized beds. Powder [34] Inc. F. Fluent 6.3-User’s Guide. New York: Fluent Inc., 2006. Technol 2014; 263:121–34. [35] Umeki  K, Yamamoto  K, Namioka  T, et  al. High temperature [23] Cpfd Software. Barracuda Virtual Reactor User Manual. steam-only gasification of woody biomass. Appl Energ 2010; Albuquerque: CPFD Software, LLC, 2016. 87:791–8. [24] Liang Y, Zhang Y, Lu C. CPFD simulation on wear mechanisms [36] Smagorinsky  JS. General circulation experiments with the in disk–donut FCC strippers. Powder Technol 2015; 279:269–81. primitive equations. Mon Weather Rev 1963; 91:99–164. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clean Energy Oxford University Press

CPFD simulation on particle behaviour in an entrained-flow gasifier

Loading next page...
 
/lp/oxford-university-press/cpfd-simulation-on-particle-behaviour-in-an-entrained-flow-gasifier-j7xEhQ6iX1
Copyright
© The Author(s) 2020. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy.
ISSN
2515-4230
eISSN
2515-396X
DOI
10.1093/ce/zkz032
Publisher site
See Article on Publisher Site

Abstract

A computational particle fluid dynamics simulation model for entrained-flow gasification was established in this study. The simulation results agree with The recirculation zone the experimental data. The detailed particle information and residence-time distribution were obtained by injecting particle tracers in the simulation. The results show that the particles in the gasifier can be classified into three flowing zones, i.e. a fast-flowing zone, a recirculation zone and a spreading zone. The criterion for this classification was also provided. The rapid gas expansion caused by the fast reactions plays a significant role in forming The spreading zone the particle stream into these three zones. It accelerates the particles in the centre of the gasifier while pushing the particles near the expansion edge into the gas recirculation. Also, the concentrated oxygen distribution in the gasifier results in the formation of high- and low-temperature regions. The particles in the fast-flowing zone flow directly through the high-temperature The fast-flowing zone region and most of these particles in this zone were fully reacted with a short residence time. Since particles in the recirculation zone are in a relatively low-temperature region, most of these particles are not fully gasified, although with a long residence time. The rest of particles in the spreading zone show moderate properties between the above two zones. Keywords: CPFD simulation, entrained-flow gasifier, particle behaviour, gasification process [1]. However, the hydrodynamics and reactions in the Introduction entrained-flow gasifier are quite complex and their mech- Entrained-flow gasification is most widely used in the coal anisms still are not fully understood. This limits the fur - chemical industry. The gasifier can process a large volume ther optimization of gasifier design. A further investigation of coal at high temperature with high gasification efficiency Received: 1 September 2019; Accepted: 18 November 2019 © The Author(s) 2020. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any 1 medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 2 | Clean Energy, 2019, Vol. XX, No. XX into their mechanisms is of importance. While the charac- downer [18, 19], where gas–solid behaviour is similar to that teristics of the hydrodynamics and reactions in the gasifier in an entrained-flow reactor. CPFD provides a new way to are difficult to obtain using experimental tests, simulation investigate the entrained-flow gasifier in a transient-state simulation. There are two publications [15 20 , ] concerning using computational fluid dynamics is becoming an im- fluidized-bed gasification using CPFD simulation, but none portant tool to investigate these characteristics. about entrained-flow gasification has been reported. This As one of the earliest entrained-flow gasifiers, the one- stage down-fired gasifier has been extensively studied. Wu study will investigate a full-size entrained-flow gasifier by et  al. [2, 3] established a simulation model for a General using CPFD simulation. A  model for the gasification will Electric (GE) gasifier by using the simplified probability be established. Also, the comparison results of experiment density function (PDF) model. The simulation results agree and simulation will be shown. Most importantly, the spe- with the industrial data. Bi et al. [4] modelled a Gaskombinat cific particle information will be obtained and a classifica- Schwarze Pumpe (GSP) gasifier and obtained its flow field tion of particles in the gasifier will be made based on their and gas composition by using the same model. Ma and hydrodynamics. Zitney [1] simulated both one-stage and two-stage gasifiers by using improved physical and chemical submodels. 1 Establishment of the simulation model Chen et  al. [5] developed a numerical method to predict coal gasification in an entrained-flow gasifier emphasizing The entrained-flow gasifier simulated in this study is the the influence of the injection patterns. Vascellari et al. [6, same as the gasifier established by Brown et  al. [21] in 7] developed advanced submodels for both the pyrolysis their experimental study. As shown in Fig. 1, its diameter process and char reactions. Their simulation results agree is 200 mm and its length is 2.0 m. The symbols x and z in with the experimental data. Then, Richter [8] analysed the Fig. 1 indicate the radial and axial direction, respectively. reacting particles in the entrained-flow gasifier based on The Utah Bituminous coal was used and its properties are their study. Abani and Ghoniem [9] captured the character - shown in Table 1. A single burner was mounted at the top istics of the turbulence in a small entrained-flow gasifier of the gasifier in the experiment with two channels, i.e. through large eddy simulation. Kumar and Ghoniem [10, a primary channel in the centre and an annulus channel. 11] established a multi-scale model for the entrained- Coal particles and oxygen were injected through the pri- flow gasifier then applied it in both the GE and Mitsubishi mary channel while the water vapor was injected in the Heavy Industries (MHI) gasifiers to investigate the effect of annulus channel. The operating conditions and particle- the particle grinding size on carbon conversion [12]. feed composition are shown in Table 2 and the particle- Due to the complexity of the gasification reaction size distribution for the coal particles is presented in and the limitation of the computational power, the geo- Table 3. metric models for some of these studies [2–5] were often A 3D geometric model (circular cylinder) was estab- simplified to 2D or pseudo-3D model. In the pseudo-3D lished in Solidworks software, then meshed using the model, only half or one-quarter of the whole gasifier was mesh generator—a built-in module in Barracuda software. modelled and a periodic boundary condition was applied. The mesh density in the centre of the gasifier was higher Also, most of these studies [2–5] were based on the steady- to match the complexity of the hydrodynamics and reac- state simulation results. Several studies [2–4] assume that tions in this area. Liang et al. [22] indicate that the impact the homogeneous reactions in the gasifier are in a chem- of mesh density on the CPFD-simulation results is limited. ical equilibrium. Furthermore, the particle trajectories and The mesh number applied in this study was 15 372. In the overall particle properties were discussed in only a few CPFD simulation, the number of computational particles previous studies [1, 8]. Little specific particle information, is controlled by a parameter named the ‘number density’ i.e. temperatures, carbon contents and locations for the [23]. It was set to 200–2000 in most of the simulation cases. discrete particles, was provided. Since more computational particles are needed to simu- Recently, computational particle fluid dynamics (CPFD) late a dilute gas–solid reactor, the number density in this was developed by Snider [13] and applied in simulations study was set to 10 000, which corresponds to an injection of gas–solid reactors. CPFD is one of the Discrete Element into the simulated gasifier of 5966 computational particles Methods (DEM) [14] based on the Euler-Lagrange model. per second. Being different from the general DEM, CPFD combines the There are mainly two kinds of inlet boundary conditions particles with the same properties into one particle parcel, (BCs) in CPFD simulation, i.e. flow BCs and injection BCs. i.e. one computational particle [13]. Also, multiphase The injection BCs can be used to simulate the injection particle-in-cell (MP-PIC) is used to simplify the particle– effect of the nozzle with relatively coarser meshes. So, the particle interactions in the CPFD model [13 15 , ]. These two primary channel used this kind of BC to save computing features greatly improve the computing efficiency of CPFD power. The initial speeds for both particles and gas were simulation and enable it to model the 3D gas–solid reactors set to 20 m/s. A particle expansion angle of 40° was used. with numerous discrete particles. It has been successfully However, the actual expansion angle in the simulation is applied in investigating the gas–solid riser [1617 , ] and largely affected by the gas behaviour and it was finally Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 Liang et al | 3 x Primary Secondary Outlet stream stream Fig. 1: The schematic of the gasifier model and CPFD boundary conditions Table 1: The properties of the Utah Bituminous coal [21] Proximate analysis Ash V olatiles Fixed carbon High heating value –1 Moisture (wt. %) (wt. %) (wt. %) (wt. %) (MJ.kg , dry) 2.4 8.3 45.6 43.7 29.8 Elemental analysis C H O N S (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) 71 6 12.7 1.3 0.5 Table 2: The experimental test conditions operated by Brown 2 The numerical model et al. [21] 2.1 The governing equations –1 Primary flow rate, kg.s 0.00729 The governing equations for the CPFD simulations are the Primary particle loading, kg coal/kg primary gas 0.91 general ones described by Snider et al. [13, 15]. Primary stream temperature, K 367 Primary gas composition, mole fraction O 0.85 2 2.2 Chemical reactions Ar 0.126 2.2.1 The devolatilization model H O 0.024 The coal-devolatilization rate is very fast due to the high –1 Secondary flow rate, kg.s 0.00184 temperature in the gasifier [4, 9]. The expression for the Secondary stream temperature, K 450 coal devolatilization [25] is: Secondary gas composition, mole fraction H O 1.0 Coal → a CH + a CO + a CO + a H 1 x 2 3 2 4 2 + a H O + a N + a Char 5 2 6 2 7 (1) Table 3: The particle-size distribution used in this study and where a + a + a + a + a + a + a = 1. 1 2 3 4 5 6 7 the experiment by brown et al. [21] Similarly to the simulation method of devolatilization Weight % Particle-size distribution, μm used by Abani and Ghoniem [9], the inert gas during the devolatilization is neglected in this study and the gas prod- 20% 3 ucts are assumed to be CH, CO, CO , H and H O. 20% 20 2 2 2 2 A one-step model is used to describe the devolatilization 20% 28 rate [26]: 20% 50 Å ã 20% 80 dm E V V (2) = −A exp m v V dt RT where m is the remaining volatile mass in the particle and reshaped to ~20°, as shown in Fig. 4, at 1.018 s in this study. T is the weighted temperature [23] of the particle (0.8) and This angle is consistent with that in a down-fired gasifier 5 –1 the gas (0.2). The activation energy, E , is 2.1 × 10 s [2] and study simulated by Wu et al. [3]. On the other hand, the an- 7 –1 the pre-exponential factor, A , is 3.28 × 10 J.kmol [2]. nular channel was set with the flow BC, as its area is larger. Also, the annular channel was assumed to be square to re- 2.2.2 Heterogenous reaction model duce the number of Cartesian-coordinate-based meshes in After coal devolatilization, the particles mainly consist the CPFD simulation, which can further improve the com- of char and ash. Three main heterogeneous reactions are puting efficiency [24]. Besides, an atmospheric-pressure used to describe the char reaction [9, 27]: boundary was set at the bottom of the gasifier, where both C + 0.5O → CO (R1) gas and particles can leave via this exit. Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 4 | Clean Energy, 2019, Vol. XX, No. XX computation. These models were introduced for this study C + CO → 2CO (R2) and their expressions are listed in Table 5. C + H O → CO + H (R3) 2 2 The studies by Wu et al. [2] and Bi et al. [4] show that the 2.3 The thermal model heterogenous reaction rate is determined by both the in- The general CPFD thermal model [23] was employed in this trinsic chemical rate and the diffusion rate. The expression study. This model includes the models for both gas–solid for the heterogeneous reaction rate in this study is intro- and gas–wall heat transfer. The expressions and their ex- duced from their studies, which is: planations are stated in the literature [23]. Also, the heat R R i,d i,k loss is considerable in the experiment [21] and must be in- R = (3) R + R i,d i,k cluded in the simulation. The wall temperature of the simu- lated gasifier was set as the same value (1100 K) tested by where R is the heterogenous reaction rate for the ith gas Brown et al. [21] using a thermocouple on the gasifier wall with the char particle and R is the diffusion rate, which i,d in their experiment [21]. Besides, the temperature of the can be described as wall adjacent to the primary channel was set to the same 0.75 [ T +T /2] p f (4) value as the feeding-particle temperature. Moreover, the R = C P i,d i i built-in radiation model [23] in Barracuda was also applied 12 –0.75 where C , the diffusion coefficient, is 5  × 10 s.k in this in this study to further improve the simulation results. study; T is the particle temperature; is the g T as tempera- p f ture; and d is the particle diameter. R is the apparent re- p i,k 2.4 The other models action rate and can be described as Å ã Ä ä The drag model used in this study was the Wenyu–Ergun E n R = A exp − P /10 (5) i,k i i correlation. Its expression is described in the literature RT [23]. Besides, the model of large eddy simulation (LES) [36] where n is the order of this reaction model; A is the pre- was applied in this study. Its detailed description is stated exponential factor; E is the activation energy; and P is the i i in the literature [23]. partial pressure for the ith gas. The values [4 2] for e , very parameter in this model are listed in Table 4. 3 Simulation results 2.2.3 Homogeneous reactions 3.1 Validation of the simulated results Based on the same simulation method of Abani and Ghoniem [9] as well as Anderson et al. [31], the hydrocarbons Table 6 shows the comparison between the CPFD- in this study are expressed as CH and the reaction rates of 2 simulation results and the experimental results of Brown CH are adopted for CH. Six main reactions are used to de- 4 2 et  al. [21]. All of the simulation and experimental results scribe the homogeneous reactions in the gasification [27]: were obtained from the central area at a height of 0.23 m above the bottom of the gasifier. The simulation time was CO + 0.5O → CO (R4) 2 2 4 s and the simulation results were averaged results in 4 s. H + 0.5O → H O (R5) 2 2 2 As shown in Table 6, the simulation results agree with the experimental data, which validates the CPFD model estab- CH + 1.5O → CO + H O(R6) 2 2 2 2 lished in this study. CO + H O → CO + H (R7) 2 2 2 CO + H → CO + H O(R8) 2 2 2 3.2 The distributions of gas temperature and velocity in the gasifier CH + H O → CO + 2H(R9) 2 2 2 Yan et al. [32] established a set of reaction models for the Fig. 2a shows the averaged temperature (K) distribution homogeneous reactions of gasification. The expressions in the simulated gasifier. As shown in Fig. 2a, there are of these models are concise, which is beneficial for the Table 5: The expressions for homogeneous reaction rates Table 4: The values used for parameters of the heterogeneous –3 –1 Reactions Reaction rate (kmol.m.s ) reaction rates 0.5 0.5 R4 [33] 1 × 10 exp −15154.25/T C C C . g CO O H O 2 2 A E i i 9 R5 [33] 2.2 × 10 exp −13109.63/T C C g H O –2 –1 –n –1 2 2 Reactions (kg.m .s .Pa ) (J.kmol ) n 0.2 1.3 R6 [34] 2.119 × 10 exp −24379.1/T C C CH O 4 2 R1 [28] 300 1.3 × 10 0.65 R7 [35] 2.5 × 10 exp −16597.5/T C C g CO H O R2 [29] 2224 2.2 × 10 0.6 R8 [35] 9.43 × 10 exp −20563.51/T C C g CO H 2 2 R3 [30] 42.5 1.42 × 10 0.4 R9 [25] 0.312 exp −30000/ 1.987T C g CH 4 Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 Liang et al | 5 Table 6: The data comparison between simulation and experiment CO CO H O H 2 2 2 Data (mol %) (mol %) (mol %) (mol %) Carbon conversion % Outlet gas temperature (K) Simulation 34.1 12.4 28.7 15.3 82.3 1453.2 Experiment [21] 34 16 28 17 82 1350~1400 AB C 4.0000048e+00 O2 0.8 Nodes vect Cells av_Tf mag 0.7 17.5 0.6 0.5 12.5 0.4 0.3 7.5 0.2 0.1 2.5 Fig. 2: The distributions of the time-averaged (t = 0~4.0 s) gas temperature (a), gas speed (b) and oxygen mole fraction (c) in the gasifier two distinct temperature regions in the gasifier: a high- caused by the fast reactions, where the gas temperature temperature region in the upper half of the central gasifier increases sharply from 450 K to >3000 K. The gas expands (red area) and a lower-temperature region near the wall. in both radial and axial directions, with its down-flowing The general temperature difference for these two regions is direction being the most prominent. The central gas speed more than 1000 K. Fig. 2b shows the averaged gas-velocity in this expansion is almost four times that on the edge of (m/s) distribution. As shown in Fig. 2b , there is gas recircu- the expansion. This largely reshapes the particle behaviour lation near the wall at the top of the gasifier. Meanwhile, in the gasifier and will be discussed in Section 4.4. the central gas gradually turns into the plug flow as it flows Fig. 2c shows the averaged oxygen mole fraction dis- downward in the gasifier. These results agree with the re- tribution in the simulated gasifier. The oxygen is highly ports in the literature [4, 27]. concentrated beneath the burner and is consumed quickly As clearly shown in the amplified picture for Fig. 2b , by the fierce oxidation reactions. This forms the high- there exists a rapid gas expansion in the simulated gasifier temperature region shown in Fig. 2a. In other words, the Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 6 | Clean Energy, 2019, Vol. XX, No. XX concentrated oxygen distribution results in the formation Fig. 3b and c show the particle residence time and speed of the high- and low-temperature regions discussed above. distribution at 4.0 s, respectively. As shown in these two The high-temperature region can be seen as an excellent figures, the particles in the central region at the upper- half reaction area, with a sufficient gas reactant of oxygen and gasifier have the lowest residence times due to the highest particles flowing through this region to be consumed rap- downward speed, whereas the particles in the wall region idly. This will be further illustrated in Sections 4.3–4.5. have longer residence times with low speed. The particle- residence times are especially longer for particles en- trained by the gas recirculation, as they first flow upward –1 3.3 The distributions of particle temperature, then downward with speeds below 5 m.s . These results speed, residence time and carbon content can further illustrate the particle carbon-content distribu- tion shown in Fig. 3d. Fig. 3 shows the sectional views of the simulation results for Fig. 3d shows the particle carbon content (mass fraction) particles at 4.0 s. As shown in Fig. 3a, the particle-temperature in the simulated gasifier. As shown in Fig. 3d, the volatile distribution is similar to that for the gas shown in Fig. 2a. The matter was quickly released from the particles. Also, the particle temperatures in the central region are becoming particle carbon contents are generally lower in the cen- lower with particles flowing downward. Meanwhile, the par - tral region and higher in the wall region. The particles in ticles near the wall remain at relatively low temperatures the central region have shorter residence time, but their (1100 K) as they reach a balance of heat transfer to the wall. conversion is higher. This indicates that a long particle- The carbon conversion of the particles is highly related to residence time may not always result in a good particle the temperature and will be discussed later. A Particles Temperat BC Particles ResTime Particles Speed D Particles mf–S–C 2840 4 23 0.84 20.7 0.756 2592.7 3.6 18.4 2345.4 3.2 0.672 2.8 16.1 0.588 2098.1 2.4 13.8 0.504 1850.8 11.5 2 0.42 1603.5 1.6 9.2 0.336 1356.2 1.2 6.9 0.252 1108.9 0.8 4.6 0.168 861.6 0.4 2.3 0.084 614.3 0 0 Fig. 3: The instantaneous distributions of the particle temperature (a), residence time (b), particle speed (d) and carbon content (d) at 4.0 s Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 Liang et al | 7 carbon conversion. The influence of the particle tempera- the particle tracers were first centrally injected from the top ture and the gas-reactant distribution should also be in- of the gasifier then (t = 1.044 s) distributed as an inverted- cluded. For a one-stage down-fired gasifier, the particles cone similar to the gas distribution shown in Fig. 2b . in the central region have more opportunities to achieve a As shown in Fig. 4, at 1.080 s, the axial particle speeds in the higher conversion, as they are not only exposed to a high central region are much faster than in the other regions. temperature, but also get easier access to the gas reactants Meanwhile, the particles closer to the gasifier wall are like oxygen. The detailed particle-reaction process will be turning back and flowing upward after 1.044  s, entrained discussed later by injecting particle tracers. by the gas recirculation shown in Fig. 2b . This indicates that the particle tracers quickly spread in both axial and radial directions due to the rapid gas expansion caused by 3.4 The detailed particle-reaction process the fast reactions shown in Fig. 2b . This rapid gas expan- The detailed particle-reaction process was investigated by sion is the driving force to push one portion of the par - particle tracers. Since the gas–solid flow in the gasifier was ticles into the gas recirculation and keep another portion steady after a simulation time of 1.0 s, the feeding coal par - of the particles with a high down-flowing speed. Also, the ticles from simulation time 1.0 to 1.03  s were marked as carbon contents for particles, flowing directly through the particle tracers in this study. Note that only the particle high-temperature region, are nearly zero, while the carbon identity was changed during this short period of time, contents for particles closer to the wall are still nearly 80%. while the particle properties and flow rate remained the On the other hand, a schematic of the general route for same throughout the entire simulation. The following re- the particles entrained by the gas recirculation is summar - sults were collected from the particle tracers. ized from the simulation results and is shown in Fig. 5. Fig. 4 shows the distribution of carbon contents (mass Note that the particle paths shown in Fig. 5 are in a sequen- fraction) for particle tracers at different times. Note that tial order denoted with numbers from 1 to 5.  As shown only particle tracers can be seen in Fig. 4, which means that in Fig. 5, the recirculated particles cannot enter the high- all the other feeding coal particles coexisting in the gasifier temperature region although they flowed through its edge. –1 were hidden for an easier observation. As shown in Fig. 4, This is because their particle speeds are too small (0.9 m.s Particles mf-S-C 0.85 t = 1.018 s t = 1.044 s t = 1.080 s t = 1.132 s t = 1.198 s t = 1.518 s 0.765 0.68 0.595 0.51 0.425 0.34 0.255 0.17 0.085 Fig. 4: The distribution of particle carbon content for particle tracers at different times Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 8 | Clean Energy, 2019, Vol. XX, No. XX 31 0.25 0.2 0.15 High temperature region 0.1 0.05 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3 Residence time (s) Fig. 6: The probability distribution of the particle-residence time The recirculation zone The spreading zone Fig. 5: The schematic of the general route for particles entrained by the The fast-flowing zone gas recirculation shown in Fig. 4 at t = 1.518 s) while the edge of the central –1 gas speed shown in Fig. 2b is about 10 m.s . These particles instantly flow downward as soon as they approach the edge of the central gas. As a result, no radial momentum is left for them to enter the central high-temperature region; instead, they flow downward along the edge of the central gas. Afterwards, they are pushed towards the wall surface, Fig. 7: The schematic of the three zones for particles in the simulated gasifier driven by the gas expansion, and then flow downward along the wall surface with a temperature of only 1100 K, through the rest of their lifetimes in the simulated gasifier. consists of the fully reacted particles from the central re- Consequently, they have the longest particle-residence gion, whose speeds are much faster due to their much times but the lowest carbon conversions. smaller particle masses and the acceleration of the gas expansion. The ‘long tail’ after 2.0 s shown in Fig. 6 mainly consists 3.5 The probability distribution of the of the particles entrained by the gas recirculation, whose particle-residence time flowing route is much longer. However, their conversion Fig. 6 shows the residence-time probability distribution of is low, since their route provides little access to the high- the particle tracers by counting the particle numbers in temperature region. These particles account for 14.8% of every interval of 0.2 s at the gasifier outlet. As shown in Fig. 6 , all the particles in the gasifier. Besides, the other particles a considerable number (13%) of particle tracers flew out that gradually spread around the entire gasifier contribute of the gasifier during 0.4–0.6 s, forming a shortcut particle to the probability distribution during 0.6–2 s shown in Fig. 6. flow. This result, combined with the results shown in Fig. 4 These particles are the majority, which flow downward at 1.518 s, indicates that the shortcut particle flow mainly with a relatively slow speed and a moderate conversion. Probability distribution Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 Liang et al | 9 Based on the analysis above, the particles in the gasifier Nomenclature can be classified into three zones: a fast-flowing zone, a –2 –1 –n A Pre-exponential factor kg.m.s .Pa recirculation zone and a spreading zone. Fig. 7 shows the A Pre-exponential factor for coal devolatilization, schematic of these three zones for particles in the simu- –1 J.koml lated gasifier. The particle distribution in Fig. 7 is based a Mass fraction on the results shown in Fig. 4 at 1.198  s. As shown in –0.75 C The diffusion coefficient, s.k Fig. 7, the particles with upward axial speed are colored d Particle diameter, m –1 in pink and this is also the criterion for the particles of E Activation energy, J.kmol –1 E Activation energy for coal devolatilization, s the recirculation zone. Its corresponding residence-time m The remaining volatile mass in the particle, kg range is 2.0–3.0  s shown in Fig. 6. On the other hand, n Order of reaction based on the first and distinct peak value shown in Fig. 6 –2 –1 R Heterogenous reaction rate for the ith gas, kg.m .s (0.4–0.6  s), the fast-flowing zone can be identified and P Partial pressure for the ith gas, Pa denoted in Fig. 7. Besides, the rest of the particles in the T Weighted temperature, K gasifier can be classified as the spread zone (0.6~2.0  s T Fluid temperature, K shown in Fig. 6). T Particle temperature, K t Time, s Conflict of Interest 4 Conclusion None declared. A CPFD-simulation model for entrained-flow gasification was established in this study. The simulation results agree References with the experimental data. The detailed particle infor - [1] Ma  J, Zitney  SE. Computational fluid dynamic modeling of mation was obtained. The results show that the particles entrained-flow gasifiers with improved physical and chem- in the gasifier can be classified into three zones, i.e. a fast- ical submodels. Energ Fuel 2012; 26:7195–219. flowing zone, a recirculation zone and a spreading zone. [2] Wu Y, Zhang J, Smith, et al. Three-dimensional simulation for an The criterion for this classification was also provided. entrained flow coal slurry gasifier. Energ Fuel 2010; 24:1156–63. The gas expansion plays a significant role in forming [3] Wu  Y, Zhang  J, Wang  M, et  al. 3D numerical simulation of Texaco gasifier using assumed PDF model. Journal of Chemical the particle stream into these three zones. It accelerates Industry and Engineering (China) 2007; 58:2369–74. the axial speed of particles in the centre of the gasifier [4] Bi  D, Guan  Q, Xuan  W, et  al. Numerical simulation of GSP while it pushes another portion of particles into the gas gasifier based on double-mixture fractions PDF model. Journal recirculation. of Chemical Industry and Engineering (China) 2014; 65:3753–9 Also, the concentrated oxygen distribution in the gasifier [5] Chen  C, Hung  C, Chen  W. Numerical investigation on per - results in the formation of high- and low-temperature re- formance of coal gasification under various injection patterns gions, and particles flowing through the high-temperature in an entrained flow gasifier. Appl Energ 2012; 100:218–28. [6] Vascellari M, Arora R, Pollack M, et al. Simulation of entrained region will have sufficient time to react with the oxygen. flow gasification with advanced coal conversion submodels. Particles in the fast-flowing zone flow directly through the Part 1: Pyrolysis. Fuel 2013; 113:654–69. high-temperature region with a high speed. Most of the [7] Vascellari  M, Arora  R, Hasse  C. Simulation of entrained flow particles in this zone are fully reacted, although their resi- gasification with advanced coal conversion submodels. Part dence times are very short. While particles in the recircula- 2: Char conversion. Fuel 2014; 118:369–84. tion part have little access to the high-temperature region [8] Richter  A, Vascellari  M, Nikrityuk  PA, et  al. Detailed analysis and most of the particles in this zone still have high carbon of reacting particles in an entrained-flow gasifier. Fuel Process Technol 2016; 144:95–108. content, their residence times are very long. The particles [9] Abani G, Ahmed F. Large eddy simulations of coal gasification in the spreading zone gradually spread throughout the en- in an entrained flow gasifier. Fuel 2013; 104:664–80. tire gasifier with a moderate residence time, temperature [10] Kumar M, Ghoniem AF. Multiphysics simulations of entrained and carbon conversion. flow gasification. Part I: Validating the nonreacting flow solver The detailed particle behaviour obtained in this study and the particle turbulent dispersion model. Energ Fuel 2011; can deepen our understanding of the complex reac- 26:451–63. tion process in the gasifier and provide a scientific basis [11] Kumar M, Ghoniem AF. Multiphysics simulations of entrained flow gasification. Part II: Constructing and validating the for better gasifier design. Usually, the geometrical model overall model. Energ Fuel 2012; 26:464–79. of a simulated gasifier needs to be simplified due to the [12] Kumar M, Ghoniem AF. Application of a validated gasification computing-power constraint. However, some of the non- model to determine the impact of coal particle grinding size symmetrically shaped gasifiers are not suitable to be on carbon conversion. Fuel 2013; 108:565–77. simplified as half-size or 2D geometries. So, CPFD provides [13] Snider DM. An incompressible three-dimensional multiphase a promising way to simulate those gasifiers with compli- particle-in-cell model for dense particle flows. J Comput Phys cated geometries. 2001; 170:523–49. Downloaded from https://academic.oup.com/ce/advance-article-abstract/doi/10.1093/ce/zkz032/5722363 by guest on 18 February 2020 10 | Clean Energy, 2019, Vol. XX, No. XX [14] Deen  NG, Annaland  MVS, Hoef  MAVD, et  al. Review of dis- [25] Wen CY, Chaung TZ. Entrainment coal gasification modeling. crete particle modeling of fluidized beds. Chem Eng Sci 2007; Ind Eng Process Des Dev 1979; 18:684–95. 62:28–44. [26] Badzioch  S, Hawksley  PGW, Badzioch  S, et  al. Kinetics of [15] Snider  DM, Clark  SM, O’Rourke  PJ. Eulerian–Lagrangian thermal decomposition of pulverized coal particles. Ind Eng method for three-dimensional thermal reacting flow with ap- Chem Process Des Dev 1970; 9:684–95. plication to coal gasifiers. Chem Eng Sci 2011; 66:1285–95. [27] Sun  Z, Dai  Z, Zhou  Z, et  al. Numerical simulation of indus- [16] Chen  C, Werther  J, Heinrich  S, et  al. CPFD simulation trial opposed multiburner coal–water slurry entrained flow of circulating fluidized bed risers. Powder Technol 2013; gasifier. Ind Eng Chem Res 2012; 51:2560–9. 235:238–47. [28] Monson CR, Germane GJ, Blackham AU, et al. Char oxidation [17] Wang  Q, Niemi  T, Peltola  J, et  al. Particle size distribution in at elevated pressures. Combustion & Flame 1995; 100:669–83. CPFD modeling of gas-solid flows in a CFB riser. Particuology [29] Roberts DG, Harris DJ. Char gasification with O, CO , and H O: 2 2 2 2015; 21:107–17. effects of pressure on intrinsic reaction kinetics. Energ Fuel [18] Abbasi A, Islam MA, Ege PE, et al. CPFD flow pattern simula- 2000; 14:483–9. tion in downer reactors. Aiche J 2013; 59:1635–47. [30] Fang W. Study on reaction kinetic of steam-coal chars gasifi- [19] Lanza  A, Islam  MA, De  Lasa  H. CPFD modeling and experi- cation with TGA.J ournal of China Coal Society 2004; 29:350–3. mental validation of gas–solid flow in a down flow reactor. [31] Andersen J, Rasmussen CL, Giselsson T, et al. Global combus- Comput Chem Eng 2016; 90:79–93. tion mechanisms for use in cfd modeling under oxy-fuel con- [20] Kraft  S, Kirnbauer  F, Hofbauer  H. CPFD simulations of an ditions. Energ Fuel 2009; 23:1379–89. industrial-sized dual fluidized bed steam gasification [32] Yan L, Lim JC, Yue G, et al. Simulation of biomass-steam gas- system of biomass with 8MW fuel input. Appl Energ 2017; ification in fluidized bed reactors: model setup, compari- 190:408–20. sons and preliminary predictions. Bioresour Technol 2016; [21] Brown BW, Smoot LD, Smith PJ, et al. Measurement and pre- 221:625–35. diction of entrained‐flow gasification processes. Aiche J 1988; [33] Gómez-Barea A, Leckner B. Modeling of biomass gasification 34:435–46. in fluidized bed. Progress in Energy & Combustion Science 2010; [22] Liang Y, Zhang Y, Li T, et al. A critical validation study on CPFD 36:444–509. model in simulating gas–solid bubbling fluidized beds. Powder [34] Inc. F. Fluent 6.3-User’s Guide. New York: Fluent Inc., 2006. Technol 2014; 263:121–34. [35] Umeki  K, Yamamoto  K, Namioka  T, et  al. High temperature [23] Cpfd Software. Barracuda Virtual Reactor User Manual. steam-only gasification of woody biomass. Appl Energ 2010; Albuquerque: CPFD Software, LLC, 2016. 87:791–8. [24] Liang Y, Zhang Y, Lu C. CPFD simulation on wear mechanisms [36] Smagorinsky  JS. General circulation experiments with the in disk–donut FCC strippers. Powder Technol 2015; 279:269–81. primitive equations. Mon Weather Rev 1963; 91:99–164.

Journal

Clean EnergyOxford University Press

Published: Apr 4, 2020

References