Numerical simulation study on influence of mesoscale structure in riser reactor

doi:10.11949/0438-1157.20220399

Abstract

Abstract:

There exists typical mesoscale structure of particle clusters in riser reactor, and its distribution characteristics have important impacts on gas-solid flow and reaction characteristics. The analysis of the influence of mesoscale structure is helpful to provide basic information for reactor design and optimal operation. In this paper, the gas-solid flow model in the riser is established by using the drag model based on the energy-minimization multi-scale (EMMS) method, and the influence of particle cluster on the momentum transfer between gas and solid is therefore described. In addition, by considering the existence of particle clusters and the influence of the heterogeneity of particle clusters on the chemical reaction, a correction factor describing the influence of mesoscale structure on the reaction rate is proposed, which is coupled with the gas-solid flow model. A combined flow-reaction mathematical model based on mesoscale structure is therefore established. The model is validated against the experimental data. The model was further applied to simulate and analyze the flow-reaction characteristics of an industrial catalytic cracking riser reactor. The results show that the model can reasonably describe the gas-solid interaction in the riser and can predict the distribution characteristics of mesoscale structures near the wall. Due to the existence of clusters, it is difficult for the heavy oil components to fully contact with the catalyst. The distribution characteristics of clusters can lead to the high concentration of heavy oil and low concentration of gasoline and diesel near the wall. Due to the flow resistance in the cluster, excessive secondary reactions of gasoline and diesel occur in the cluster to produce more coke, resulting in higher coke concentration near the wall. Compared with the traditional model based on averaging method, the optimal value of gasoline yield predicted by the present model is closer to the industrial practice. Therefore, the present model can reasonably describe the coupled flow-reaction characteristics in the riser, reveal the influence of mesoscale structure, and is promising in providing important basic information for the development of reaction terminator technology in industrial risers.

Key words: particle cluster, riser, mesoscale model, fluid catalytic cracking, numerical simulation

摘要：

提升管反应器存在典型的颗粒聚团介尺度结构，其分布特性对气固流动、反应有重要影响，对介尺度结构影响规律进行分析有助于为反应器的设计与优化操作提供基础信息。采用基于能量最小多尺度（EMMS）方法的曳力模型建立了提升管气固两相流动模型，考虑了颗粒聚团对气固相间动量传递的影响。此外，进一步通过考虑颗粒聚团的存在以及颗粒聚团的非均匀性对化学反应的影响，提出了描述介尺度结构对反应速率影响的修正因子，与气固流动模型进行耦合，建立了基于介尺度结构的流动-反应综合数学模型，并进行了模型验证。进一步应用该模型，对工业催化裂化提升管反应器的流动-反应特性进行了模拟分析。结果表明，该模型可以合理描述提升管气固相互作用，能够预测出壁面附近存在较多介尺度结构的分布特性，由于聚团的存在使得重油组分难以与催化剂充分接触，生成汽柴油的反应速率较低，转化较慢，聚团的分布特性导致靠近边壁处的重油组分浓度较高，汽柴油组分浓度较低；汽柴油在聚团内部的流动阻力较大，在聚团内发生过量的二次反应生成较多焦炭，导致壁面处焦炭浓度较高。与传统基于平均化而未考虑聚团影响的模型相比，基于介尺度结构的模型所预测的汽油收率最佳值与工业实际相接近。因此，基于介尺度结构的流动-反应综合数学模型可以合理描述提升管内进行的流动-反应耦合特性，并能揭示介尺度结构对催化裂化反应过程的影响，有望为工业提升管装置反应终止剂技术的开发提供重要的基础信息。

关键词: 颗粒聚团, 提升管, 介尺度模型, 流化催化裂化, 数值模拟

CLC Number:

Xiaogang SHI, Chengxiu WANG, Jinsen GAO, Xingying LAN. Numerical simulation study on influence of mesoscale structure in riser reactor[J]. CIESC Journal, 2022, 73(6): 2708-2721.

石孝刚, 王成秀, 高金森, 蓝兴英. 提升管反应器介尺度结构影响规律的数值模拟研究[J]. 化工学报, 2022, 73(6): 2708-2721.

Figures/Tables 19

Table 1 EMMS model equations

项目	方程表达式
曳力模型系数β	$β = 34 C d ε s ε g ρ g u g - u s d p ε g - 2.65 H D ???????????? ε g ≥ 0.4 150 ε s 2 μ g ε g d p 2 + 1.75 ε s ρ g u g - u s d p ???????? ε g < 0.4$
曳力系数修正因子	$H D = A e R e + B e C e$
ε_g>0.997	$A e = 1 B e = 1 C e = 0$
0.997>ε_g>0.99	$A e = 0.4243 + 0.88 1 - 1 1 + e x p 0.9989 - ε g 3 × 10 - 5 1 + e x p 0.9942 - ε g 2.18 × 10 - 3 B e = 1.661 × 10 - 3 + 0.2436 e x p - 0.5 0.9985 - ε g 1.91 × 10 - 3 2 C e = 8.25 × 10 - 2 - 0.0574 e x p - 0.5 0.9979 - ε g 7.03 × 10 - 3 2$
0.99>ε_g>0.545	$A e = 49.1698 - 49.5722 ε g - 0.4896 B e = 137.6308 - 21.6308 ε g 13.031 C e = ε g - 1.0013 - 6.633 × 10 - 2 + 9.1391 ε g - 1.0013 + 6.9231 ε g - 1.0013 2$
0.545>ε_g>0.4	$A e = 0.8526 - 0.5846 1 + ε g 0.4325 22.6279 B e = 1 C e = 0$

Table 1 EMMS model equations

项目	方程表达式
曳力模型系数β	$β = 34 C d ε s ε g ρ g u g - u s d p ε g - 2.65 H D ???????????? ε g ≥ 0.4 150 ε s 2 μ g ε g d p 2 + 1.75 ε s ρ g u g - u s d p ???????? ε g < 0.4$
曳力系数修正因子	$H D = A e R e + B e C e$
ε_g>0.997	$A e = 1 B e = 1 C e = 0$
0.997>ε_g>0.99	$A e = 0.4243 + 0.88 1 - 1 1 + e x p 0.9989 - ε g 3 × 10 - 5 1 + e x p 0.9942 - ε g 2.18 × 10 - 3 B e = 1.661 × 10 - 3 + 0.2436 e x p - 0.5 0.9985 - ε g 1.91 × 10 - 3 2 C e = 8.25 × 10 - 2 - 0.0574 e x p - 0.5 0.9979 - ε g 7.03 × 10 - 3 2$
0.99>ε_g>0.545	$A e = 49.1698 - 49.5722 ε g - 0.4896 B e = 137.6308 - 21.6308 ε g 13.031 C e = ε g - 1.0013 - 6.633 × 10 - 2 + 9.1391 ε g - 1.0013 + 6.9231 ε g - 1.0013 2$
0.545>ε_g>0.4	$A e = 0.8526 - 0.5846 1 + ε g 0.4325 22.6279 B e = 1 C e = 0$

Table 2 Ratio of reaction rate on cluster to that on single particle

空隙率	气速/(m/s)	短轴	长短轴比	倾角/(°)	$H c$
空隙率	气速/(m/s)	短轴	长短轴比	倾角/(°)	一次反应H_c,1	二次反应H_c,2
0.83	0.1	0.67	1	0	0.1560	0.8900
0.83	0.5	0.67	1	0	0.1930	0.9589
0.83	1	0.67	1	0	0.2107	0.9937
0.83	1.5	0.67	1	0	0.2228	1.0165
0.83	2	0.67	1	0	0.2325	1.0339
0.83	2.5	0.67	1	0	0.2409	1.0484
0.83	3	0.67	1	0	0.2485	1.0609
0.90	5	0.5	1	0	0.4511	1.1105
0.90	7.5	0.5	1	0	0.5242	1.1119
0.90	10	0.5	1	0	0.5828	1.1073
0.96	0.5	0.5	1	0	0.5638	1.0498
0.96	1	0.5	1	0	0.6299	1.0522
…	…	…	…	…	…	…

Table 2 Ratio of reaction rate on cluster to that on single particle

空隙率	气速/(m/s)	短轴	长短轴比	倾角/(°)	$H c$
空隙率	气速/(m/s)	短轴	长短轴比	倾角/(°)	一次反应H_c,1	二次反应H_c,2
0.83	0.1	0.67	1	0	0.1560	0.8900
0.83	0.5	0.67	1	0	0.1930	0.9589
0.83	1	0.67	1	0	0.2107	0.9937
0.83	1.5	0.67	1	0	0.2228	1.0165
0.83	2	0.67	1	0	0.2325	1.0339
0.83	2.5	0.67	1	0	0.2409	1.0484
0.83	3	0.67	1	0	0.2485	1.0609
0.90	5	0.5	1	0	0.4511	1.1105
0.90	7.5	0.5	1	0	0.5242	1.1119
0.90	10	0.5	1	0	0.5828	1.1073
0.96	0.5	0.5	1	0	0.5638	1.0498
0.96	1	0.5	1	0	0.6299	1.0522
…	…	…	…	…	…	…

Table 3 Correlation for influence factor of heterogeneous cluster on reaction rate［19］

H_r表达式	ε_g范围
$H r = 4.5484 - 8.8775 ε g$	[0.4,0.4162]
$H r = - 42.3887 + 203.6538 ε g - 239.6766 ε g 2$	[0.4162,0.4257]
$H r = - 34.6239 + 219.6173 ε g - 446.5862 ε g 2 + ?????? 297.1701 ε g 3$	[0.4257,0.5457]
$H r = 0.3667 - 0.04321 ε g + 0.6543 ε g 2$	[0.5457,1.0]

Table 3 Correlation for influence factor of heterogeneous cluster on reaction rate［19］

H_r表达式	ε_g范围
$H r = 4.5484 - 8.8775 ε g$	[0.4,0.4162]
$H r = - 42.3887 + 203.6538 ε g - 239.6766 ε g 2$	[0.4162,0.4257]
$H r = - 34.6239 + 219.6173 ε g - 446.5862 ε g 2 + ?????? 297.1701 ε g 3$	[0.4257,0.5457]
$H r = 0.3667 - 0.04321 ε g + 0.6543 ε g 2$	[0.5457,1.0]

Fig.1 Schematic diagram (a) and geometry and mesh (b) of simulation set-up

Table 4 Size of simulation set-up and properties of materials

参数	数值
提升管直径D/m	0.076
提升管高度h/m	10
颗粒粒径d_p/μm	76
颗粒密度ρ_p/(kg/m³)	1780
气体密度ρ_g/(kg/m³)	1.1795

Fig.2 Axial distribution of ozone concentration in riser (Ug=9 m/s, dash line is for homogeneous model while solid line for mesoscale model)

Fig.3 Radial distribution of ozone concentration in riser(Ug=9 m/s, dash line is for homogeneous model while solid line for mesoscale model)

Fig.4 Industrial FCC riser and its geometry

Table 5 Simulation parameter for industrial FCC riser

参数	数值
原料油质量流率/(t/h)	152
雾化蒸汽质量流率/(t/h)	9.6
预提升蒸汽质量流率/(kg/h)	4070
催化剂循环量/(t/h)	1156
剂油比	7
物料混合温度/℃	550

Fig.5 14-lump kinetic network for FCC reactions

Fig.6 Axial distribution of catalyst particle in the riser

Fig.7 Instantaneous distribution of particle concentration in FCC riser

Fig.8 Average temperature of oil gas and instantaneous distribution of particle temperature in FCC riser

Fig.9 Conversion along the height in riser

Fig.10 Distribution of heavy oil concentration

Fig.11 Distribution of gasoline concentration

Fig.12 Distribution of diesel concentration

Fig.13 Distribution of coke concentration

Fig.14 Yield of different components along riser height (solid line for mesoscale model, and dash line for homogeneous model)

References 28

1	王成秀, 裴华健, 苏鑫, 等. 密相提升管内颗粒速度与颗粒浓度分布及发展特性[J]. 化学反应工程与工艺, 2020, 36(1): 8-16.
	Wang C X, Pei H J, Su X, et al. Particle velocity and solids holdup characteristics and the flow development in a high density circulating fluidized bed riser[J]. Chemical Reaction Engineering and Technology, 2020, 36(1): 8-16.
2	Zhang H, Huang W X, Zhu J X. Gas-solids flow behavior: CFB riser vs. downer[J]. AIChE Journal, 2001, 47(9): 2000-2011.
3	Manyele S V, Pärssinen J H, Zhu J X. Characterizing particle aggregates in a high-density and high-flux CFB riser[J]. Chemical Engineering Journal, 2002, 88(1/2/3): 151-161.
4	Wang J W, Ge W, Li J H. Eulerian simulation of heterogeneous gas-solid flows in CFB risers: EMMS-based sub-grid scale model with a revised cluster description[J]. Chemical Engineering Science, 2008, 63(6): 1553-1571.
5	Rossbach V, Padoin N, Meier H F, et al. Influence of ultrasonic waves on the gas-solid flow and the solids dispersion in a CFB riser: numerical and experimental study[J]. Powder Technology, 2021, 389: 430-449.
6	Jiradilok V, Gidaspow D, Damronglerd S, et al. Kinetic theory based CFD simulation of turbulent fluidization of FCC particles in a riser[J]. Chemical Engineering Science, 2006, 61(17): 5544-5559.
7	Yin L J, Wang S Y, Lu H L, et al. Simulation of effect of catalyst particle cluster on dry methane reforming in circulating fluidized beds[J]. Chemical Engineering Journal, 2007, 131(1/2/3): 123-134.
8	Wang S Y, Yin L J, Lu H L, et al. Simulation of effect of catalytic particle clustering on methane steam reforming in a circulating fluidized bed reformer[J]. Chemical Engineering Journal, 2008, 139(1): 136-146.
9	吕林英, 蓝兴英, 吴迎亚, 等. FCC提升管反应器中颗粒聚团对裂化反应的影响[J]. 化工学报, 2015, 66(8): 2920-2928.
	Lyu L Y, Lan X Y, Wu Y Y, et al. Effect of particles cluster on behavior of catalytic cracking reaction in FCC riser[J]. CIESC Journal, 2015, 66(8): 2920-2928.
10	Li J H, Huang W L, Chen J H. Possible roadmap to advancing the knowledge system and tackling challenges from complexity[J]. Chemical Engineering Science, 2021, 237: 116548.
11	Shi Z S, Wang W, Li J H. A bubble-based EMMS model for gas-solid bubbling fluidization[J]. Chemical Engineering Science, 2011, 66(22): 5541-5555.
12	Guo L, Wu J, Li J H. Complexity at mesoscales: a common challenge in developing artificial intelligence[J]. Engineering, 2019, 5(5): 241-253.
13	Cloete S, Amini S, Johansen S T. On the effect of cluster resolution in riser flows on momentum and reaction kinetic interaction[J]. Powder Technology, 2011, 210(1): 6-17.
14	Chalermsinsuwan B, Piumsomboon P, Gidaspow D. Kinetic theory based computation of PSRI riser(Ⅰ): Estimate of mass transfer coefficient[J]. Chemical Engineering Science, 2009, 64(6): 1195-1211.
15	Chalermsinsuwan B, Piumsomboon P, Gidaspow D. Kinetic theory based computation of PSRI riser(Ⅱ): Computation of mass transfer coefficient with chemical reaction[J]. Chemical Engineering Science, 2009, 64(6): 1212-1222.
16	Holloway W, Sundaresan S. Filtered models for reacting gas-particle flows[J]. Chemical Engineering Science, 2012, 82: 132-143.
17	Dong W G, Wang W, Li J H. A multiscale mass transfer model for gas-solid riser flows(Ⅰ):Sub-grid model and simple tests[J]. Chemical Engineering Science, 2008, 63(10): 2798-2810.
18	Hong K, Shi Z S, Wang W, et al. A structure-dependent multi-fluid model (SFM) for heterogeneous gas-solid flow[J]. Chemical Engineering Science, 2013, 99: 191-202.
19	Liu C F, Wang W, Zhang N, et al. Structure-dependent multi-fluid model for mass transfer and reactions in gas-solid fluidized beds[J]. Chemical Engineering Science, 2015, 122: 114-129.
20	Huang Z Q, Wang L X, Zhou Q. Development of a filtered reaction rate model for reactive gas-solid flows based on fine-grid simulations[J]. AIChE Journal, 2021, 67(5): e17185.
21	Zou Z, Yan D, Zhu J Y, et al. Simulation of the fluid–solid noncatalytic reaction based on the structure-based mass-transfer model: shrinking core reaction[J]. Industrial & Engineering Chemistry Research, 2020, 59(40): 17729-17739.
22	Wu Y Y, Peng L, Qin L Q, et al. Validation and application of CPFD models in simulating hydrodynamics and reactions in riser reactor with Geldart A particles[J]. Powder Technology, 2018, 323: 269-283.
23	Wu Y Y, Shi X G, Wang C X, et al. CPFD simulation of hydrodynamics, heat transfer, and reactions in a downer reactor for coal pyrolysis with binary particles[J]. Energy & Fuels, 2019, 33(12): 12295-12307.
24	Wang M, Lan X Y, Wang C X, et al. Numerical simulation of the pilot-scale high-density circulating fluidized bed riser[J]. Industrial & Engineering Chemistry Research, 2021, 60(7): 3184-3197.
25	Shi X G, Wu Y Y, Wang M, et al. Physicochemical processes occurring inside clusters consisting of FCC catalyst particles[J]. Chemical Engineering & Technology, 2017, 40(5): 847-853.
26	Wang C. High density gas-solids circulating fluidized bed riser and downer reactors[D]. Ontario :The University of Western Ontario, 2013.
27	Gidaspow D. Multiphase Flow and Fluidization: Continuum and Kinetic Theory Descriptions[M]. USA: Academic Press, 1994.
28	Lan X Y, Xu C M, Wang G, et al. CFD modeling of gas-solid flow and cracking reaction in two-stage riser FCC reactors[J]. Chemical Engineering Science, 2009, 64(17): 3847-3858.

[1]	Zhanyu YE, He SHAN, Zhenyuan XU. Performance simulation of paper folding-like evaporator for solar evaporation systems [J]. CIESC Journal, 2023, 74(S1): 132-140.
[2]	Yifei ZHANG, Fangchen LIU, Shuangxing ZHANG, Wenjing DU. Performance analysis of printed circuit heat exchanger for supercritical carbon dioxide [J]. CIESC Journal, 2023, 74(S1): 183-190.
[3]	Zhiguo WANG, Meng XUE, Yushuang DONG, Tianzhen ZHANG, Xiaokai QIN, Qiang HAN. Numerical simulation and analysis of geothermal rock mass heat flow coupling based on fracture roughness characterization method [J]. CIESC Journal, 2023, 74(S1): 223-234.
[4]	Jiahao SONG, Wen WANG. Study on coupling operation characteristics of Stirling engine and high temperature heat pipe [J]. CIESC Journal, 2023, 74(S1): 287-294.
[5]	Siyu ZHANG, Yonggao YIN, Pengqi JIA, Wei YE. Study on seasonal thermal energy storage characteristics of double U-shaped buried pipe group [J]. CIESC Journal, 2023, 74(S1): 295-301.
[6]	Song HE, Qiaomai LIU, Guangshuo XIE, Simin WANG, Juan XIAO. Two-phase flow simulation and surrogate-assisted optimization of gas film drag reduction in high-concentration coal-water slurry pipeline [J]. CIESC Journal, 2023, 74(9): 3766-3774.
[7]	Lei XING, Chunyu MIAO, Minghu JIANG, Lixin ZHAO, Xinya LI. Optimal design and performance analysis of downhole micro gas-liquid hydrocyclone [J]. CIESC Journal, 2023, 74(8): 3394-3406.
[8]	Chen HAN, Youmin SITU, Bin ZHU, Jianliang XU, Xiaolei GUO, Haifeng LIU. Study of reaction and flow characteristics in multi-nozzle pulverized coal gasifier with co-processing of wastewater [J]. CIESC Journal, 2023, 74(8): 3266-3278.
[9]	Xiaosong CHENG, Yonggao YIN, Chunwen CHE. Performance comparison of different working pairs on a liquid desiccant dehumidification system with vacuum regeneration [J]. CIESC Journal, 2023, 74(8): 3494-3501.
[10]	Wenzhu LIU, Heming YUN, Baoxue WANG, Mingzhe HU, Chonglong ZHONG. Research on topology optimization of microchannel based on field synergy and entransy dissipation [J]. CIESC Journal, 2023, 74(8): 3329-3341.
[11]	Rui HONG, Baoqiang YUAN, Wenjing DU. Analysis on mechanism of heat transfer deterioration of supercritical carbon dioxide in vertical upward tube [J]. CIESC Journal, 2023, 74(8): 3309-3319.
[12]	Kexin HUANG, Tong LI, Anqi LI, Mei LIN. Mode decomposition of flow field in T-junction with rotating impeller [J]. CIESC Journal, 2023, 74(7): 2848-2857.
[13]	Fangzhe SHI, Yunhua GAN. Numerical simulation of start-up characteristics and heat transfer performance of ultra-thin heat pipe [J]. CIESC Journal, 2023, 74(7): 2814-2823.
[14]	Xingchi ZHU, Zhiyuan GUO, Zhiyong JI, Jing WANG, Panpan ZHANG, Jie LIU, Yingying ZHAO, Junsheng YUAN. Simulation and optimization of selective electrodialysis magnesium and lithium separation process [J]. CIESC Journal, 2023, 74(6): 2477-2485.
[15]	Zhihang ZHENG, Junnan MA, Zihan YAN, Chunxi LU. Study on the pressure pulsation characteristics in jet influence zone of riser [J]. CIESC Journal, 2023, 74(6): 2335-2350.