CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 607-616.doi: 10.11949/j.issn.0438-1157.20181343

• Process system engineering • Previous Articles     Next Articles

Improved state transfer algorithm-based kinetics parameter estimation for cascaded plug flow reactors

Yongfei XUE1(),Yalin WANG1(),Bei SUN1,Qianzhong LI2,Jiazhou SUN1   

  1. 1. School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, China
    2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2018-11-15 Revised:2018-11-21 Online:2019-02-05 Published:2018-12-04
  • Contact: Yalin WANG E-mail:xueyongfei@csu.edu.cn;ylwang@csu.edu.cn

Abstract:

The actual chemical process reaction system usually consists of several interconnected cascaded reactor. To establish its mechanism model and estimate its dynamic parameters, it is necessary to repeatedly solve large-scale nonlinear differential equations. The calculation is very expensive. Since this parameter optimization process involves solving large sets of differential equations, it is very time consuming. According to the problem that intelligent optimization algorithm always needs enormous computing resource while a satisfied solution is acceptable for an industrial application, an improved state transfer algorithm (STA) is proposed to estimate the kinetics parameters of the cascaded plug flow reactors model. This method uses an opposite operator to initialize the start status of STA, and a tolerable error threshold to break the optimization process. This improvement is helpful since much computing time is saved while the global search capability and fast convergence capability of standard STA are retained. Simulation study, whose target is optimization the kinetics parameters of the cascaded plug flow reactors of an industrial hydrocracking process, verified the effectiveness and superiority of this proposed method.

Key words: cascaded plug flow reactors, reaction kinetics, kinetic modeling, parameter estimation, state transfer algorithm, expensive optimization, satisfied solution

CLC Number: 

  • TP 273

Fig.1

Diagram of plug flow reactor for modeling"

Fig.2

Cascaded plug flow reactors"

Fig.3

Optimization result of Michalewicz function by standard STA"

Fig.4

Kinetics parameters estimation of cascaded plug flow reactors based on improved STA"

Fig.5

Reaction network of six lumped components in four hydrocracking reactors"

Table 1

Physical property data used for estimating kinetics parameters of hydrocracking"

物性名称本文取值
第1反应器中混合物的比定压热容cˉ1,13.14 kJ?kg-1?K-1
第2反应器中混合物的比定压热容cˉ1,22.48 kJ?kg-1?K-1
第3反应器中混合物的比定压热容cˉ1,32.47 kJ?kg-1?K-1
第4反应器中混合物的比定压热容cˉ1,42.26 kJ?kg-1?K-1
反应器级间冷氢的比定压热容cH214.32 kJ?kg-1?K-1
加氢裂化反应平均放热系数Hr418.4 kJ?kg-1
第1反应器中混合物的密度ρ1905 kg·m-3
第2反应器中混合物的密度ρ2833 kg·m-3
第3反应器中混合物的密度ρ3809.8 kg·m-3
第4反应器中混合物的密度ρ4737.1 kg·m-3

Fig.6

Fitness function of improving STA"

Table 2

Estimation results of kinetics parameters for hydrocracking reaction"

反应速率常数活化能Ei/(J·mol-1)指前因子ki0/s-1
k1

95700.39

95700.39

95700.39

95700.39

95700.39

95700.39

4585.95
k22383.74
k32373.56
k4417.33
k539.32
k6

97199.38

97199.38

97199.38

97199.38

473.33
k796.48
k82.65×10-5
k90
k10

63753.57

63753.57

63753.57

0.20
k110
k129.92×10-3
k13

97292.32

97292.32

44.70
k140
k158018.3611.70×10-3

"

项目标准STA

改进STA

(平均值)

性能提升

(平均值)/%

寻优迭代次数20041.779.15
计算总耗时/s30477.126215.6079.61
fitness调用耗时/s30378.396108.0679.89
ode15 s调用耗时/s30416.736199.9779.62

Table 4

Parameters setting of improved STA"

参数名称取值参数名称取值
伸缩算子γ1迭代次数iter2.0×102
旋转算子α(1/2)iterαmax误差满意阈值ξ3.0×10-2
旋转算子初值αmax1活化能Ei上限1.3×105
旋转算子终值αmin1.0×10-3活化能Ei下限6.0×103
轴向算子δ1指前因子ki0上限7.0×103
平移算子β1指前因子ki0下限0
状态个数SE30所求问题状态维数20

Fig.7

Prediction results on mass fraction of some key components at outlet of hydrocracking reactor"

Fig.8

Prediction results of outlet temperature for cascaded hydrocracking reactors"

Table 5

Statistical error of 16 hydrocracking testing samples which are adjacent"

预测项目标准STA改进STA
MAEMREMAEMRE
尾油质量分数0.0019481.0627%0.0024121.3155%
柴油质量分数0.0011880.3069%0.0015100.3899%
航煤质量分数0.0002200.1089%0.0003060.1513%
重石质量分数0.0004550.2386%0.0006290.3302%
轻石质量分数0.0002601.6356%0.0003392.1251%
轻端质量分数0.0002801.3670%0.0003781.8609%
1反出口温度0.0765430.0113%0.1118870.0166%
2反出口温度0.1067600.0158%0.1394210.0207%
3反出口温度0.1096460.0162%0.1434360.0212%
4反出口温度0.0984960.0146%0.1224680.0182%
1 SunB, GuiW H, WuT B, et al. An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential[J]. Hydrometallurgy, 2013, 140: 102-110.
2 FuY, LiuX G. Nonlinear wave modeling and dynamic analysis of high-purity heat integrated air separation column[J]. Separation and Purification Technology, 2015, 151: 14-22.
3 SildirH, ArkunY, CakalB, et al. A dynamic non-isothermal model for a hydrocracking reactor: model development by the method of continuous lumping and application to an industrial unit[J]. Journal of Process Control, 2012, 22(10): 1956-1965.
4 SunB, GuiW H, WangY L, et al. A gradient optimization scheme for solution purification process[J]. Control Engineering Practice, 2015, 44: 89-103.
5 SunB, GuiW H, WangY L, et al. Intelligent optimal setting control of a cobalt removal process[J]. Journal of Process Control, 2014, 24(5): 586-599.
6 SildirH, ArkunY, CakalB, et al. Plant-wide hierarchical optimization and control of an industrial hydrocracking process[J]. Journal of Process Control, 2013, 23(9): 1229-1240.
7 LiY G, GuiW H, TeoK L, et al. Optimal control for zinc solution purification based on interacting CSTR models[J]. Journal of Process Control, 2012, 22(10): 1878-1889.
8 李勇刚, 马蕾, 伍铁斌, 等. 多反应器级联的沉铁过程pH优化控制 [J]. 化工学报, 2018, 69(6): 2586-2593.
LiY G, MaL, WuT B, et al. pH optimization and control in iron removal process of multi-reactor cascade[J]. CIESC Journal, 2018, 69(6): 2586-2593.
9 BrooksK. Steady-state multiplicity in an adiabatic continuous stirred tank reactor with vapor recycle[J]. AIChE Journal, 2013, 59(2): 553-559.
10 许瑜飞, 钱锋, 杨明磊, 等. 改进鲸鱼优化算法及其在渣油加氢参数优化的应用[J]. 化工学报, 2018, 69(3): 891-899.
XuY F, QianF, YangM L, et al. Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters[J]. CIESC Journal, 2018, 69(3): 891-899.
11 VrentasJ S, VrentasC M. Analysis of plug flow reactors with variable mass density[J]. AIChE Journal, 2014, 60(12): 4185-4189.
12 Del ValleY, VenayagamoorthyG K, MohagheghiS, et al. Particle swarm optimization: basic concepts, variants and applications in power systems[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(2): 171-195.
13 WangY, LiB, WeiseT, et al. Self-adaptive learning based particle swarm optimization[J]. Information Sciences, 2011, 181(20): 4515-4538.
14 周红标, 乔俊飞. 混合多目标骨干粒子群优化算法在污水处理过程优化控制中的应用[J]. 化工学报, 2017, 68(9): 3511-3521.
ZhouH B, QiaoJ F. Optimal control of wastewater treatment process using hybrid multi-objective barebones particle swarm optimization algorithm[J]. CIESC Journal, 2017, 68(9): 3511-3521.
15 韩红桂, 张璐, 乔俊飞. 基于多目标粒子群算法的污水处理智能优化控制[J]. 化工学报, 2017, 68(4): 1474-1481.
HanH G, ZhangL, QiaoJ F. Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm[J]. CIESC Journal, 2017, 68(4): 1474-1481.
16 DebK, PratapA, AgarwalS, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
17 ZhangR D, TaoJ L, GaoF R. A new approach of Takagi-Sugeno fuzzy modeling using an improved genetic algorithm optimization for oxygen content in a coke furnace[J]. Industrial & Engineering Chemistry Research, 2016, 55(22): 6465-6474.
18 WangY, LiH X, HuangT W, et al. Differential evolution based on covariance matrix learning and bimodal distribution parameter setting[J]. Applied Soft Computing, 2014, 18: 232-247.
19 赵敏华, 胡毅, 李金, 等. 使用博弈差分算法的电站锅炉高效低污染燃烧均衡优化[J]. 化工学报, 2017, 68(6): 2455-2464.
ZhaoM H, HuY, LiJ, et al. Equilibrium optimization for high efficiency and low pollution combustion of power-generation boilers using game differential evolution algorithm[J]. CIESC Journal, 2017, 68(6): 2455-2464.
20 OteizaP P, BrignoleN B. An evolutionary algorithm applied to inventory control for natural gasoline[J]. Industrial & Engineering Chemistry Research, 2016, 55(51): 13062-13073.
21 ZhouX J, YangC H, GuiW H. State transition algorithm[J]. Journal of Industrial and Management Optimization, 2012, 8(4): 1039-1056.
22 ZhouX J, ShiP, LimC C, et al. A dynamic state transition algorithm with application to sensor network localization[J]. Neurocomputing, 2018, 273: 237-250.
23 ZhouX J, GaoD Y, YangC H, et al. Discrete state transition algorithm for unconstrained integer optimization problems[J]. Neurocomputing, 2016, 173: 864-874.
24 HanJ, DongT X, ZhouX J, et al. State transition algorithm for constrained optimization problems[C]//Proceedings of the 33rd Chinese control conference. Nanjing, China: IEEE CPP, 2014: 7543-7548.
25 HanJ, YangC H, ZhouX J, et al. A two-stage state transition algorithm for constrained engineering optimization problems[J]. International Journal of Control Automation and Systems, 2018, 16(2): 522-534.
26 ZhouX J, YangC H, GuiW H. Nonlinear system identification and control using state transition algorithm[J]. Applied Mathematics and Computation, 2014, 226: 169-179.
27 张凤雪, 阳春华, 周晓君, 等. 基于控制周期计算的锌液净化除铜过程优化控制[J]. 控制理论与应用, 2017, 34(10): 1388-1395.
ZhangF X, YangC H, ZhouX J, et al. Optimal control based on control period calculation for copper removal process of zinc solution purification[J]. Control Theory & Applications, 2017, 34(10): 1388-1395.
28 AhandaniM A. Opposition-based learning in the shuffled bidirectional differential evolution algorithm[J]. Swarm and Evolutionary Computation, 2016, 26: 64-85.
29 RahnamayanS, TizhooshH R, SalamaM M A. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1): 64-79.
30 LiQ Y, JiangQ Y, CaoZ K, et al. Modeling and simulation for the hydrocracking reactor[C]//Proceedings of the 27th Chinese control conference. Kunming: IEEE CPP, 2008: 204-208.
31 王松汉. 石油化工设计手册 [M]. 北京: 化学工业出版社, 2002.
WangS H. Petrochemical Design Manual[M]. Beijing: Chemical Industry Press, 2002.
[1] Zhixin SHANG, Xianglan ZHANG. DFT study on effects of hydrolysis degrees of 3-mercaptopropyltriethoxysilane on grafting mechanisms of nano-silica [J]. CIESC Journal, 2019, 70(5): 1663-1673.
[2] Jingchun YAN, Laihong SHEN, Shouxi JIANG, Huijun GE. Chemical looping combustion of high-sodium coal and gasification kinetics of coal char [J]. CIESC Journal, 2019, 70(5): 1913-1922.
[3] Shipin YANG, Zhen HUANG, Lijuan LI, Jianquan SONG, Jing YE, Hui WANG. A deep dive diagnostic and correction algorithm for mismatched sub-models in complicated chemical processes [J]. CIESC Journal, 2019, 70(4): 1485-1493.
[4] Desheng LI, Chao ZHANG, Shihai DENG, Zhifeng HU, Jinlong LI, Yuanhui LIU. Experimental study on effective nitrate removal from sewage by ZVI-based catalyzed reduction [J]. CIESC Journal, 2019, 70(3): 1065-1074.
[5] Shan DOU, Guangyu ZHANG, Zhihua XIONG. Anomaly detection of process unit based on LSTM time series reconstruction [J]. CIESC Journal, 2019, 70(2): 481-486.
[6] Jian TANG, Junfei QIAO. Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm [J]. CIESC Journal, 2019, 70(2): 696-706.
[7] Jiong DING, Qi CHEN, Qiyue XU, Suijun YANG, Shuliang YE. ARC thermal inertia correction method based on C80 data merging [J]. CIESC Journal, 2019, 70(1): 417-424.
[8] XUE Chao, MAO Yanpeng, WANG Wenlong, SONG Zhanlong, ZHAO Xiqiang, SUN Jing, WANG Yanxiang. Treatment of phenol wastewater by microwave catalytic wet oxidation under high pressure [J]. CIESC Journal, 2018, 69(S2): 210-217.
[9] SONG Rui, JIN Guangyuan, CUI Zhengwei, SONG Chunfang, CHEN Haiying. Dielectric properties of mixed materials in transesterification reaction system [J]. CIESC Journal, 2018, 69(8): 3670-3677.
[10] LI Hanshuang, ZHAO Zhonggai, LIU Fei. Identification of linear parameter varying systems with variational Bayesian algorithm [J]. CIESC Journal, 2018, 69(7): 3125-3134.
[11] LI Yannan, CHENG Jun, LIU Jianzhong, ZHOU Junhu, CEN Kefa. CO2removal from biohythane by absorption in ionic liquid[P66614][Triz]loaded on molecular sieve SBA-15 [J]. CIESC Journal, 2018, 69(6): 2526-2532.
[12] WANG Ziming, CHEN Liang, YUE Shuang, WANG Chunbo. Microstructure evolution of procedural products during limestone simultaneous calcination/sulfation [J]. CIESC Journal, 2018, 69(5): 2149-2157.
[13] NI Zhuo, LIN Yuhao, HUANG Weiying, LIN Lirong. Preparation and reaction kinetics of epoxy resin microcapsules [J]. CIESC Journal, 2018, 69(4): 1790-1798.
[14] XU Yufei, QIAN Feng, YANG Minglei, DU Wenli, ZHONG Weimin. Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters [J]. CIESC Journal, 2018, 69(3): 891-899.
[15] LUO Ruihan, CHEN Juan, WANG Qi. Kinetic model optimization of n-butane isomerization by improved biogeography optimization algorithm [J]. CIESC Journal, 2018, 69(3): 1158-1166.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LING Lixia, ZHANG Riguang, WANG Baojun, XIE Kechang. Pyrolysis Mechanisms of Quinoline and Isoquinoline with Density Functional Theory[J]. , 2009, 17(5): 805 -813 .
[2] LEI Zhigang, LONG Aibin, JIA Meiru, LIU Xueyi. Experimental and Kinetic Study of Selective Catalytic Reduction of NO with NH3 over CuO/Al2O3/Cordierite Catalyst[J]. , 2010, 18(5): 721 -729 .
[3] SU Haifeng, LIU Huaikun, WANG Fan, LÜXiaoyan, WEN Yanxuan. Kinetics of Reductive Leaching of Low-grade Pyrolusite with Molasses Alcohol Wastewater in H2SO4[J]. , 2010, 18(5): 730 -735 .
[4] WANG Jianlin, XUE Yaoyu, YU Tao, ZHAO Liqiang. Run-to-run Optimization for Fed-batch Fermentation Process with Swarm Energy Conservation Particle Swarm Optimization Algorithm[J]. , 2010, 18(5): 787 -794 .
[5] SUN Fubao, MAO Zhonggui, ZHANG Jianhua, ZHANG Hongjian, TANG Lei, ZHANG Chengming, ZHANG Jing, ZHAI Fangfang. Water-recycled Cassava Bioethanol Production Integrated with Two-stage UASB Treatment[J]. , 2010, 18(5): 837 -842 .
[6] Gao Ruichang, Song Baodong and Yuan Xiaojing( Chemical Engineering Research Center, Tianjin University, Tianjin 300072). LIQUID FLOW DISTRIBUTION IN GAS - LIQUID COUNTER - CONTACTING PACKED COLUMN[J]. , 1999, 50(1): 94 -100 .
[7] Su Yaxin, Luo Zhongyang and Cen Kefa( Institute of Thermal Power Engineering , Zhejiang University , Hangzhou 310027). A STUDY ON THE FINS OF HEAT EXCHANGERS FROM OPTIMIZATION OF ENTROPY GENERATION[J]. , 1999, 50(1): 118 -124 .
[8] Luo Xiaoping(Department of Industrial Equipment and Control Engineering , South China University of Technology, Guangzhou 510641)Deng Xianhe and Deng Songjiu( Research Institute of Chemical Engineering, South China University of Technology, Guangzhou 5106. RESEARCH ON FLOW RESISTANCE OF RING SUPPORT HEAT EXCHANGER WITH LONGITUDINAL FLUID FLOW ON SHELL SIDE[J]. , 1999, 50(1): 130 -135 .
[9] Jin Wenzheng , Gao Guangtu , Qu Yixin and Wang Wenchuan ( College of Chemical Engineering, Beijing Univercity of Chemical Technology, Beijing 100029). MONTE CARLO SIMULATION OF HENRY CONSTANT OF METHANE OR BENZENE IN INFINITE DILUTE AQUEOUS SOLUTIONS[J]. , 1999, 50(2): 174 -184 .
[10]

LI Qingzhao;ZHAO Changsui;CHEN Xiaoping;WU Weifang;LI Yingjie

.

Combustion of pulverized coal in O2/CO2 mixtures and its pore structure development

[J]. , 2008, 59(11): 2891 -2897 .