CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 750-756.doi: 10.11949/j.issn.0438-1157.20181361

Previous Articles     Next Articles

Multi-objective optimization of QPSO for thereaction-regeneration process

Junren BAI1(),Jun YI1(),Qian LI2,Ling WU1,Xuemei CHEN1   

  1. 1. School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
    2. Mathematics Teaching Department, College of Mobile Telecommunications, Chongqing University of Posts and Telecom, Chongqing 401520, China
  • Received:2018-11-18 Revised:2018-12-08 Online:2019-02-05 Published:2018-12-04
  • Contact: Jun YI E-mail:bjr25793@qq.com;laoyifrcq@163.com

Abstract:

It is difficult to solve the multi-objective optimization problem of improving efficiency, reducing loss and reducing emissions for the catalytic cracking reaction regeneration process. The improved multi-objective quantum-based particle swarm optimization-crowding entropy sorting (MQPSO-CES) is used to solve the problem. A multi-objective optimization model is established to maximize the light oil absorption rate and synchronously minimize the coke yield and sulfide emissions. Particularly, crowding entropy sorting is used to update the archive, which accurately estimates the distribution of the non-dominated solutions. Afterwards, an adaptive factor is introduced to self-adaptively and dynamically adjust the construction of the attractor, which can balance the convergence and diversity of the proposed algorithm. In addition, with the application of a piecewise Gauss mutation operator, the precision of the local search can be enhanced. Finally, the multi-objective model is resolved with the novel algorithm. The results indicate that the improved algorithm can outperform other algorithms with convergent and well-distributed approximate Pareto fronts when dealing with ZDT3-4 and DTLZ1-2 benchmark problems. In addition, the proposed algorithm can obtain 76.22% of light oil absorption rate, 5.72% of coke yield and 626 mg/m3 of sulfide emissions in the reaction and generation process, illustrate its superiority compared with other algorithms.

Key words: catalysis, reaction, control, optimization, quantum-based particle swarm optimization, crowding entropy

CLC Number: 

  • TP 18

Fig.1

Piecewise Gauss mutation"

Fig.2

Approximate Pareto front obtained by MQPSO-CES"

Table 1

Test results of each algorithm"

算法指标ZDT3(E-3)ZDT4(E-3)DTLZ1(E-2)DTLZ2(E-2)
MOBFO

GD

SP

8.435±0.274

42.38±33.57

8.947±0.813

36.58±24.68

33.14±5.69

52.21±36.27

21.65±9.71

48.26±15.24

SPEA2

GD

SP

8.428±0.185

41.42±32.06

9.157±1.135

35.26±12.35

32.69±5.20

51.06±35.59

18.35±3.25

42.36±14.25

NSGA-II

GD

SP

8.524±0.362

41.18±28.36

9.265±1.358

35.01±8.65

33.18±6.85

46.24±29.63

16.25±3.61

43.65±15.32

MOPSO

GD

SP

8.365±0.254

42.12±32.46

9.542±1.253

42.15±23.19

34.38±0.95

53.43±26.68

18.13±6.25

42.32±13.25

MQPSO

GD

SP

10.23±1.218

41.63±31.28

8.362±1.285

42.43±24.18

25.12±1.03

50.92±35.32

21.02±7.35

41.26±10.65

Table 2

BPNN parameter settings"

参数迭代次数隐含层传递函数输出层传递函数隐含层节点数
轻质油吸收率500TansigPurelin15
焦炭产率500TansigPurelin15
硫化物排放量500LogsigPurelin15

Fig.3

Prediction error of training sets for objective function model"

Table 3

Partial optimization results of FCC reaction and regeneration process"

算法轻质油吸收率/%焦炭产率/%

硫化物排放量/

(mg/m3)

qrqcTr1/℃Tr2/℃Ta1/℃Ta1/℃Pr/kPaPa/kPa
MQPSO-CES76.045.836320.515.27503.7493.1656.7681.2235.7283.2
76.225.726260.485.18503.9493.5657.3680.7241.1283.8
MQPSO75.586.216580.475.66511.7504.2653.1683.5237.6283.4
73.655.826410.475.40512.2496.7656.2679.4227.4282.9
MOPSO74.386.276640.465.95513.8502.8649.4682.5229.8283.4
73.495.956810.455.57513.3495.7651.1678.1231.7283.5
NSGA-II75.096.226720.455.56509.9492.1650.8679.3232.5282.4
73.256.116480.465.46511.2497.2647.8675.4240.9283.0
SPEA274.866.306770.465.53512.9502.4650.2681.4229.5282.5
73.146.176680.455.32507.5496.7649.5680.5233.4283.1
MOBFO74.755.926540.445.78504.8494.8646.7678.7238.5282.1
74.365.846760.465.61510.7498.1647.2676.8227.6282.5
1 鲁波娜, 张景远, 王维, 等. FCC反应过程的CFD模拟进展[J]. 化工学报, 2016, 67(8): 3121-3132.
LuB N, ZhangJ Y, WangW, et al. CFD modeling of FCC reaction process: a review[J]. CIESC Journal, 2016, 67(8): 3121-3132.
2 刘蕾, 赵众, 陶兴文, 等. 催化裂化反应再生系统的建模与优化[J]. 石油化工自动化, 2009, 26(5): 26-30.
LiuL, ZhaoZ, TaoX W, et al. The modeling and optimization for FCC reactor-regenerator system[J]. Automation in Petro-Chemical Industry, 2009, 26(5): 26-30.
3 谢朝钢, 魏晓丽, 龚剑洪, 等. 催化裂化反应机理研究进展及实践应用[J]. 石油学报(石油加工) , 2017 , 33(2): 189-197.
XieC G, WeiX L, GongJ H, et al. Progress on chemistry of catalytic cracking reaction and its practice[J]. Acta Petroeli Sinica (Petroleum Processing Section), 2017, 33(2): 189-197.
4 刘梦溪, 卢春喜, 时铭显. 催化裂化后反应系统快分的研究进展[J]. 化工学报, 2016, 67(8): 3133-3145.
LiuM X, LuC X, ShiM X. Advances in quick separators of post-riser system in FRCC unit [J]. CIESC Journal, 2016, 67(8): 3133-3145.
5 熊凯, 卢春喜. 催化裂化(裂解)集总反应动力学模型研究进展[J]. 石油学报(石油加工), 2015, 31(2): 295-305.
XiongK, LuC X. Research progresses of lump kinetic model of FCC and catalytic pyrolysis[J]. Acta Petroeli Sinica (Petroleum Processing Section), 2015, 31(2): 295-305.
6 吴飞跃, 翁惠新, 罗世贤. FDFCC工艺中重油提升管催化裂化反应动力学模型[J].石油学报, 2008, 24(5): 540-546.
WuF Y, WengH X, LuoS X. Kinetic model for heavy oil catalytic cracking in riser of FCC process[J]. Acta Petroeli Sinica, 2008, 24(5): 540-546.
7 张忠洋, 李泽钦, 李宇龙, 等. GA辅助BP神经网络预测催化裂化装置汽油产率[J]. 石油炼制与化工, 2014, 45(7): 91-96.
ZhangZ Y, LiZ Q, LiY L, et al. Prediction of gasoline yield in FCC unit by GA aided BP neural network[J]. Petroleum Processing and Petrochemicals, 2014, 45(7): 91-96.
8 YiJ, HuangD, LiT F, et al. A novel framework for fault diagnosis using kernel partial least squares based on an optimal preference matrix[J]. IEEE Transaction on Industrial Electronics, 2017, 64(5): 4315-4324.
9 YiJ, HuangD, FuS Y, et al. Optimized relative transformation matrix using bacterial foraging algorithm for process fault detection[J]. IEEE Transaction on Industrial Electronics, 2016, 63(4): 2595-2605.
10 YiJ, BaiJ R, ZhouW, et al. Operating parameters optimization for the aluminum electrolysis process using an improved quantum-behaved particle swarm algorithm[J]. IEEE Transaction on Industrial Informatics, 2018, 14(8): 3405-3415.
11 MichalopoulosJ, PapadokonstadakisS, ArarnpatzisG, et al. Modelling of an industrial fluid catalytic cracking unit using neural networks[J]. Institution of Chemical Engineers, 2001, 79(3): 137-143.
12 LidT, StrandS. Real-time optimization of a cat cracker unit[J]. Computers & Chemical Engineering, 1997, 21(1/2): 887-892.
13 SankararaoB, GuptaK S. Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit using two jumping gene adaptations of simulated annealing [J]. Computers and Chemical Engineering, 2007, 31: 1496-1515.
14 KasatB R, GuptaK S. Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit using genetic algorithm with the jumping genes operator[J]. Computers and Chemical Engineering, 2003, 27: 1785-1800.
15 ChenC, YangB, YuanJ, et al. Establishment and solution of eight-lump kinetic model for FCC gasoline secondary reaction using particle swarm optimization[J]. Fuel, 2007, 86: 2325-2332.
16 WangY, WuL, YuanX. Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure[J]. Soft Computing, 2010, 14(3): 193-209.
17 肖晓伟, 肖迪, 林锦国, 等. 多目标优化问题的研究概述[J]. 计算机应用研究, 2011 , 28(3): 805-808.
XiaoX W, XiaoD, LinJ G, et al. Overview on multi-objective optimization problem research[J]. Application Research of Computers, 2011, 28(3): 805-808.
18 SunJ, FengB, XuW. Particle swarm optimization with particles having quantum behavior[C]//Proc. of the IEEE Congress on Evolutionary Computation. Portland, USA: IEEE, 2004: 325-331.
19 WangY N, WuL H, YuanX. Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure[J]. Soft Computing, 2010, 14(3): 193-209.
20 CoelhoL D S. Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems[J]. Expert Systems with Applications, 2010, 37(2): 1676-1683.
21 ZitzlerE, LaumannsM, ThieleL. SPEA2: improving the strength pareto evolutionary algorithm[C]//Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, 2002: 95-100.
22 SierraM R, CoelloC A C. Multi-objective particle swarm optimizer: a survey of the state-of-the-art[J]. International Journal of Computation Intelligence Research, 2006, 22(3): 287-308.
23 DebK, PratapA, AgarwalS, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
24 华长春, 王雅洁, 李军朋, 等. 基于NSGA-Ⅱ算法的高炉生产配料多目标优化模型建立[J]. 化工学报, 2016, 67(3): 1040-1047.
HuaC C, WangY J, LiJ P, et al. Multi-objective optimization model for blast furnace production and ingredients based on NSGA-II algorithm[J]. CIESC Journal, 2016, 67(3): 1040-1047.
25 YiJ, HuangD, FuS Y, et al. Multi-objective bacterial foraging optimization algorithm based on parallel cell entropy for aluminum electrolysis production process[J]. IEEE Transaction on Industrial Electronics, 2016, 63(4): 2488-2500.
26 易军, 黄迪, 李太福, 等. 基于拥挤距离排序的铝电解多目标优化[J].仪器仪表学报, 2015, 36(11): 2502-2509.
YiJ, HuangD, LiT F, et al. Optimization of aluminum electrolysis production process based on crowding distance sorting[J]. Chinese Journal of Scientific Instrument, 2015, 36(11): 2502-2509.
27 施展, 陈庆伟. 基于QPSO和拥挤距离排序的多目标量子粒子群优化算法[J]. 控制与决策, 2011, 26(4): 540-547.
ShiZ, ChenQ W. Multi-objective quantum-behaved particle swarm optimization algorithm based on QPSO and crowding distance sorting[J]. Control and Decision, 2011, 26 (4): 540-547.
28 DingS F, SuC Y, YuJ Z. An optimizing BP neural network algorithm based on genetic algorithm[J]. Artificial Intelligence Review, 2011, 36(2): 153-162.
29 刘朝华, 王慧娟, 吴春笃, 等. 基于BP神经网络的脉冲放电等离子体氧化酸性橙Ⅱ影响因素分析[J]. 化工学报, 2012, 63 (10): 3190-3195.
LiuC H, WangH J, WuC D, et al. Analysis of factors effecting acid orange 7 decoloration in pulsed discharge plasma system based on BP neural network mode[J]. CIESC Journal, 2012, 63(10): 3190-3195.
[1] Yanrao CHEN, Taoyan MAO, Cheng ZHENG. Microwave synthesis and properties of dioctadecyldihydroxyethyl ammonium bromide [J]. CIESC Journal, 2019, 70(S1): 226-234.
[2] Xingpeng LIU, Dandan YAN. Spectrum changes of electromagnetic pluses in chemical reactions [J]. CIESC Journal, 2019, 70(S1): 177-181.
[3] Enwei ZHI, Fei YAN, Mifeng REN, Gaowei YAN. Soft sensor of wet ball mill load parameters based on transfer variational autoencoder - label mapping [J]. CIESC Journal, 2019, 70(S1): 150-157.
[4] Zhaowen ZENG, Cheng ZHENG, Taoyan MAO, Yuan WEI, Runhui XIAO, Siyu PENG. Progress in research and application of microwave in chemical process [J]. CIESC Journal, 2019, 70(S1): 1-14.
[5] Hao YANG, Eryan YAN. Simulation research of microwave heating efficiency for beamed energy thruster [J]. CIESC Journal, 2019, 70(S1): 93-98.
[6] Weifeng XU, Aipeng JIANG, Haokun WANG, Enhui JIANG, Qiang DING, Hanhan GAO. A grid reconstruction strategy based on pseudo Wigner-Ville analysis for dynamic optimization problem [J]. CIESC Journal, 2019, 70(S1): 158-167.
[7] Chao PENG, Yuyuan WANG, Chang ai DENG, Fangfang ZHAO, Kuiyi YOU. Preparation of hydroxylamine sulfate by continuous reaction-extraction coupling technology [J]. CIESC Journal, 2019, 70(5): 1842-1847.
[8] Peng ZHANG, Yulu WANG, Wenjie DING, Wenlin ZHAO. Synthesis of new sulfhydryl flocculant PAM-GSH and its performance in removing Mn (Ⅱ) [J]. CIESC Journal, 2019, 70(5): 1932-1941.
[9] Liangjie JIN, Peng BAI, Xianghai GUO. Energy-saving optimization of partial diabatic distillation with side streams [J]. CIESC Journal, 2019, 70(5): 1804-1814.
[10] Dong HUANG, Xionglin LUO. Judgement of process transition control strategies for large-range conditions change of chemical processes [J]. CIESC Journal, 2019, 70(5): 1848-1857.
[11] Qin WANG, Bingjian ZHANG, Chang HE, Qinglin CHEN. Solvent evaluation model base on energy consumption objective for aromatic extraction distillation units [J]. CIESC Journal, 2019, 70(5): 1815-1822.
[12] Fei LI, Cuili YANG, Wenjing LI, Junfei QIAO. Optimal control of wastewater treatment process using NSGAII algorithm based on multi-objective uniform distribution [J]. CIESC Journal, 2019, 70(5): 1868-1878.
[13] Shuai REN, Xing LI, Jing ZHANG, Xiaohan WANG, Daiqing ZHAO. Investigation on combustion characteristics of ethanol and dimethyl ether micro-jet flames [J]. CIESC Journal, 2019, 70(5): 1973-1980.
[14] 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.
[15] 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.
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 .