XUE Yongfei, WANG Yalin, SUN Bei, LI Qianzhong, SUN Jiazhou. Improved state transfer algorithm-based kinetics parameter estimation for cascaded plug flow reactors[J]. CIESC Journal, 2019, 70(2): 607-616
XUE Yongfei, WANG Yalin, SUN Bei, LI Qianzhong, SUN Jiazhou. Improved state transfer algorithm-based kinetics parameter estimation for cascaded plug flow reactors[J]. CIESC Journal, 2019, 70(2): 607-616DOI: 10.11949/j.issn.0438-1157.20181343.
Improved state transfer algorithm-based kinetics parameter estimation for cascaded plug flow reactors
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.
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Sun B , Gui W H , Wu T B , et al . An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential [J ] . Hydrometallurgy , 2013 , 140 : 102 - 110 .
Fu Y , Liu X G . Nonlinear wave modeling and dynamic analysis of high-purity heat integrated air separation column [J ] . Separation and Purification Technology , 2015 , 151 : 14 - 22 .
Sildir H , Arkun Y , Cakal B , 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 .
Sun B , Gui W H , Wang Y L , et al . A gradient optimization scheme for solution purification process [J ] . Control Engineering Practice , 2015 , 44 : 89 - 103 .
Sun B , Gui W H , Wang Y L , et al . Intelligent optimal setting control of a cobalt removal process [J ] . Journal of Process Control , 2014 , 24 ( 5 ): 586 - 599 .
Sildir H , Arkun Y , Cakal B , et al . Plant-wide hierarchical optimization and control of an industrial hydrocracking process [J ] . Journal of Process Control , 2013 , 23 ( 9 ): 1229 - 1240 .
Li Y G , Gui W H , Teo K L , et al . Optimal control for zinc solution purification based on interacting CSTR models [J ] . Journal of Process Control , 2012 , 22 ( 10 ): 1878 - 1889 .
Li Y G , Ma L , Wu T B , et al . pH optimization and control in iron removal process of multi-reactor cascade [J ] . CIESC Journal , 2018 , 69 ( 6 ): 2586 - 2593 .
Brooks K . Steady-state multiplicity in an adiabatic continuous stirred tank reactor with vapor recycle [J ] . AIChE Journal , 2013 , 59 ( 2 ): 553 - 559 .
Xu Y F , Qian F , Yang M L , et al . Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters [J ] . CIESC Journal , 2018 , 69 ( 3 ): 891 - 899 .
Vrentas J S , Vrentas C M . Analysis of plug flow reactors with variable mass density [J ] . AIChE Journal , 2014 , 60 ( 12 ): 4185 - 4189 .
Del Valle Y , Venayagamoorthy G K , Mohagheghi S , et al . Particle swarm optimization: basic concepts, variants and applications in power systems [J ] . IEEE Transactions on Evolutionary Computation , 2008 , 12 ( 2 ): 171 - 195 .
Wang Y , Li B , Weise T , et al . Self-adaptive learning based particle swarm optimization [J ] . Information Sciences , 2011 , 181 ( 20 ): 4515 - 4538 .
Han H G , Zhang L , Qiao J F . Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm [J ] . CIESC Journal , 2017 , 68 ( 4 ): 1474 - 1481 .
Deb K , Pratap A , Agarwal S , et al . A fast and elitist multiobjective genetic algorithm: NSGA-II [J ] . IEEE Transactions on Evolutionary Computation , 2002 , 6 ( 2 ): 182 - 197 .
Zhang R D , Tao J L , Gao F 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 .
Wang Y , Li H X , Huang T W , et al . Differential evolution based on covariance matrix learning and bimodal distribution parameter setting [J ] . Applied Soft Computing , 2014 , 18 : 232 - 247 .
Zhao M H , Hu Y , Li J , 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 .
Oteiza P P , Brignole N B . An evolutionary algorithm applied to inventory control for natural gasoline [J ] . Industrial & Engineering Chemistry Research , 2016 , 55 ( 51 ): 13062 - 13073 .
Zhou X J , Yang C H , Gui W H . State transition algorithm [J ] . Journal of Industrial and Management Optimization , 2012 , 8 ( 4 ): 1039 - 1056 .
Zhou X J , Shi P , Lim C C , et al . A dynamic state transition algorithm with application to sensor network localization [J ] . Neurocomputing , 2018 , 273 : 237 - 250 .
Zhou X J , Gao D Y , Yang C H , et al . Discrete state transition algorithm for unconstrained integer optimization problems [J ] . Neurocomputing , 2016 , 173 : 864 - 874 .
Han J , Dong T X , Zhou X 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 .
Han J , Yang C H , Zhou X 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 .
Zhou X J , Yang C H , Gui W H . Nonlinear system identification and control using state transition algorithm [J ] . Applied Mathematics and Computation , 2014 , 226 : 169 - 179 .
Zhang F X , Yang C H , Zhou X 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 .
Ahandani M A . Opposition-based learning in the shuffled bidirectional differential evolution algorithm [J ] . Swarm and Evolutionary Computation , 2016 , 26 : 64 - 85 .
Rahnamayan S , Tizhoosh H R , Salama M M A . Opposition-based differential evolution [J ] . IEEE Transactions on Evolutionary Computation , 2008 , 12 ( 1 ): 64 - 79 .
Li Q Y , Jiang Q Y , Cao Z K , et al . Modeling and simulation for the hydrocracking reactor [C ] // Proceedings of the 27th Chinese control conference . Kunming : IEEE CPP , 2008 : 204 - 208 .
王松汉 . 石油化工设计手册 [M ] . 北京 : 化学工业出版社 , 2002 .
Wang S H . Petrochemical Design Manual [M ] . Beijing : Chemical Industry Press , 2002 .