乙炔加氢反应过程混合建模与优化
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摘要
针对传统单一建模方法所构建的乙炔加氢反应器数学模型存在预测性能无法满足工业实际应用需求的问题,提出了一种机理与神经网络嵌套的建模方法,充分利用机理模型包含的能质约束信息降低神经网络模型的约束违反度,得到了能够良好描述实际工业乙炔加氢反应过程特性的混合模型。基于反应器混合模型,研究了以运行效益为目标函数的优化问题。主要决策变量包括:一段反应器进料中氢气与乙炔的摩尔比(RH/A)、进料温度和反应器运行周期等几个关键参数。针对反应器长期运行后,催化剂活性降低造成的处理能力下降的问题,提出了反应温度补偿机制和RH/A并行调节的运行优化策略,并采用序列法对反应器运行周期进行离散化处理。通过引入差异化变异策略、潜在解替代策略对两阶段差分算法进行改进,采用增量式编码法结合改进两阶段差分算法,对优化问题进行求解。结果证实了优化策略与改进算法的有效性,并据此确定了反应器最佳运行方案。
The mathematical model of the acetylene hydrogenation reactor established by traditional single modeling method does not meet the needs of industrial practical applications in predictive performance. This paperproposes a mechanism and neural network nesting modeling method, which fully utilizes the mechanism model. Itmakes full use of mass and energy balance information in mechanism model to reduce the degree of constraintviolation of the neural network model, which can describe the process characteristics of industrial reactor well. Theoptimization problem which targets the operational profits as the objective function is studied basing on the hybridmodel. The main decision variables include several key parameters, such as the reactor feed hydrogen-alkyne ratio,the feed temperature, and the two-stage reactor operating cycle and many more. For the long-term operation of thereactor, processing capacity of the reactor will decrease due to the decreased catalyst activity, and an improvedoptimizing strategy is proposed by adjusting the hydrogen-alkyne ratio as well as the reaction temperaturesimultaneously. The sequence method is used to discretize the operating cycle of the reactor. The two-stagedifference algorithm is improved by introducing differential mutation strategy and potential solution alternativestrategy. Then the optimization problem is solved by combining the incremental coding method with the improved two-stage difference algorithm. And the results confirm the effectiveness. Furthermore the optimal operating cycle and operating strategy of the reactor are given.
The mathematical model of the acetylene hydrogenation reactor established by traditional single modeling method does not meet the needs of industrial practical applications in predictive performance. This paperproposes a mechanism and neural network nesting modeling method, which fully utilizes the mechanism model. Itmakes full use of mass and energy balance information in mechanism model to reduce the degree of constraintviolation of the neural network model, which can describe the process characteristics of industrial reactor well. Theoptimization problem which targets the operational profits as the objective function is studied basing on the hybridmodel. The main decision variables include several key parameters, such as the reactor feed hydrogen-alkyne ratio,the feed temperature, and the two-stage reactor operating cycle and many more. For the long-term operation of thereactor, processing capacity of the reactor will decrease due to the decreased catalyst activity, and an improvedoptimizing strategy is proposed by adjusting the hydrogen-alkyne ratio as well as the reaction temperaturesimultaneously. The sequence method is used to discretize the operating cycle of the reactor. The two-stagedifference algorithm is improved by introducing differential mutation strategy and potential solution alternativestrategy. Then the optimization problem is solved by combining the incremental coding method with the improved two-stage difference algorithm. And the results confirm the effectiveness. Furthermore the optimal operating cycle and operating strategy of the reactor are given.
引文
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[19]Tian L,Jiang D,Qian F,et al.Improve acetylene hydrogenation selectivity using dynamic deactivation estimation[J].Hydrocarbon Processing,2015,94(12):39-44.
[20]N?si N,Alikoski M,White D C.Advanced control of acetylene reactor[J].Hydrocarbon Process,1985,6(2):57-60.
[21]Liu Z Z,Wang Y,Yang S X,et al.Differential evolution with a two-stage optimization mechanism for numerical optimization[J].IEEE T.Evolut.Comput.,2016,2(12):3170-3177.
[22]Wang Y,Cai Z,Zhang Q.Differential evolution with composite trial vector generation strategies and control parameters[J].IEEET.Evolut.Comput.,2011,15(1):55-66.
[23]张大斌,江华,徐柳怡.基于两阶段变异交叉策略的差分进化算法[J].计算机工程,2014,40(8):183-189.Zhang D B,Jiang H,Xu L Y.Differential evolution algorithm based on two-stage mutation and crossing strategy[J].Computer Engineering,2014,40(8):183-189.
[24]Wang Y,Wang B C,Li H X.Incorporating objective function information into the feasibility rule for constrained evolutionary optimization[J].IEEE Transactions on Cybernetics,2016,46(12):2938-2852.
[25]Mallipeddi R,Suganthan P N.Problem definitions and evaluation criteria for the CEC2010 competition on constrains realparameter optimization[R].Singapore:Nanyang Technological University,2010.
[26]Rao R V,Patel V.An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems[J].Int.J.Ind.Eng.Comput.,2012,3(4)535-560.
[27]Montgomery D C.Design and Analysis of Experiments[M].7th ed New York:John Wiley and Sons,Inc,2008.
[28]Takahama T,Sakai S.Constrained optimization by theεconstrained differential evolution with an archive and gradientbased mutation[J].IEEE T.Evolut,Comput.,2010,5(3):1-9.
[29]Yu K,Wang X,Wang Z.Constrained optimization based on improved teaching-learning-based optimization algorithm[J].Information Science,2016,352(12):61-78.
[30]Chen X,Du W L,Huaglory T,et al.Dynamic optimization of industrial processes with nonuniform discretization-based control vector parameterization[J].IEEE T.Autom.Sci.Eng.,2014,11(4):1289-1299.
[31]Liang J J,Shang Z G,Li Z H.Coevolutionary comprehensive learning particle swarm optimizer[J].IEEE T.Evolut,Comput.,2010,6(2):1-8.
[2]王松汉,何细藕.乙烯工艺与技术[M].北京:中国石化出版社2000.Wang S H,He X O.Ethylene Process and Technology[M].Beijing China Petrochemical Press,2000.
[3]张万钧.扬子乙烯装置技术综览.第一篇:综合技术[M].北京中国石化出版社,1997.Zhang W J.Technology Overview of Yangzi Ethylene Plant Chapter 1:Integrated Technology[M].Beijing:China Petrochemical Press,1997.
[4]谢府命,许峰,梁志珊,等.乙炔加氢反应器全周期操作优化[J].化工学报,2018,69(3):1081-1091.Xie F M,Xu F,Liang Z S,et al.Operation optimization of acetylene hydrogenation reactor on regeneration cycle[J].CIESCJournal,2018,69(3):1081-1091.
[5]吴斌,李绍军,刘漫丹,等.补偿模糊神经网络在乙炔加氢反应器中的作用[J].计算技术与自动化,2003,22(2):307-310.Wu B,Li S J,Liu M D,et al.Application of compensated fuzzy neural network in acetylene hydrogenation reactor[J].Computing Technology and Automation,2003,22(2):307-310.
[6]Gobbo R,Soares R P,Lansarin M A,et al.Modeling,simulation and optimization of a front-end system for acetylene hydrogenation reactors[J].Braz.J.Chem.Eng.,2004,21(4)545-556.
[7]Azizi M,Zolfaghari S A,Mousavi S A,et al.Study on the acetylene hydrogenation process for ethylene production simulation,modification and optimization[J].Chemica Engineering Communication,2013,200(7):863-877.
[8]Caetano R,Lemos M A,Lemos F,et al.Modeling and control o an exothermal reaction[J].Chemical Engineering Journal,2014238(4):93-99.
[9]田亮,蒋达,钱锋.乙炔加氢反应系统操作优化策略[J].化工学报,2015,66(1):373-377.Tian L,Jiang D,Qian F.Operation optimization strategy for acetylene hydrogenation reaction system[J].CIESC Journal,201566(1):373-377.
[10]Du W L,Bao C Y,Chen X,et al.Dynamic optimization of the tandem acetylene hydrogenation process[J].Industrial&Engineering Chemistry Research,2016,55(46):11983-11995.
[11]田亮,蒋达,钱峰.钯金属催化剂上的乙炔工业选择性加氢反应动力学比较[J].计算机与应用化学,2012,29(9):1031-1035.Tian L,Jiang D,Qian F.Reaction kinetic comparisons for industrial selective hydrogenation of acetylene on palladium catalysts[J].Computers and Applied Chemistry,2012,29(9):1031-1035.
[12]Biegler L T.An overview of simultaneous strategies for dynamic optimization[J].Chem.Eng.Process.,2007,46(6):1043-1053.
[13]Borodzinski A.Selective hydrogenation of ethyne in ethene-rich streams on palladium catalysts(Part 1):Effect of changes to the catalyst during reaction[J].Cat.Rev.,2006,48(9):91-144.
[14]Schbib N S,García M A,Gigola C E,et al.Kinetics of front-end acetylene hydrogenation in ethylene production[J].Ind.Eng.Chem.Res.,1996,35(5):1496-1505.
[15]Albers P,Pietsch J,Parker S F.Poisoning and deactivation of palladium catalysts[J].J.Molec.Catal.A:Chem.,2001,173(1/2):275-286.
[16]Saeedizad M,Sahebdelfar S,Mansourpour Z.Deactivation kinetics of platinum-based catalysts in dehydrogenation of higher alkanes[J].Chem.Eng.J.,2009,154(11):76-81.
[17]田亮,蒋达,钱锋.催化剂失活条件下的碳二加氢反应器模拟与优化[J].化工学报,2012,63(1):185-192.Tian L,Jiang D,Qian F.Simulation and optimization of acetylene converter with decreasing catalyst activity[J].CIESC Journal,2012,63(1):185-192.
[18]刘艳,任章.基于神经网络的混合模型建模方法及应用[J].计算机仿真,2007,24(2):45-48.Liu Y,Ren Z.A mixed model modeling method based on neural network and its application[J].Computer Simulation,2007,24(2):45-48.
[19]Tian L,Jiang D,Qian F,et al.Improve acetylene hydrogenation selectivity using dynamic deactivation estimation[J].Hydrocarbon Processing,2015,94(12):39-44.
[20]N?si N,Alikoski M,White D C.Advanced control of acetylene reactor[J].Hydrocarbon Process,1985,6(2):57-60.
[21]Liu Z Z,Wang Y,Yang S X,et al.Differential evolution with a two-stage optimization mechanism for numerical optimization[J].IEEE T.Evolut.Comput.,2016,2(12):3170-3177.
[22]Wang Y,Cai Z,Zhang Q.Differential evolution with composite trial vector generation strategies and control parameters[J].IEEET.Evolut.Comput.,2011,15(1):55-66.
[23]张大斌,江华,徐柳怡.基于两阶段变异交叉策略的差分进化算法[J].计算机工程,2014,40(8):183-189.Zhang D B,Jiang H,Xu L Y.Differential evolution algorithm based on two-stage mutation and crossing strategy[J].Computer Engineering,2014,40(8):183-189.
[24]Wang Y,Wang B C,Li H X.Incorporating objective function information into the feasibility rule for constrained evolutionary optimization[J].IEEE Transactions on Cybernetics,2016,46(12):2938-2852.
[25]Mallipeddi R,Suganthan P N.Problem definitions and evaluation criteria for the CEC2010 competition on constrains realparameter optimization[R].Singapore:Nanyang Technological University,2010.
[26]Rao R V,Patel V.An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems[J].Int.J.Ind.Eng.Comput.,2012,3(4)535-560.
[27]Montgomery D C.Design and Analysis of Experiments[M].7th ed New York:John Wiley and Sons,Inc,2008.
[28]Takahama T,Sakai S.Constrained optimization by theεconstrained differential evolution with an archive and gradientbased mutation[J].IEEE T.Evolut,Comput.,2010,5(3):1-9.
[29]Yu K,Wang X,Wang Z.Constrained optimization based on improved teaching-learning-based optimization algorithm[J].Information Science,2016,352(12):61-78.
[30]Chen X,Du W L,Huaglory T,et al.Dynamic optimization of industrial processes with nonuniform discretization-based control vector parameterization[J].IEEE T.Autom.Sci.Eng.,2014,11(4):1289-1299.
[31]Liang J J,Shang Z G,Li Z H.Coevolutionary comprehensive learning particle swarm optimizer[J].IEEE T.Evolut,Comput.,2010,6(2):1-8.