基于PSO-控制变量参数化混合策略的间歇化工过程优化控制
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摘要
控制变量参数化方法作为一种化工过程动态优化的梯度搜索算法,其求解效率过于依赖初始给定轨迹。目前初始轨迹一般都是设定在边界值或中间值,缺乏科学依据,从而大大影响了算法的收敛速度。针对这一问题,提出了一种粒子群优化(PSO)与控制变量参数化方法混合的策略,首先利用粒子群优化对间歇化工过程最优控制量进行求解,结果作为控制变量参数化方法初始给定轨迹,进行二次优化。双层优化的混合策略提高了控制变量参数化方法的收敛速度和粒子群优化算法的求解精度。将混合策略应用于两个间歇化工过程优化控制实例,仿真结果表明了该算法对求解化工过程动态优化问题具有可行性和有效性。
As a gradient search algorithm for dynamic optimization of chemical process, the efficiency of control vector parameterization depends on the initial given trajectory deeply. At present, the initial trajectory is usually set at the boundary value or the intermediate value, which does not have enough scientific reason and it affects the convergence speed of the algorithm. To solve this problem, a hybrid strategy combined with particle swarm optimization and control vector parameterization method is proposed in this paper, it uses particle swarm optimization to achieve the value of control variables before employing the method of control vector parameterization to reoptimize the process. The two-layer optimization hybrid strategy improves the convergence speed of the control vector parameterization method and the precision of the particle swarm optimization. The hybrid strategy is applied to two examples of batch chemical process optimization control, and the simulation results show that the algorithm is feasible and effective for solving dynamic optimization problems of chemical process.
As a gradient search algorithm for dynamic optimization of chemical process, the efficiency of control vector parameterization depends on the initial given trajectory deeply. At present, the initial trajectory is usually set at the boundary value or the intermediate value, which does not have enough scientific reason and it affects the convergence speed of the algorithm. To solve this problem, a hybrid strategy combined with particle swarm optimization and control vector parameterization method is proposed in this paper, it uses particle swarm optimization to achieve the value of control variables before employing the method of control vector parameterization to reoptimize the process. The two-layer optimization hybrid strategy improves the convergence speed of the control vector parameterization method and the precision of the particle swarm optimization. The hybrid strategy is applied to two examples of batch chemical process optimization control, and the simulation results show that the algorithm is feasible and effective for solving dynamic optimization problems of chemical process.
引文
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[2]Pollard J P,Sargent R W H.Off line computation of optimum controls for a plate distillation column[J].Automatica,1970,6(1):59-76.
[3]Sargent R W H,Sullivan G R.The development of an efficient optimal control package[J].Nonlinear and Stochastic Programming,1979,13(7):158-168.
[4]Vassiliadis V S,Sargent R W H,Pantelides C C.Solution of a class of multistage dynamic optimization problems(Ⅱ):Problems with path constraints[J].I&EC Research,1994,33(9):2123-2133.
[5]Martin S,Klaus S,Thomas B,et al.Dynamic optimization using adaptive control vector parameterization[J].Computers&Chemical Engineering,2005,29(8):1731-1751.
[6]Wang P,Tian X M.Enhanced control vector parameterization method and its application in dynamic optimization[J].Control&Decision,2009,24(11):1757-1760.
[7]Chen X,Du W L,Huaglory T,et al.Dynamic optimization of industrial processes with nonuniform discretization-based control vector parameterization[J].IEEE Transactions on Automation Science&Engineering,2014,11(4):1289-1299.
[8]Teo K L,Jennings L S,Lee H W J,et al.The control parametrization enhancing transform for constrained optimal control problems[J].J.Austral.Math.Soc.Sen.B,1999,40:314-335.
[9]Li G D,Liu P,Liu X G.A control parameterization approach with variable time nodes for optimal control problems[J].Asian Journal of Control,2016,18(3):976-984.
[10]Binder T,Cruse A,Cruz Villar C A,et al.Dynamic optimization using a wavelet based adaptive control[J].Computers and Chemical Engineering,2000,24:1201-1207.
[11]Liu P,Li G D,Liu X G.Fast engineering optimization:a novel highly effective control parameterization approach for industrial dynamic processes[J].Isa Transactions,2015,58:248-254.
[12]Sun F,Zhong W M,Cheng H,et al.Novel control vector parameterization method with differential evolution algorithm and its application in dynamic optimization of chemical processes[J].Chinese Journal of Chemical Engineering,2013,21(1):64-71.
[13]王东风,孟丽.粒子群优化算法的性能分析和参数选择[J].自动化学报,2016,42(10):1552-1561.Wang D F,Meng L.Performance analysis and parameter selection of particle swarm optimization algorithm[J].Journal of Automation,2016,42(10):1552-1561.
[14]Logsdon J S,Biegler L T.A relaxed reduced space SQP strategy for dynamic optimization problems[J].Computers&Chemical Engineering,1993,17(4):367-372.
[15]Rajesh J,Gupta K,Kusumakar H S,et al.Dynamic optimization of chemical processes using ant colony framework[J].Computers&Chemistry,2001,25(6):583-595.
[16]Renfro J G,Morshedi A M,Asbjornsen O A.Simultaneous optimization and solution of systems described by differential algebraic equations[J].Computers&Chemical Engineering,1987,11(5):503-517.
[17]Zhang B,Chen D Z,Zhao W.Iterative ant-colony algorithm and its application to dynamic optimization of chemical process[J].Computers&Chemical Engineering,2005,29(10):2078-2086.
[18]Lee J,Ramirez W F.Optimal fed-batch control of induced foreign protein production by recombinant bacteria[J].AIChE Journal,1994,40(5):899-907.
[19]Tholudur A,Ramirez W F.Obtaining smoother singular arc policies using a modified iterative dynamic programming algorithm[J].International Journal of Control,1997,68(5):1115-1128.
[20]Sarkar D,Modak J M.Optimization of fed-batch bioreactors using genetic algorithm:multiple control variables[J].Computers&Chemical Engineering,2004,28(5):789-798.
[21]Nikumb S,Ghosh S,Jayaraman V K,et al.Biogeography-based optimization for dynamic optimization of chemical reactors[M]//Applications of Mataheuristics in Process Engineering,Springer,2014:201-216.
[22]Hirmaje T,Balsacanto E,Banga J R.DOTcvpSB,a software toolbox for dynamic optimization in systems biology[J].BMCBioinformatics,2009,10:199.
[23]黎向宇.基于时间网格重构的多重打靶最优控制策略研究[D].杭州:浙江大学,2017.Li X Y.Study on optimal control strategy for multiple targets based on time grid reconstruction[D].Hangzhou:Zhejiang University,2017.