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基于微粒群优化算法的不确定性调合调度

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Chinese J.Chem.Eng.,13(4)535—541(2005) Blending Scheduling under Uncertainty Based on Particle Swarm Optimization Algorithm丰 ZHAO Xiaoqiang(赵小强)and RONG Gang(荣冈)” Control,Zhejiang Uni- National Key Laboratory of Industrial Control Technology,Institute of Advanced Process versity,Hangzhou 310027,China Abstract tion Blending is an important unit operation in process industry.Blending scheduling is nonlinear optimiza- problem with constraints.It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization(PSO)algorithm is developed for nonlinear optimization problems with both contin- to obtain a global optimum solution quickly,PSO algorithm is applied to problem of blending scheduling under uncertainty.The calculation results based on an example of gasoline blending agree satisfactory with the ideal values,which illustrates that the PSO algorithm is valid and effective in UOUS and discrete variables.In order solve the solving the blending scheduling problem. Keywords blending scheduling,uncertainty,gasoline blending,particle swarm optimization algorithm,nonlinear optimization 1 INTRoDUCTIoN Scheduling is defined as so it is often considered a a key problem for the prof- Blending methods include the short—term manipula- a itabihty of company. tion of the degrees of freedom of satisfy the economic process in order to objectivestl|.namely the maxi— a batch blending,circulation blending,in-tank mixing blending,partial in-hne blending and continuous in- line mization of the product profit.Scheduling is problem and optimal scheduling is an crucial very blending.The objectives are of product blending objective hard to achieve in process industry[2J. Blending problem is often encountered in process meet product quality specifications while conforming to performance and environ_men. scheduling to tal requirements[6j,to minimize product quality give- away,to allocate the available blending component,to industries,e.g.,chemical,pharmaceutical,cosmet— ics and food industry. It represents nonlinear pro— meet product demands and products[2】. to maximize the profit of treated grams with mass balance constraints,nonlinear blend— ing properties and combinatorial aspects etc.The op— eration mixes two ent a or Currently many blending applications as are more components with a di乳r— extensions of linear blending problems.Linear pro— are properties to meet given specification to form


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