A hybrid genetic algorithm to optimize simple distillation column sequences

作     者：GANYongSheng AndreasLinninger 

作者机构：ShanghaiResearchInstituteofPetrochemicalTechnologyShanghai201208China LaboratoryofProductandProcessDesignDepartmentofChemicalEngineeringUniversityofIllinoisatChicagoILUSA60607 

出 版 物：《计算机与应用化学》 (Computers and Applied Chemistry)

年 卷 期：2004年第21卷第3期

页      面：321-328页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 081701[工学-化学工程] 0703[理学-化学] 

主　　题：化工计算 遗传算法 蒸馏柱 分离次序 最优化 

摘      要：Based on the principles of Genetic Algorithms (GAs), a hybrid genetic algorithm used to optimize simple distillation column sequences was established. A new data structure, a novel arithmetic crossover operator and a dynamic mutation operator were proposed. Together with the feasibility test of distillation columns, they are capable to obtain the optimum simple column sequence at one time without the limitation of the number of mixture components, ideal or non-ideal mixtures and sloppy or sharp splits. Compared with conventional algorithms, this hybrid genetic algorithm avoids solving complicated nonlinear equations and demands less derivative information and computation time. Result comparison between this genetic algorithm and Underwood method and Doherty method shows that this hybrid genetic algorithm is reliable.