Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/92155
DC FieldValue
dc.titleMultiobjective optimization of a free radical bulk polymerization reactor using genetic algorithm
dc.contributor.authorGarg, S.
dc.contributor.authorGupta, S.K.
dc.date.accessioned2014-10-09T09:56:56Z
dc.date.available2014-10-09T09:56:56Z
dc.date.issued1999-01
dc.identifier.citationGarg, S.,Gupta, S.K. (1999-01). Multiobjective optimization of a free radical bulk polymerization reactor using genetic algorithm. Macromolecular Theory and Simulations 8 (1) : 46-53. ScholarBank@NUS Repository.
dc.identifier.issn10221344
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/92155
dc.description.abstractA multiobjective optimization technique has been developed for free radical bulk polymerization reactors using genetic algorithm. The polymerization of methyl methacrylate in a batch reactor has been studied as an example. The two objective functions which are minimized are the total reaction time and the polydispersity index of the polymer product. Simultaneously, end-point constraints are incorporated to attain desired values of the monomer conversion (xm) and the number average chain length (μn). A nondominated sorting genetic algorithm (NSGA) has been adapted to obtain the optimal control variable (temperature) history. It has been shown that the optimal solution converges to a unique point and no Pareto set is obtained. It has been observed that the optimal solution obtained using the NSGA for multiobjective function optimization compares very well with the solution obtained using the simple genetic algorithm (SGA) for a single objective function optimization problem, in which only the total reaction time is minimized and the two endpoint constraints on xm and μn are satisfied. © Wiley-VCH Verlag GmbH, 1999.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCHEMICAL & ENVIRONMENTAL ENGINEERING
dc.description.sourcetitleMacromolecular Theory and Simulations
dc.description.volume8
dc.description.issue1
dc.description.page46-53
dc.description.codenMTHSE
dc.identifier.isiutNOT_IN_WOS
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