Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0376-7388(00)00440-3
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dc.titleMulti-objective optimization of membrane separation modules using genetic algorithm
dc.contributor.authorYuen, C.C.
dc.contributor.authorAatmeeyata
dc.contributor.authorGupta, S.K.
dc.contributor.authorRay, A.K.
dc.date.accessioned2014-10-09T09:57:05Z
dc.date.available2014-10-09T09:57:05Z
dc.date.issued2000-08-20
dc.identifier.citationYuen, C.C., Aatmeeyata, Gupta, S.K., Ray, A.K. (2000-08-20). Multi-objective optimization of membrane separation modules using genetic algorithm. Journal of Membrane Science 176 (2) : 177-196. ScholarBank@NUS Repository. https://doi.org/10.1016/S0376-7388(00)00440-3
dc.identifier.issn03767388
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/92162
dc.description.abstractHollow fiber membrane separation modules are used extensively in industry for a variety of separation processes. In most cases, conflicting requirements and constraints govern the optimal choice of decision (or design) variables. In fact, these optimization problems may involve several objectives, some of which must be maximized, while the others minimized simultaneously. Often, a set of equally good (non-dominated or Pareto optimal) solutions exist. In this study, a membrane separation module for the dialysis of beer has been taken as an example system to illustrate the multi-objective optimization of any membrane module. A mathematical model is first developed and 'tuned' using some experimental results available in the literature. The model is then used to study a few simple multi-objective optimization problems using the non-dominated sorting genetic algorithm (NSGA). Two objective functions are used: the alcohol removal (%) from the beer is maximized, while simultaneously minimizing the removal of the 'extract' (taste chemicals). Pareto optimal solutions are obtained for this module. It was found that the inner radius of the hollow fiber is the most important decision variable for most cases. Another optimization problem using the cost as the third objective function is also solved, using a combination of the ε-constraint method and NSGA. It is found that the Pareto solutions lie on a curve in the three-dimensional objective function space, and do not form a surface. Copyright (C) 2000 Elsevier Science B.V.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0376-7388(00)00440-3
dc.sourceScopus
dc.subjectBeer dialysis
dc.subjectGenetic algorithm
dc.subjectMembrane separation module
dc.subjectOptimization problem
dc.subjectPareto sets
dc.typeArticle
dc.contributor.departmentCHEMICAL & ENVIRONMENTAL ENGINEERING
dc.description.doi10.1016/S0376-7388(00)00440-3
dc.description.sourcetitleJournal of Membrane Science
dc.description.volume176
dc.description.issue2
dc.description.page177-196
dc.description.codenJMESD
dc.identifier.isiut000088820400004
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