Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2004.11.006
DC FieldValue
dc.titleModeling and control of a pilot pH plant using genetic algorithm
dc.contributor.authorTan, W.W.
dc.contributor.authorLu, F.
dc.contributor.authorLoh, A.P.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-06-17T02:57:10Z
dc.date.available2014-06-17T02:57:10Z
dc.date.issued2005-06
dc.identifier.citationTan, W.W., Lu, F., Loh, A.P., Tan, K.C. (2005-06). Modeling and control of a pilot pH plant using genetic algorithm. Engineering Applications of Artificial Intelligence 18 (4) : 485-494. ScholarBank@NUS Repository. https://doi.org/10.1016/j.engappai.2004.11.006
dc.identifier.issn09521976
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/56664
dc.description.abstractThe work described in this paper aims at exploring the use of computational intelligence (CI) techniques for designing a Wiener-model controller to perform pH control. First, genetic algorithm (GA) is utilized to identify the static inverse titration relationship of a weak-acid strong-base titration process. The resulting model of the inverse neutralization equation then serves as the component in a Wiener model controller that linearizes the pH process. As the bulk of the system non-linearity is cancelled by the inverse model, a setpoint-weighted Proportional plus Integral plus Derivative (PID) controller is used to generate the control signal. A multi-objective evolutionary algorithm (MOEA) is employed to evolve a pareto optimal set of PID parameters in order to achieve the conflicting goals of fast rise time with small overshoots. Experimental results obtained from a laboratory-scale acid-base titration process are then presented to demonstrate the feasibility of the design methodology. © 2004 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.engappai.2004.11.006
dc.sourceScopus
dc.subjectEvolutionary algorithm
dc.subjectMulti-objective optimization
dc.subjectParameter identification
dc.subjectpH control
dc.subjectPID design
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/j.engappai.2004.11.006
dc.description.sourcetitleEngineering Applications of Artificial Intelligence
dc.description.volume18
dc.description.issue4
dc.description.page485-494
dc.description.codenEAAIE
dc.identifier.isiut000228891400008
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