Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.engappai.2004.11.006
DC Field | Value | |
---|---|---|
dc.title | Modeling and control of a pilot pH plant using genetic algorithm | |
dc.contributor.author | Tan, W.W. | |
dc.contributor.author | Lu, F. | |
dc.contributor.author | Loh, A.P. | |
dc.contributor.author | Tan, K.C. | |
dc.date.accessioned | 2014-06-17T02:57:10Z | |
dc.date.available | 2014-06-17T02:57:10Z | |
dc.date.issued | 2005-06 | |
dc.identifier.citation | Tan, 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.issn | 09521976 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/56664 | |
dc.description.abstract | The 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.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.engappai.2004.11.006 | |
dc.source | Scopus | |
dc.subject | Evolutionary algorithm | |
dc.subject | Multi-objective optimization | |
dc.subject | Parameter identification | |
dc.subject | pH control | |
dc.subject | PID design | |
dc.type | Article | |
dc.contributor.department | ELECTRICAL & COMPUTER ENGINEERING | |
dc.description.doi | 10.1016/j.engappai.2004.11.006 | |
dc.description.sourcetitle | Engineering Applications of Artificial Intelligence | |
dc.description.volume | 18 | |
dc.description.issue | 4 | |
dc.description.page | 485-494 | |
dc.description.coden | EAAIE | |
dc.identifier.isiut | 000228891400008 | |
Appears in Collections: | Staff Publications |
Show simple item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.