Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2004.11.006
Title: Modeling and control of a pilot pH plant using genetic algorithm
Authors: Tan, W.W. 
Lu, F.
Loh, A.P. 
Tan, K.C. 
Keywords: Evolutionary algorithm
Multi-objective optimization
Parameter identification
pH control
PID design
Issue Date: Jun-2005
Source: 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
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.
Source Title: Engineering Applications of Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/56664
ISSN: 09521976
DOI: 10.1016/j.engappai.2004.11.006
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