Tan, W.W.Lu, F.Loh, A.P.Tan, K.C.ELECTRICAL & COMPUTER ENGINEERING2014-06-172014-06-172005-06Tan, 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.2004.11.00609521976https://scholarbank.nus.edu.sg/handle/10635/56664The 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.Evolutionary algorithmMulti-objective optimizationParameter identificationpH controlPID designArticle000228891400008