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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 5638632, 7 pages
Research Article

A Predictive Neural Network-Based Cascade Control for pH Reactors

1College of Engineering at Wadi Aldawaser, Prince Sattam bin Abdulaziz University, P.O. Box 54, Wadi Aldawaser 11991, Saudi Arabia
2Chemical Engineering Department, School of Engineering, University of Bradford, Bradford, West Yorkshire BD7 IDP, UK

Received 8 May 2016; Revised 7 July 2016; Accepted 25 July 2016

Academic Editor: Qingsong Xu

Copyright © 2016 Mujahed AlDhaifallah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper is concerned with the development of predictive neural network-based cascade control for pH reactors. The cascade structure consists of a master control loop (fuzzy proportional-integral) and a slave one (predictive neural network). The master loop is chosen to be more accurate but slower than the slave one. The strong features found in cascade structure have been added to the inherent features in model predictive neural network. The neural network is used to alleviate modeling difficulties found with pH reactor and to predict its behavior. The parameters of predictive algorithm are determined using an optimization algorithm. The effectiveness and feasibility of the proposed design have been demonstrated using MatLab.