Advanced process control techniques for water treatment using artificial neural networks. (1st April 2004)

• Record Type: Journal Article
• Authors: Shariff, Riyaz; Cudrak, Audrey; Zhang, Qing; Stanley, Stephen J
• Is Part Of: Journal of environmental engineering and science. Volume 3(2004)Supplement 1
• Page Start: S61
• Page End: S67
• Publication Date: 2004-04-01
• Subjects: environment; Environmental engineering -- Periodicals; Sanitary engineering -- Periodicals; Environmental engineering -- Canada -- Periodicals; Environnement, Technique de l' -- Périodiques; Technique sanitaire -- Périodiques; Environnement, Technique de l' -- Canada -- Périodiques; Environmental engineering; Sanitary engineering; Canada; Electronic journals; Computer network resources; Periodicals; 628.05
• DOI: 10.1139/s04-005
• Languages: English
• ISSNs: 1496-2551

Abstract

Abstract : Virtually all water utilities are looking at improving the operation of their plants to keep control of costs and to meet stringent water quality regulations. Better process control and automation of the plants can help achieve these goals. However, traditional control techniques such as proportional–integral–derivative (PID) can be inadequate when automating certain water treatment processes such as turbidity, organics, or hardness removal in a clarification process. Advanced process control techniques are alternatives to mitigate this impediment. At the cornerstone of many advanced process control techniques is a model of the process being controlled, which can be developed using artificial neural networks (ANNs). This paper describes various advanced process control techniques, the potentially large role of ANN models in implementing these techniques, and issues and solutions when using ANN in a real-time control system. Key words: artificial neural networks, model-based control, proportional-integral-derivative control, forward and inverse models, direct and indirect control.