Information processing coupled with expert systems for water treatment plants

Abstract The water utility industry is facing increased pressures to produce higher quality finished water at lower costs. The traditional feedforward-feedback control systems used in water treatment plants can be enhanced by using predictive models (with feedforward control) and diagnostic expert systems (with feedback control). The use of predictive models is advantageous as it is virtually impossible to ascertain specific chemical dose requirements from measurements of raw water quality parameters. These predictive models may be either regression relationships or neural networks. When feedback data indicate that the finished water quality is not acceptable and usual corrective actions based on feedback from sensors fails to correct the situation, diagnostic expert systems can be effectively used to aid operator decision making.