Abstract
The operation of water treatment plants is significantly different from most manufacturing industrial operations because raw water sources are often subject to natural perturbations like flood and drought, both of which significantly affect the characteristics of the abstracted water. More recently, improved sensor technology has enabled the successful regulation of variables such as pH and chlorine residual. Without a precise knowledge of the characteristics of the material to be removed, most chemical dosage requirements for primary water treatment are determined from laboratory measurements which are conducted (usually) not less than once a day. This paper gives a brief explanation of water treatment plant operation, and outlines a number of case studies where system knowledge contained within artificial neural networks has been used to provide solutions to operational problems within the water industry. (6 pages)
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