Abstract Attempts to improve the performance of water treatment works through the application of improved control and measurement have had variable success. The most quoted reason for this is that the individual dynamic operations defming the treatment cycle are complex, highly non-linear and poorly understood. These problems are compounded by the use of faulty or badly maintained sensors. The efficient and robust operation of any industrial system is critically dependent on the quality of the measurements made. Also, the structure of the control policy and choice of the individual controller parameters are important decisions to the economic operation.Because of their ability to capture non-linear information very efficiently, artificial neural networks (ANNs) have found great popularity amongst the 'control community' and other disciplines. This paper discusses a recent application of ANNs at surface water treatment works. The study is used to describe how the introduction of ANNs has resulted in more reliable system measurement and consequently improved coagulation control
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