ANN-based sensing and control developments in the water industry: a decade of innovation

Abstract

Compared to other process industries, the technology employed by the water industry is of a relatively low level. In general, however, methods of process regulation are far from ideal, leading to inefficient plant operation, occurrence of unnecessary costs and in some cases low water quality. Improvements in control and supervision methods have been recognised as one means of achieving higher water quality and efficiency objectives in the potable water industry. 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 defining the treatment cycle are complex, highly non-linear and poorly understood. These problems are compounded by the use of faulty or badly maintained sensors. 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. The paper discusses an 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.