Application of neural networks to water and wastewater treatment plant operation

Abstract

The application of artificial intelligence techniques to the operation of water and wastewater treatment plants in recent years is reviewed. The expert system approach is the most prevalent, but difficulties in acquiring and representing knowledge of the complex phenomena in these plants have led to the search for additional approaches. Fuzzy logic and statistical process control are used for formulating expert rules from plant historical operating data, but artificial neural networks, which can learn from examples, are believed to be a better solution for this task and for many additional problems encountered in the operation of the plants. Basic concepts of neural network organization and training are given as well as recent advances in learning speed improvement that have paved the way for easy application of this technique in large industrial plants. Current and future utilization of neural networks in areas of water and wastewater plant modelling, expert rule extraction, fault detection and diagnosis, plant and instrument monitoring, dynamic forecasting, and robust control are discussed. Examples are given from the application of neural networks to the operation of the Shafdan wastewater treatment plant in Israel. Some limitations of the neural network approach, together with ways of overcoming these limitations, are described. The overall conclusion is that we will soon see neural network techniques applied to achieve better plant operation.