Use of fuzzy neural-net model for rule generation of activated sludge process

Activated sludge plant is usually difficult to operate and control because of its complex operational behaviour and usual significant process disturba nces. To increase safety and improve operating performance of this biologic al wastewater treatment process, it is important to develop computer operat ional decision support systems. This intelligent computing system is able t o assist ordinary operators to work at the level of a domain expert in dail y operation. Neural network techniques and fuzzy logic methods have become important and effective tools to help build such intelligent system. The ar tificial neural network technique is powerful because it can learn to repre sent complicated data patterns or data relationships between input and outp ut variables of the system being studied. Nevertheless, it has limitations in performing heuristic reasoning of the domain problem. On the other hand, expert systems are good at performing heuristic reasoning by making use of logic rules. It is, however, generally weak for knowledge acquisition. In this study, a fuzzy neural model is developed for addressing the operating problems of activated sludge processes, relating to prediction and heuristi c understanding of the sludge age. Neural network techniques and fuzzy logi c are used in model development. Simulation studies show that this fuzzy-ne ural network model obtained is able to extract fuzzy rules from a set of nu merical data that can be used to carry out heuristic reasoning, (C) 1999 El sevier Science Ltd. All rights reserved.