Optimal control of wastewater treatment plants via integrated neural network and genetic algorithms

The successful operation of wastewater treatment plants involves many uncertain factors. Not only the physical and chemical properties of wastewater streams but also the complexity of biological mechanisms would significantly influence the performance of treatment process. Due to the rising concerns of environmental and economic impacts, improved control algorithms, using artificial intelligence technologies, have gradually received wide attention in the scientific community. This paper develops a genetic algorithm-based neural network for the assistance of intelligent controller design. An industrial wastewater treatment plant in Taiwan verified the applicability of such a methodology. The hybrid intelligent control technology applied in this paper is suitable to many other types of wastewater treatment plants by a slightly modified concept.