IMPLEMENTATION OF FUZZY NEURAL NETWORK IN ACTIVATED SLUDGE PROCESS OF THE WASTEWATER TREATMENT PLANT

Wastewater treatment plants play an important role in maintaining water quality and preserving the environment. The problem addressed in this study is the inefficiency of controller of the activate sludge process due to high energy consumption of the activated sludge process, lack of adaptability of the default controller, and strict effluent quality compliance set by local and national authorities. The objectives of this research are to develop an effective control strategy for the activated sludge process in tank 5 and to enhance the overall performance of the wastewater treatment plant. The proposed method of research utilizes a fuzzy neural network to model and optimize the control parameter of tank 5 which is the oxygen transfer coefficient. The proposed control strategy combines the benefits of fuzzy logic and neural network techniques to provide robust and adaptive control in complex and uncertain wastewater treatment systems. The modelling of the proposed controller is by employing the data of default controller. The outcomes of this study are expected to include improved process efficiency, enhanced treatment quality, reduced operational costs, and minimized environmental impact. The results will provide valuable insights for the wastewater treatment plant operators and contribute to the advancement of control strategies in wastewater treatment systems