Open AccessAuto-Control Technique Using Gradient Method Based on Radial Basis Function Neural Networks to Control of an Activated Sludge Process of Wastewater Treatment
Author(s) -
Lemita Abdallah,
S. Boulahbel,
Sami Kahla,
Moussa Sedraoui
Publication year - 2020
Publication title -
journal européen des systèmes automatisés/journal européen des systèmes automaitsés
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.16
H-Index - 20
eISSN - 2116-7087
pISSN - 1269-6935
DOI - 10.18280/jesa.530510
Subject(s) - activated sludge , wastewater , artificial neural network , sewage treatment , aeration , radial basis function , gradient descent , computer science , environmental science , environmental engineering , engineering , waste management , artificial intelligence
Dissolved oxygen (DO) concentration is a key variable in the activated sludge wastewater treatment processes. In this paper, an auto control strategy based on Euler method and gradient method with radial basis function (RBF) neural networks (NNs) is proposed to solve the DO concentration control problem in an activated sludge process of wastewater treatment. The control purpose is to maintain the dissolved oxygen concentration in the aerated tank for having the substrate concentration within the standard limits established by legislation of wastewater treatment. For that reason, a new proposed control strategy based on gradient descent method and RBF neural network has been used. Compared with RBF neural network PI control, the obtained results show the effectiveness in terms of both transient and steady performances of proposed control method for dissolved oxygen control in the activated sludge wastewater treatment processes.