Most systems in science and engineering can be described in the form of ordinary differential equations, but only a limited number of these equations can be solved analytically. For that reason, numerical methods have been used to get the approximate solutions of differential equations. Among these methods, the most famous is the Euler method. In this paper, a new proposed control strategy utilizing the Euler and the gradient method based on Radial Basis Function Neural Network (RBFNN) model have been used to control the activated sludge process of wastewater treatment. The aim was to maintain the Dissolved Oxygen (DO) level in the aerated tank and have the substrate concentration Chemical Oxygen Demand (COD5) within the standard limits. The simulation results of DO show the robustness of the proposed control method compared to the classical method. The proposed method can be applied in wastewater treatment systems.

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.

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