Gradient Descent Optimization Control of an Activated Sludge Process based on Radial Basis Function Neural Network

Abstract

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.

Numeric Validation of the Inversion Model of Electrical Resistivity Imaging Method using the Levenberg-Marquardt Algorithm

“…Levenberg-Marquardt is an effective algorithm for nonlinear regression models [11][12][13]. It strategically combines Gradient Descent [14,15] and Gauss-Newton approaches [16,17]. When parameters are distant from the optimal values, the algorithm leverages a Gradient Descent-like search to approach global optimum territory while avoiding entrapment in suboptimal local extrema.…”