Adaptive Event-Triggered Control for Wastewater Treatment Process Using Self-Organizing Fuzzy Neural Network

Abstract

In a wastewater treatment process (WWTP), which covers a variety of physical, chemical, and biological treatment steps, it is a challenging control task to maintain proper dissolved oxygen (DO) and nitrate nitrogen (NO) concentrations to meet effluent standards. This paper proposes an event-triggered adaptive control method using a self-organizing fuzzy neural network (ETSOFNN) to regulate DO and NO concentrations. Moreover, a self-organizing fuzzy neural network (SOFNN) based on the maximum correlation entropy-induced criterion identifies and approximates the nonlinear function, which further enables the dynamic adjustment of the controller structure, including the addition or deletion of parameters. In addition, a dynamic event-triggered mechanism with a relative threshold strategy is introduced into the controller, and the trigger conditions are designed according to the tracking error. Utilizing Lyapunov stability theory, the stability of the control system is demonstrated. Finally, simulations based on the benchmark simulation model 1 (BSM1) platform are conducted to verify the effectiveness of the ETSOFNN method. Note to Practitioners—In this paper, the aim is to develop an online tracking and control method for controlling dissolved oxygen (DO) concentration and nitrate nitrogen (NO) concentration in wastewater treatment process (WWTP) and to make it applicable to wastewater treatment plants under a wide range of operating conditions. Due to the dynamic and complex nature of WWTP, which involves multiple treatment units and control variables, as well as characteristics such as strong coupling, non-Gaussian characteristics, and significant nonlinearity, there are considerable challenges in accurately establishing mathematical models. Therefore, this paper investigates a dynamic event-triggered mechanism and utilizes a self-organizing fuzzy neural network (SOFNN) applied to the control method of WWTP. Firstly, a mathematical model of WWTP is established. Secondly, using the SOFNN based on correlation entropy compensation, the structure of the controller is automatically constructed, which enables more accurate adjustment depending on the actual working conditions to improve the robustness and adaptability of the controller. Next, a dynamic event-triggered strategy is applied in the controller to reduce the number of controller updates and thus reduce the communication cost. Finally, the results of simulation experiments show that this control method performs well, and the strategy will be extended to actual wastewater treatment plants in the future to optimize the operation results.