Effluent Quality-Aware Event-Triggered Model Predictive Control for Wastewater Treatment Plants

Abstract

Wastewater treatment plants (WWTPs) are large-scale and nonlinear processes with tightly integrated operating units. The application of online optimization-based control strategies, such as model predictive control (MPC), to WWTPs generally faces high computational complexity. This paper proposes an event-triggered approach to address this issue. The model predictive controller updates information and solves the optimization problem only when the corresponding triggered logic is satisfied. The triggered logic sets the maximum allowable deviation for the tracking variables. Moreover, to ensure system performance, the design of the event-triggered logic incorporates the effluent quality. By obtaining the optimal sequence for the effluent quality within the receding horizon of the MPC, the cumulative deviation between the predicted and desired effluent quality is analyzed to evaluate the performance within that horizon. Based on these two conditions, the need for adjusting control actions is determined. Even if the maximum allowable range for the tracking variables in the triggered logic design is set unreasonably, the consideration of effluent quality factors in the triggered conditions ensures good performance. Simulation results demonstrate an average reduction in computational effort of 25.49% under different weather conditions while simultaneously ensuring minimal impact on the effluent quality and total cost index and compliance with effluent discharge regulations. Furthermore, this method can be combined with other approaches to guarantee effluent quality while further reducing computation time and complexity.