Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment. (12th July 2018) - Sadeghassadi, Mahsa; Macnab, Chris J.B.; Gopaluni, Bhushan; Westwick, David
Highlights: Design of an optimal variable setpoint and a setpoint-tracking control loop for the dissolved oxygen concentration in the BSM1 benchmark. Design of a nominal optimal setpoint for the dry weather conditions by solving a nonlinear optimization problem, which minimizes the pollution or the energy usage or both. Design of a novel algorithm that adjusts the setpoint dynamically during weather events (responding appropriately to significant changes in the influent). Design of a constrained nonlinear model predictive control that tracks the designed setpoint. Abstract: This paper addresses both the design of an optimal variable setpoint and a setpoint-tracking control loop for the dissolved oxygen concentration in a biological wastewater treatment process. Although exact knowledge of influent changes during rain/storm events is unrealistic, we take advantage of the fact that during dry weather conditions the influent changes are periodic and thus predictable. Specifically, a nonlinear optimization procedure utilizes dry weather data to decide on a nominal fixed setpoint, or a weighting gain, or both; during weather events an algorithm uses the optimization solution(s) together with the ammonium predictions to adjust the setpoint dynamically (responding appropriately to significant changes in the influent). A constrained nonlinear neural-network model predictive control tracks the setpoint. Simulations with the BSM1 compare several variations of the proposed methods to a fixed-setpoint PI control, demonstrating improvement in effluent quality or reduction in energy use, or both.