Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 115, Issue -, Pages 150-160Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2018.04.007
Keywords
BSM1; Neural network control; Model predictive control; Biological wastewater treatment
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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. (c) 2018 Elsevier Ltd. All rights reserved.
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