Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6594719 | Computers & Chemical Engineering | 2018 | 34 Pages |
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.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Mahsa Sadeghassadi, Chris J.B. Macnab, Bhushan Gopaluni, David Westwick,