Search

Abstract: Being an optimizing technology, model predictive control (MPC) can now be found in a wide variety of application fields. The main and most obvious control goal to be achieved in a wastewater treatment plant is to fulfill the effluent quality standards, while minimizing the operational costs. In order to maintain the effluent quality within regulation-specified limits, the MPC strategy has been applied to the Benchmark Simulation Model 1 (BSM1) simulation benchmark of wastewater treatment process. After the discussion of open loop responses of outputs to manipulated inputs and measured influent disturbances, the strategies of feedback by linear dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC) and nonlinear model predictive control (NLMPC), and improvement by feedforward based on influent flow rate or ammonium concentration have been investigated. The simulation results indicate that good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. Compared to DMC and QDMC, NLMPC with penalty function brings little improvement. Two measured disturbances have been used for feedforward control, the influent flow rate and ammonium concentration. It is shown that the performance of feedforward with respect to the influent ammonium concentration is much higher than for the feedforward with respect to the influent flow rate. However, this latter is slightly better than the DMC feedback. The best performance is obtained by combining both feedforward controllers with respect to the influent ammonium concentration and flow rate. In all cases, the improvement of performance is correlated with more aeration energy consumption. [Copyright &y& Elsevier]

A linear, low-order input/output model is identified for a nonlinear distributed parameter system using a combination of singular value decomposition and the Karhunen Loève expansion. The model captures the dominant behavior of the system around a nominal operating point. A quadratic dynamic model-based controller (QDMC) is designed based on this low-order model. Sufficient conditions for closed-loop stability are presented and proven. The system identification method and the resulting QDMC controller are demonstrated on a nonlinear multiple-input multiple-output (MIMO) chemical reactor that produces benzene from the hydro-dealkylation of toluene. [Copyright &y& Elsevier]