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.

本文讨论了生物废水处理过程中溶解氧浓度的最佳可变设定点和设定点跟踪控制回路的设计。尽管对下雨/暴风雨期间进水变化的确切了解是不现实的，但我们利用了这样的事实，即在干燥天气条件下进水变化是周期性的，因此是可预测的。具体来说，非线性优化程序利用干旱天气数据来确定名义上的固定设定值或加权增益，或两者兼而有之。在天气事件期间，算法使用优化解决方案以及铵盐预测来动态调整设定点（适当地响应进水口的重大变化）。约束非线性神经网络模型的预测控制跟踪设定值。