Oh Jeil, Bartos Matthew
Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin, Austin, 78712, TX, USA.
Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin, Austin, 78712, TX, USA.
Water Res. 2023 May 15;235:119825. doi: 10.1016/j.watres.2023.119825. Epub 2023 Mar 3.
Smart stormwater systems equipped with real-time controls are transforming urban drainage management by enhancing the flood control and water treatment potential of previously static infrastructure. Real-time control of detention basins, for instance, has been shown to improve contaminant removal by increasing hydraulic retention times while also reducing downstream flood risk. However, to date, few studies have explored optimal real-time control strategies for achieving both water quality and flood control targets. This study advances a new model predictive control (MPC) algorithm for stormwater detention ponds that determines the outlet valve control schedule needed to maximize pollutant removal and minimize flooding using forecasts of the incoming pollutograph and hydrograph. Comparing MPC against three rule-based control strategies, MPC is found to be more effective at balancing between multiple competing control objectives such as preventing overflows, reducing peak discharges, and improving water quality. Moreover, when paired with an online data assimilation scheme based on Extended Kalman Filtering (EKF), MPC is found to be robust to uncertainty in both pollutograph forecasts and water quality measurements. By providing an integrated control strategy that optimizes both water quality and quantity goals while remaining robust to uncertainty in hydrologic and pollutant dynamics, this study paves the way for real-world smart stormwater systems that will achieve improved flood and nonpoint source pollution management.
配备实时控制的智能雨水系统正在改变城市排水管理，通过提升先前静态基础设施的防洪和水处理潜力。例如，对滞洪池的实时控制已表明，通过增加水力停留时间，可提高污染物去除率，同时降低下游洪水风险。然而，迄今为止，很少有研究探索实现水质和防洪目标的最优实时控制策略。本研究提出了一种用于雨水滞洪池的新型模型预测控制（MPC）算法，该算法利用入流污染物浓度图和水文图的预测来确定所需的出水阀门控制计划，以最大化污染物去除并最小化洪水。将MPC与三种基于规则的控制策略进行比较，发现MPC在平衡多个相互竞争的控制目标（如防止溢流、降低峰值流量和改善水质）方面更有效。此外，当与基于扩展卡尔曼滤波（EKF）的在线数据同化方案相结合时，发现MPC对污染物浓度图预测和水质测量中的不确定性具有鲁棒性。通过提供一种综合控制策略，该策略在优化水质和水量目标的同时，对水文和污染物动态中的不确定性保持鲁棒性，本研究为实现改进的洪水和非点源污染管理的实际智能雨水系统铺平了道路。
