Real-time Regulation of Detention Ponds via Feedback Control: Balancing Flood Mitigation and Water Quality
作者: Marcus Nóbrega Gomes, Ahmad F. Taha, Luis Miguel C. Rápallo, Eduardo M. Mendiondo, Marcio H. Giacomoni
分类: eess.SY
发布日期: 2024-03-07 (更新: 2024-08-14)
💡 一句话要点
提出实时反馈控制方法以平衡洪水缓解与水质改善
🎯 匹配领域: 支柱一：机器人控制 (Robot Control)
关键词: 滞留池 洪水管理 水质改善 模型预测控制 实时反馈控制 水文模型 生态工程
📋 核心要点
- 现有的滞留池通常采用被动控制，无法有效应对洪水和水质问题，存在性能不足的挑战。
- 论文提出了一种基于模型预测控制（MPC）的主动控制方法，能够实时优化滞留池的水力装置操作。
- 实验结果表明，MPC策略在洪水峰值流量降低方面表现出79%的提升，相较于被动控制，水质改善也更为显著。
📝 摘要（中文）
本文探讨了滞留池在洪水缓解和水质改善中的作用，提出了一种基于模型预测控制（MPC）的主动控制方法。通过对滞留池的水力装置进行实时优化，研究者开发了一种分布式准二维水文-水动力模型，并将其与MPC算法相结合，以估算阀门和可移动闸门的操作。案例研究表明，该方法在巴西圣保罗的流域中具有良好的应用潜力，MPC策略在减少峰值流量方面表现优异，相较于被动控制，能够实现79%的峰值流量降低，同时在水质改善方面也显示出优势。
🔬 方法详解
问题定义：本文旨在解决滞留池在洪水管理和水质改善中的不足，现有方法多采用被动控制，无法实现实时优化和灵活应对。
核心思路：通过引入模型预测控制（MPC）技术，实时调整滞留池的阀门和闸门操作，以提高洪水缓解和水质改善的效果。该设计允许系统根据实时数据动态调整，优化水流管理。
技术框架：整体架构包括分布式准二维水文-水动力模型与MPC算法的结合。模型负责模拟水流和污染物沉降，而MPC算法则用于实时优化阀门和闸门的开闭状态。
关键创新：最重要的创新在于将MPC应用于滞留池的实时控制，突破了传统被动控制的限制，实现了从洪水管理到水质改善的动态切换。
关键设计：在MPC算法中，设置了适应性目标函数，能够根据流域的实时水文数据调整控制策略，确保在不同情况下的最佳水流管理。
🖼️ 关键图片
📊 实验亮点
实验结果显示，MPC策略在模拟两次连续10年一遇的暴雨时，能够实现79%的峰值流量降低，而被动控制的性能仅为其一半。此外，MPC方法的平均滞留时间达到14小时，显著改善了水质。
🎯 应用场景
该研究的潜在应用领域包括城市洪水管理、生态水质改善和水资源优化配置。通过实时反馈控制，滞留池能够更有效地应对极端天气事件，提升城市的防洪能力和水环境质量，具有重要的实际价值和社会影响。
📄 摘要（原文）
Detention ponds can mitigate flooding and improve water quality by allowing the settlement of pollutants. Typically, they are operated with fully open orifices and weirs (i.e., passive control). Active controls can improve the performance of these systems: orifices can be retrofitted with controlled valves and spillways can have controllable gates. The real-time optimal operation of its hydraulic devices can be achieved with techniques such as Model Predictive Control (MPC). A distributed quasi-2D hydrologic-hydrodynamic coupled with a reservoir flood routing model is developed and integrated with an MPC algorithm to estimate the operation of valves and movable gates. The control optimization problem is adapted to switch from a flood-related algorithm focusing on mitigating floods to a heuristic objective function that aims to increase the detention time when no inflow hydrographs are predicted. The case studies show the potential of applying the methods developed in a catchment in Sao Paulo, Brazil. The performance of MPC compared to alternatives with either fully or partially open valves and gates are tested. Comparisons with HEC-RAS 2D indicate volume and peak flow errors of approximately 1.4% and 0.91% for the watershed module. Simulating two consecutive 10-year storms shows that the MPC strategy can achieve peak flow reductions of 79%. In contrast, passive control has nearly half of the performance. A 1-year continuous simulation results show that the passive scenario with 25% of the valves opened can treat 12% more runoff compared to the developed MPC approach, with an average detention time of approximately 6 hours. For the MPC approach, the average detention time is nearly 14 hours indicating that both control techniques can treat similar volumes; however, the proxy water quality for the MPC approach is enhanced due to the longer detention times.