A Physics-Based Artificial Intelligence General Framework for Optimal Control of Sewer Systems to Minimize Sewer Overflows

联合下水道系统将雨水径流、生活污水和工业废水收集在同一条管道中。在正常情况下，联合下水道系统将收集的废水输送到工厂，在那里进行处理，然后排放到地表水系统，包括河流、湖泊、河口和海洋。在强降雨或融雪期间，联合下水道系统输送的废水量有时会超过处理厂的能力，导致溢流到附近的溪流和地表水中。在美国，每年大约有8500亿加仑未经处理的污水溢出。这些污水溢出对环境和社区产生不利影响，包括污染饮用水源和关闭娱乐海滩。该项目的总体目标是开发和验证机器学习工具，以预测潜在下水道溢出的位置和体积。为了推进这一目标，首席研究员（pi）建议整合数据科学（大数据算法）、实验室规模实验和基于物理的数值模型，以加速机器学习模型的可用性，以指导和优化下水道系统的运行，目的是管理和减少下水道溢流对环境的影响。该项目的成功完成将通过发展基础知识和新的建模工具来支持管理和减少下水道溢流，从而造福社会。通过外联和教育活动，包括指导佛罗里达国际大学的两名研究生和六名本科生，将为社会带来额外的好处。由于气候变化，暴雨和洪水等极端天气事件的频率和强度增加，下水道溢流将变得更加频繁和严重。为了管理下水道溢流，需要新一代的超快模型来预测它们可能发生的时间和地点，以及在暴雨发生之前减少溢流所需的决策顺序。该项目的总体目标是整合人工智能（AI）、大数据和基于物理的数值模型，以加速机器学习（ML）模型的可用性，这些模型可用于预测、管理和减少下水道溢出。本研究的具体目标是：1)实现并验证现有的开源下水道流动动力学模型的下水道溢流模型，该模型目前无法模拟下水道溢流；2)开发一个通用的基于物理的人工智能开源框架，用于预测给定的固定操作场景（例如，闸门位置是固定的）的联合下水道溢出的位置和体积；3)开发一个人工智能开源框架，用于确定控制门的决策变量/场景的最佳序列，以最大限度地减少联合下水道溢出。除了上面列出的具体目标之外，首席研究人员（PI）建议将他们提出的新机器学习模型与各种开源的基于物理的模型结合和集成，以构建一个名为IMPACTO的新建模框架（用于预测下水道溢流和分析的集成建模，用于在封闭管道和隧道中优化控制闸门，以最大限度地减少溢流）。最后，pi建议通过训练IMPACTO来验证(1)预测两个现有下水道系统中合流下水道溢流的位置和体积，以及(2)确定控制门（例如流量）决策变量的最佳序列，以最小化下水道溢流。通过开发和验证一个集成的开源模型，该项目的成功完成具有变革性影响的潜力，该模型可用于预测、管理和减少合并下水道溢流的发生。为了实施该项目的教育和推广活动，pi计划与佛罗里达国际大学（FIU）车轮工程师项目合作，为代表性不足群体的中学开发一个教育模块，其中包括有关下水道溢出的实践活动。此外，ppi还提议利用FIU路易斯·斯托克斯少数民族参与联盟项目，从代表性不足的群体中招募六名本科生参与该项目。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A combined sewer system collects rainwater runoff, domestic sewage, and industrial wastewater in the same pipe. Under normal conditions, a combined sewer system transports the collected wastewater to a plant where it is treated and discharged to surface water systems including rivers, lakes, estuaries, and oceans. During heavy rainfall events or snowmelts, the volume of wastewater transported by a combined sewer system can sometimes exceed the treatment plant's capacity resulting in overflows into nearby streams and surface water bodies. Approximately 850 billion gallons of untreated combined sewer overflows are discharged every year in the United States. These sewer overflows have an adverse impact on the environment and communities including the contamination of drinking water sources and the closures of recreational beaches. The overarching goal of this project is to develop and validate machine learning tools to forecast the location and volume of potential sewer overflows. To advance this goal, the Principal Investigators (PIs) propose to integrate data science (big data algorithms), lab-scale experiments, and physics-based numerical models to accelerate the availability of machine learning models to guide and optimize the operation of sewer systems with the aim of managing and reducing the environmental impact of sewer overflows. The successful completion of this project will benefit society through the development of fundamental knowledge and new modeling tools to support the management and reduction of sewer overflows. Additional benefits to society will be achieved through outreach and educational activities including the mentoring of two graduate students and six undergraduate students at Florida International University.As the frequency and intensity of extreme weather events such as heavy rainfalls and flooding increase due to climate change, sewer overflows will become more frequent and severe. To manage sewer overflows, a new generation of ultrafast models are needed to predict when and where they are likely to occur, and the sequence of decisions needed to minimize overflows before heavy rainfall occurs. The overarching goal of this project is to integrate artificial intelligence (AI), big data, and physics-based numerical models to accelerate the availability of machine learning (ML) models that could be used to predict, manage, and reduce sewer overflows. The specific objectives of the research are to: 1) Implement and validate a sewer overflow model for an existing open-source sewer flow dynamics model that currently cannot simulate sewer overflows; 2) Develop a general physics-based AI open-source framework for predicting the location and volume of combined sewer overflows for a given fixed set of operational scenarios (e.g., gate positions are fixed); and 3) Develop an AI open-source framework for determining an optimal sequence of decision variables/scenarios at control gates for minimizing combined sewer overflows. In addition to the specific objectives listed above, the Principal Investigators (PI) propose to combine and integrate their proposed new ML model with various open-source physical-based models to build a new modeling framework named IMPACTO (Integrated Modeling for Prediction of sewer overflows and Analytics for optimal control of gates in Closed-conduits and Tunnels to minimize Overflows). Finally, the PIs propose to validate IMPACTO by training it to (1) predict the location and volume of combined sewer overflows in two existing sewer systems and (2) determine the optimal sequence of decision variables at control gates (e.g., flow discharges) to minimize sewer overflows. The successful completion of this project has the potential for transformative impact through the development and validation of an integrated, and open-source model that could be used to predict, manage, and reduce the occurrence of combined sewer overflows. To implement the education and outreach activities of the project, the PIs plan to develop an educational module with hands-on activities on sewer overflows for middle schools from underrepresented groups in collaboration with the Florida International University (FIU) Engineers on Wheel program. In addition, the PIs propose to leverage the FIU Louis Stokes Alliances for Minority Participation program to recruit six undergraduate students from underrepresented groups to work on the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.