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

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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.