SBIR Phase I: Human-Centered, Augmented Intelligence Software for Water and Wastewater

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from development of augmented intelligence software improving planning and operational decisions for water and wastewater systems. The most challenging issues for water utilities include addressing aging infrastructure, adapting complex systems to changing regulations, and addressing the impacts of environmental change. They arise from interconnected infrastructure networks that interface with both the built and natural environment. Current artificial intelligence and machine learning solutions for water are narrowly defined for specific use cases. The proposed intelligence software will enable transformative changes in water management by seamless composition of hybrid models from simulators and data-driven models to overcome data and information silos, enabling decision-makers to integrate data in a system model that increases resilience at reduced customer costs. These improvements can lead to significant reductions in the roughly $4.7 B annual energy spend for water/wastewater, $50 B in combined sewer system programs, and up to $1 T in aging infrastructure needs.This Small Business Innovation Research (SBIR) Phase I project will develop methods for combining multi-fidelity simulation models and data-driven models to support decision-making for both long-term planning needs and real-time operational decision support for water and wastewater systems. Meta-modeling techniques for embedding physical system understanding from high-fidelity physics-based simulators to low-fidelity models will be evaluated. Accuracy and runtime tradeoffs will be evaluated for multiple reduced-order methods (e.g. linear and non-linear equations, projection-based methods) to enable more efficient optimization of large solution spaces. Domain applications include reduced-order versions of the St Venant equations for one-dimensional flow, and analytical solutions of biological, physical, and chemical processes in secondary wastewater treatment. The project will evaluate multiple machine learning methods, including deep neural networks, reinforcement learning, random forest, support vector machines, and boosted learning algorithms, to detect patterns in observed data for near-term predictive power toward operational real-time decisions. Expert elicitation techniques will be used to quantify human expertise for subjective decision criteria, integrating valuable tacit human knowledge into the decision process. Meta-analysis of alternative hybrid modeling workflows will be evaluated to identify computationally efficient pathways to optimize complex planning challenges.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.