SBIR Phase I: Real-Time Decision Making Software for Wastewater Treatment Operators

SBIR Phase I: Real-Time Decision Making Software for Wastewater Treatment Operators is sponsored by USDA National Agricultural Library. Development of machine learning and AI tools to enhance wastewater treatment efficiency.

SBIR Phase I: Real-Time Decision Making Software for Wastewater Treatment Operators | National Agricultural Library SBIR Phase I: Real-Time Decision Making Software for Wastewater Treatment Operators The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is the development of a new generation of machine learning/artificial intelligence tools for improving the efficiency and effectiveness of wastewater treatment systems.

Over $160 billion is spent on sewer fees in the U.S., with year-to-year costs increasing by over 5% for many users. Even with the significant resources spent on wastewater treatment, 3 to 10 billion gallons of untreated sewage are still released from US wastewater treatment plants each year.

Development of technologies leveraging machine learning and artificial intelligence to better manage complex biological and chemical processes will not only have a major impact on the $91 billion wastewater treatment system control market, but on other biochemical-dependent industries as well.

In addition to reducing the societal financial burden associated with wastewater treatment, this technology will improve the sustainability of the infrastructure and will limit the environmental impact of human activities.

Wastewater operators will be able to utilize this technology's real-time decision-making software to significantly reduce their municipal facility operating costs and decrease environmental pollution caused by non-compliance and overflows. <br/><br/><br/>This SBIR Phase I project proposes to develop real-time software for assisting wastewater treatment operators with decision-making for improved efficiency and effectiveness.

Existing commercial simulation solutions for control and monitoring do not accurately reflect actual treatment plant behavior, do not model biological processes, do not require extensive configuration to be used, and do not respond rapidly to changes in plant performance.

This project improves upon current approaches by linking biological components of the wastewater treatment plant with historical data using machine learning techniques.

Phase I research will focus on development of: 1) an influent flow/composition model allowing accurate model inputs; 2) a full-scale hybrid model combining physical process and machine-learning bioprocess modules able to accurately predict plant effluent flow and quality, and; 3) a software platform to manage the model processes.

The technical approach will focus on balancing the complexity of incorporating large-scale genomic data as part of a non-linear treatment process while ensuring high accuracy and maintaining model stability.

Anticipated technical results will provide waste operators a software product that develops full-scale treatment plant models in real-time, using historical and current plant data, enabling significantly improved operational decision-making. <br/><br/>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.

United States Nat'l. Science Fndn.