SBIR Phase I: Real-time Predictions During Water Treatment: An Intelligent and Proactive Pathway to Preventing Environmental/Health Hazards and Reducing Operational Costs

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I Project is to develop next-generation monitoring & data informatics for wastewater treatment plants (WWTPs). Industry standards to test WWTP performance typically measure the chemistry of the incoming wastewater (influent) and finished output (effluent), without insight into the intervening stages. This lack of data can result in significant environmental and human health hazards for end-users, as well as regulatory fines for WWTPs. This project advances advanced microbial analytics specifically for water treatment to proactively predict and prevent negative impacts at reduced energy, chemical, and financial cost. This project has global application. This SBIR Phase I Project will combine: 1) Advanced microbial analytics tailor-made for water treatment, including global analysis of DNA, RNA, and profiles from the system microbiomes; and 2) Artificial Intelligence (AI)/Machine Learning (ML). This project identifies real-time WWTP performance predictions based on advanced microbial analytics (key drivers during treatment) to inform process control measures to optimize plant operations. For advanced microbial analytics, the objective is to prove reliable characterizations of microbial ecosystems in WWTP reactors, and to help maintain consistency and stability of the ecosystems over time. This project will propose and optimize a sampling, analysis, and reporting plan for infusion at scale. 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.