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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:ADVANCEH2O CORP.
Doing Business As Name:AdvanceH2O Corp.
PD/PI:
  • Young Lee
  • (617) 549-9817
  • younglee180@gmail.com
Award Date:07/29/2021
Estimated Total Award Amount: $ 256,000
Funds Obligated to Date: $ 256,000
  • FY 2021=$256,000
Start Date:08/01/2021
End Date:07/31/2022
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:SBIR Phase I: Real-time Predictions During Water Treatment: An Intelligent and Proactive Pathway to Preventing Environmental/Health Hazards and Reducing Operational Costs
Federal Award ID Number:2126156
DUNS ID:080674140
Program:SBIR Phase I
Program Officer:
  • Rajesh Mehta
  • (703) 292-2174
  • rmehta@nsf.gov

Awardee Location

Street:160 Riverside Blvd
City:New York
State:NY
ZIP:10069-0701
County:New York
Country:US
Awardee Cong. District:10

Primary Place of Performance

Organization Name:AdvanceH2O Corp.
Street:160 Riverside Blvd. No. 22E
City:New York
State:NY
ZIP:10069-0701
County:New York
Country:US
Cong. District:10

Abstract at Time of Award

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.

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