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Find similar grantsSBIR Phase I: Real-Time Decision Making Software for Wastewater Treatment Operators is sponsored by National Science Foundation. This grant supports the development of machine learning tools to enhance the efficiency and effectiveness of wastewater treatment.
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SBIR Phase II: Real-Time Decision Making Software for Wastewater Treatment Operators - National Science Foundation SBIR Phase II: 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 real-time software for wastewater facilities to improve nutrient removal and recovery at reduced costs.
The technology developed through this SBIR project will provide a proactive monitoring process that allows wastewater operators to observe and diagnose future process upsets, proactively mitigate underlying root causes, and prevent pollutant release without the use of expensive and environmentally damaging chemicals.
Improvements in treatment effectiveness and reduction of operating and maintenance costs will limit the environmental impact of human activities, improve sustainability of wastewater treatment infrastructure, ensure public health, and reduce financial burdens associated with wastewater treatment. Following deployment individual facilities may see annual commercial savings upwards of $1.
4 M per large facility from improved compliance and reduction in chemical costs in a wastewater services, a market opportunity estimated at upwards of $420 M in the United States. This project could lead to 35% improvement in regulatory compliance, 35% reduction in chemical treatment costs, and a guidance system for inexperienced operators in an industry expecting 50% of its operator workforce to retire over the next 5-10 years.
In addition, the project will develop a game-based training program to train new operators in the skill sets to lead operation of sophisticated facilities. This SBIR Phase II project proposes to further the development of a software platform that uses available operational, biological, and meteorological data as inputs to deliver process forecasts and insights regarding biological phosphorus removal to operators.
Machine-learning forecast models will be the basis of an attribution-based inference and decision-making system used for diagnosis and mitigation of upsets to the notoriously unstable biological phosphorus removal process. In this project, data systems of a full-scale wastewater facility will be synced with the software platform to deliver real-time results that will be evaluated over 12 months of pilot testing.
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. NSF Program Director: Anna Brady-Estevez Status Closed Effective start/end date 08/15/20 → 02/28/23 SBIR Phase II: $883,713.
00 Machine Learning Training Data Congressional District at Award Current Congressional District Core Based Statistical Area (CBSA) https://www. nsf. gov/awardsearch/showAward?
AWD_ID=2025902 Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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According to the current listing, eligibility includes: Small businesses specializing in environmental technology. Confirm the full requirements in the official notice before applying.
The current listing shows up to $150,000. Verify award ceilings, matching requirements, and allowable costs in the official notice.
SBIR Phase I: Real-Time Decision Making Software for Wastewater Treatment Operators is funded by National Science Foundation. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
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