SBIR Phase I: Real-Time Data Fusion for Water Supply Systems: Innovations for Online Automated Water Quality Model Estimation

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1315672
Agency Tracking Number: 1315672
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2013
Solicitation Year: 2012
Solicitation Topic Code: EI
Solicitation Number: N/A
Small Business Information
CitiLogics, LLC
615 Madison Avenue, Covington, KY, 41011
DUNS: 832354752
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Stuart Hooper
 (513) 550-7681
Business Contact
 Stuart Hooper
Phone: (513) 550-7681
Research Institution
The innovation will enable real-time decision support for large-scale water infrastructure systems, through the fusion of operational data and infrastructure-aware predictive models. These real-time software instruments will leverage significant investments by drinking water utilities in Supervisory Control and Data Acquisition (SCADA) systems that support operational decisions and Geographic Information Systems and infrastructure models that support infrastructure planning; these investments should and can be leveraged to support a wider scope of utility decision making. This SBIR Phase I proposal focuses on the commercial technology associated with real-time predictions of water quality evolution within water supply systems. Previous field scale evaluations of network water quality models have suffered from idealized reaction kinetics; short data collection periods; and significant parameter uncertainty and variability. By fusing operational data and infrastructure-aware predictive models, this study will, for the first time, develop a rigorous assessment of the fidelity of complex network water quality models. The data fusion software instruments inspired by this research will support the transparent integration of operational decisions with real-time data and model predictions and forecasts, leading to enhanced water quality benefits that, at present, can only be anticipated. The commercial impact and practical benefits of fusing real-time operational data with infrastructure-aware predictive models will be enabled by the ability to simply and accurately forecast distribution system hydraulics and water quality, in real-time. This technology will allow operators to routinely engage in situational response training, and conduct operational analyses to achieve practical water quality management goals ? such as maintenance of minimum chlorine residuals or control of disinfection byproducts. Engineers will apply their infrastructure knowledge to these tasks in a collaborative fashion, while knowing their infrastructure models are continuously updated through a persistent interpretation of the operational record. Managers will review dashboards and automated reports showing trends in unaccounted for water, energy usage, and water quality, and integrate those with past and future asset management decisions. Successful completion of Phases I and II of this project will test the value proposition for real-time data fusion for water supply systems, specifically for real-time network water quality prediction and forecasts. Real-time data fusion benefits and associated workflows will be put to the test in actual operating environments, supported by a new data fusion system that allows for efficient utility workflows across the organization.

* information listed above is at the time of submission.

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