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

SBIR 第一阶段：供水系统实时数据融合：在线自动水质模型估算的创新

• 批准号: 1315672
• 负责人: Stuart Hooper
• 金额: $ 15万
• 依托单位: CitiLogics, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1315672&HistoricalAwards=false
• 关键词: SBIR; Phase; Real; Time; Data

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.

这项创新将通过融合运营数据和基础设施感知预测模型，为大规模水利基础设施系统提供实时决策支持。这些实时软件工具将利用饮用水公用事业公司在支持运营决策的监控和数据采集(SCADA)系统以及支持基础设施规划的地理信息系统和基础设施模型方面的重大投资；这些投资应该而且可以被用来支持更广泛的公用事业决策。这项SBIR第一阶段提案侧重于与供水系统内水质演变的实时预测相关的商业技术。以往对网络水质模型的现场规模评估存在以下问题：反应动力学理想化；数据收集周期短；以及参数的显著不确定性和变异性。通过融合运行数据和基础设施感知的预测模型，这项研究将首次对复杂网络水质模型的保真度进行严格评估。受这项研究启发的数据融合软件工具将支持运行决策与实时数据以及模型预测和预报的透明集成，从而提高目前只能预期的水质效益。将实时运行数据与基础设施感知的预测模型融合的商业影响和实际好处将通过简单而准确地实时预测配电系统水力和水质的能力来实现。这项技术将允许操作员常规地进行情景响应培训，并进行操作分析，以实现实际的水质管理目标？例如维持最低氯残留量或控制消毒副产品。工程师将以协作的方式将他们的基础设施知识应用于这些任务，同时知道他们的基础设施模型通过对运营记录的持续解释而不断更新。管理人员将审查仪表板和自动报告，显示下落不明的水、能源使用和水质的趋势，并将这些与过去和未来的资产管理决策相结合。该项目第一阶段和第二阶段的成功完成将检验供水系统实时数据融合的价值主张，特别是实时网络水质预测和预报。实时数据融合优势和相关工作流程将在实际运营环境中接受测试，并由新的数据融合系统提供支持，该系统可在整个组织内实现高效的公用事业工作流程。