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

SBIR 第一阶段：水处理过程中的实时预测：预防环境/健康危害和降低运营成本的智能且主动的途径

• 批准号: 2126156
• 负责人: Young Lee
• 金额: $ 25.6万
• 依托单位: AdvanceH2O Corp.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126156&HistoricalAwards=false
• 关键词: SBIR; Phase; Real; time; Predictions

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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是为废水处理厂 (WWTP) 开发下一代监测和数据信息学。测试污水处理厂性能的行业标准通常测量进入废水（进水）和成品输出（出水）的化学成分，而不深入了解中间阶段。 数据的缺乏可能会对最终用户造成重大的环境和人类健康危害，并导致污水处理厂面临监管罚款。 该项目专门针对水处理推进了先进的微生物分析，以主动预测和防止负面影响，同时降低能源、化学品和财务成本。该项目具有全球应用性。 该 SBIR 第一阶段项目将结合：1）专为水处理量身定制的先进微生物分析，包括对系统微生物组 DNA、RNA 和概况的全局分析； 2) 人工智能 (AI)/机器学习 (ML)。 该项目根据先进的微生物分析（处理过程中的关键驱动因素）确定实时污水处理厂性能预测，为过程控制措施提供信息以优化工厂运营。对于高级微生物分析，目标是证明污水处理厂反应器中微生物生态系统的可靠特征，并帮助保持生态系统随时间的一致性和稳定性。 该项目将提出并优化大规模输注的采样、分析和报告计划。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。