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

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响和商业潜力是为污水处理厂（WWTPs）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）和最终输出（流出）的化学成分，而不了解中间阶段。这种数据的缺乏可能对最终用户造成严重的环境和人类健康危害，并对污水处理厂处以监管罚款。该项目推进了先进的微生物分析技术，专门用于水处理，以降低能源、化学和财务成本，主动预测和预防负面影响。本项目具有全球性的适用性。该SBIR一期项目将结合：1)为水处理量身定制的先进微生物分析，包括DNA、RNA和系统微生物组谱的全球分析；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。本项目将提出并优化大规模输液的采样、分析和报告计划。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.