SBIR Phase I: Advanced fouling detection for district cooling facilities treated with a novel nano-engineered surface treatment

这一小型企业创新研究(SBIR)项目的广泛影响/商业潜力将通过对系统运行进行最先进的监控和分析来减少区域冷却(DC)工厂的能源需求。与传统的建筑空调设备相比，区域冷却系统是一种更高效、更具成本效益的跨校园冷水生产方式。直流系统中的污垢积累是环境、水源和运行条件不可避免的副产品；它会导致效率低下，从而导致电力成本增加。该项目的目的是使用机器学习、基于算法的系统性能指标(SPI)和减少污垢积累和改善传热的纳米材料来提高对污垢的理解和分析。SPI将为DC操作员提供价值，使他们能够在维护协议中主动而不是被动，这将提高其冷却系统的效率并降低其运营成本。总而言之，这一创新的技术方案将优化DC运营，并有助于减少能源需求和排放。一座平均规模的直流电厂每年可节省约300,000美元。SBIR第一期项目建议通过以下活动提高区域制冷(DC)系统的效率：1)将机器学习技术集成到DC系统数据分析中，以生成系统性能指标(SPI)。SPI不会发出污垢已经发生的信号，而是会预测污垢的开始，以发出需要用纳米材料重新处理的信号，并进行其他维护。2)通过集成用于系统性能诊断的附加传感器来优化系统。拟议的项目将对一个系统进行验证和确认，该系统可以在没有完整的传感器套件的情况下改装单个诊断传感器。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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) project will reduce the energy demands of district cooling (DC) plants via state-of-the-art monitoring and analysis of system operations. District cooling systems are a more efficient, cost-effective way to produce chilled water across campuses compared to traditional building air conditioning units. Fouling accumulation in DC systems is an inevitable byproduct of environment, water source, and operating conditions; it contributes to inefficiencies that lead to increased power costs. This project’s aim is to improve the understanding and analysis of fouling using a machine learning, algorithm-based system performance indicator (SPI) in conjunction with a nanomaterial that reduces fouling accumulation and improves heat transfer. The SPI will provide value to DC operators by enabling them to be proactive instead of reactive in their maintenance protocols, which will improve the efficiency of their cooling systems and reduce their operating costs. Together, this innovative technology package will optimize DC operations and contribute to reduced energy demand and emissions. An average size DC plant can realize an estimated savings of $300,000 annually.This SBIR Phase I project proposes to improve the efficiency of district cooling (DC) systems by increasing their system analysis capabilities through the following activities: 1) Integrate machine learning techniques into DC system data analysis to generate a system performance indicator (SPI). Instead of signaling that fouling has already occurred, the SPI will predict fouling onset to signal the need for retreatment with the nanomaterial and conduct other maintenance. 2) Optimize a system by integrating additional sensors for system performance diagnostics. The proposed project will conduct verification and validation for a system that could retrofit a single diagnostic sensor in the absence of a full sensor suite.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.