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.
