This grant will address water quality issues and water quantity management in commercial and institutional buildings, which often have complex pipe networks that provide water for human occupants? use and building functions. Commercial and institutional buildings can account for nearly 50 percent of municipal water use, and water quality management must balance health risks from bacteria growth and material impurities with efficient and sustainable use of water. With the ultimate goal of providing guidelines for water network design and water quality management, this research will use enhanced data-gathering approaches that are cyber-connected to computational resources, to develop data-driven models to proactively ensure safe water quality while delivering water with minimal wastage. The associated educational activities span the full breadth of STEM including water science, technology, computer science and applied mathematics, and human interactions with technology. Within the university, there will be the positive impacts of the integration of research and curricula of Environmental Engineering, Operations Research, and Computer Science, and hence new opportunities to increase the diversity of the professional workforce. The project will also be a vehicle to stimulate interests of K-12 students and the public at large on the use of cyber-connected technologies for better water quality management.
The fundamental contributions include the integrative use of the sciences of chemistry and microbiology processes within buildings? water networks, and the physical water-flow dynamics, to develop new data-driven models of the space-time trajectories of water parcels as water flows through the building pipe network to its eventual uses by occupants. The research will use statistical machine-learning approaches to develop new macro functional models to capture the salient features of state dynamics to estimate the state of water quality using various measurement and monitoring techniques. In addition, the research will develop predictive models for use in a proactive water quality management system, where available controls at given points in the building?s water network, such as injection of chemicals and partial flushing of water, can achieve a desired level of quality while optimizing a given performance objective.
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.
