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Improved Large-Scale Process Cooling Operation through Energy Optimization
AbstractThis paper presents a study based on real plant data collected from chiller plants at the University of Texas at Austin. It highlights the advantages of operating the cooling processes based on an optimal strategy. A multi-component model is developed for the entire cooling process network. The model is used to formulate and solve a multi-period optimal chiller loading problem, posed as a mixed-integer nonlinear programming (MINLP) problem. The results showed that an average energy savings of 8.57% could be achieved using optimal chiller loading as compared to the historical energy consumption data from the plant. The scope of the optimization problem was expanded by including a chilled water thermal storage in the cooling system. The effect of optimal thermal energy storage operation on the net electric power consumption by the cooling system was studied. The results include a hypothetical scenario where the campus purchases electricity at wholesale market prices and an optimal hour-by-hour operating strategy is computed to use the thermal energy storage tank.
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MDPI and ACS Style
Kapoor, K.; Powell, K.M.; Cole, W.J.; Kim, J.S.; Edgar, T.F. Improved Large-Scale Process Cooling Operation through Energy Optimization. Processes 2013, 1, 312-329.View more citation formats
Kapoor K, Powell KM, Cole WJ, Kim JS, Edgar TF. Improved Large-Scale Process Cooling Operation through Energy Optimization. Processes. 2013; 1(3):312-329.Chicago/Turabian Style
Kapoor, Kriti; Powell, Kody M.; Cole, Wesley J.; Kim, Jong S.; Edgar, Thomas F. 2013. "Improved Large-Scale Process Cooling Operation through Energy Optimization." Processes 1, no. 3: 312-329.