A growing number of manufacturing industries are initiating efforts to address sustainability issues. According to the National Association of Manufacturers, the manufacturing sector currently accounts for about one third of all energy consumed in the United States . Reducing energy costs and pollution emissions involves many areas within an industrial facility. Peak electric demands are a significant component in the cost of electricity. Electric demand management relates to electric tariff rates, new power generation, and incentives to curtail peak usages. Shifting some equipment/machine usage to the periods of lower cost or using stand-by local generators during the peak demand period can yield important savings. Analysis of these options is important to decision makers to avoid unnecessary high cost of energy and equipment. This paper proposes a Decision-Guided energy management in manufacturing (DG-EMM) framework to perform what-if analysis and make optimal actionable recommendations for a manufacturing facility both on (1) operational energy management including load shedding, curtailment, and local generation and (2) planning and investment decisions for introducing renewable technologies. The DG-EMM is based on the novel technology of the Decision-Guidance Query Language (DGQL), which is a tool for fast development and iterative extension of decision-guidance and optimization solutions. The proposed DG-EMM will support user-defined objectives for optimal recommendations, such as minimizing emissions and energy costs and maximizing Return on Investment (ROI). A case study of the peak demand control for an example manufacturing facility is discussed.
- Design Engineering Division and Computers and Information in Engineering Division
A Decision-Guided Energy Management Framework for Sustainable Manufacturing
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Shao, G, Brodsky, A, Arinez, J, Menasce, D, & Ammann, P. "A Decision-Guided Energy Management Framework for Sustainable Manufacturing." Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B. Washington, DC, USA. August 28–31, 2011. pp. 305-314. ASME. https://doi.org/10.1115/DETC2011-47454
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