Benchmarking Production System, Process Energy, and Facility Energy Performance Using a Systems Approach

Published

January 31, 2011

Author(s)

Jorge Arinez, Stephan Biller, Kevin W. Lyons, Swee K. Leong, Guodong Shao, Byeong Eon Lee, John L. Michaloski

Abstract

Evaluating overall energy performance of a manufacturing system requires accurate information on how, when, and where energy is being used. Collecting and tracking energy data is necessary for determining performance benchmarks and reducing energy consumption. Optimizing energy efficiency in manufacturing systems is difficult to achieve since energy management is typically performed separately from the production monitoring and control systems. Further, low-level equipment energy data collection is costly to do, and, if done, is often not well-linked to production data. The smarter integration of production system, process energy, and facility energy data is a significant opportunity to improve manufacturing sustainability. This paper will examine the issues related to the linking of these three types of data as well as develop a methodology for jointly modeling and evaluating production, process energy, and facility energy performance. A case study of a sand casting production line will be discussed to better understand the integration issues, validate the methodology, test performance benchmarks, and investigate sustainable manufacturing opportunities.

Proceedings Title

Proceedings of the 2010 Performance Metrics for Intelligent Systems (PerMIS) Workshop

Conference Dates

September 28-30, 2010

Conference Location

Baltimore, MD, US

Pub Type

Conferences

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Keywords

manufacturing, integration, discrete event simulation, Finite State Machine, production, sustainability

Citation

Arinez, J. , Biller, S. , Lyons, K. , Leong, S. , Shao, G. , Lee, B. and Michaloski, J. (2011), Benchmarking Production System, Process Energy, and Facility Energy Performance Using a Systems Approach, Proceedings of the 2010 Performance Metrics for Intelligent Systems (PerMIS) Workshop, Baltimore, MD, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=906726 (Accessed December 6, 2024)