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Modelling and analysis of energy footprint of manufacturing systems

Author

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  • Hyun Woo Jeon
  • Marco Taisch
  • Vittaldas V. Prabhu

Abstract

Increasing the energy efficiency of manufacturing plants will reduce the production costs and environmental impact. In order to analyse and improve the energy efficiency of manufacturing plants, however, we need models to evaluate the energy footprints of the plants. A key challenge of estimating plant-level footprints is that systemic methods of connecting information on the product, machine and plant levels are not available. Thus, we propose methods to parameterise product-level elements and to model machine-level factors based on those elements. From the machine-level models, the proposed approach performs simulation experiments and provides the energy footprints in closed-form equations for the plant level. We also suggest that the resulting model can be combined with probabilistic techniques to benchmark the energy efficiency of plants at the industry level. In a case study, we demonstrate how to apply the proposed methods to estimate the energy footprint of a hypothetical plant. The procedures introduced here enable manufacturers to evaluate the energy consumption of their facilities at early stages of manufacturing, and provide tools to assess the energy efficiency of their plant by comparison with peers.

Suggested Citation

  • Hyun Woo Jeon & Marco Taisch & Vittaldas V. Prabhu, 2015. "Modelling and analysis of energy footprint of manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7049-7059, December.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:23:p:7049-7059
    DOI: 10.1080/00207543.2014.961208
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    References listed on IDEAS

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    1. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    2. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, November.
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    Cited by:

    1. Fang Wang & Yunqing Rao & Chaoyong Zhang & Qiuhua Tang & Liping Zhang, 2016. "Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes," Sustainability, MDPI, vol. 8(8), pages 1-20, August.
    2. Guo, Yansong & Duflou, Joost R. & Deng, Yelin & Lauwers, Bert, 2018. "A life cycle energy analysis integrated process planning approach to foster the sustainability of discrete part manufacturing," Energy, Elsevier, vol. 153(C), pages 604-617.
    3. Gert van Wyk & Vinessa Naidoo & E. Innocents Edoun, 2021. "Guiding Principles for Establishing Energy Consumption Reduction and Increase Production Performance in Manufacturing," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 502-515.

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