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Data-driven modeling and real-time distributed control for energy efficient manufacturing systems

Author

Listed:
  • Zou, Jing
  • Chang, Qing
  • Arinez, Jorge
  • Xiao, Guoxian

Abstract

As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. In this paper, a novel data-driven stochastic manufacturing system modeling method is proposed to identify and predict energy saving opportunities and their impact on production. A real-time distributed feedback production control policy, which integrates the current and predicted system performance, is established to improve the overall profit and energy efficiency. A case study is presented to demonstrate the effectiveness of the proposed control policy.

Suggested Citation

  • Zou, Jing & Chang, Qing & Arinez, Jorge & Xiao, Guoxian, 2017. "Data-driven modeling and real-time distributed control for energy efficient manufacturing systems," Energy, Elsevier, vol. 127(C), pages 247-257.
    • Handle: RePEc:eee:energy:v:127:y:2017:i:c:p:247-257
    DOI: 10.1016/j.energy.2017.03.123
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    References listed on IDEAS

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    1. Wiendahl, Hans-Peter & Breithaupt, Jan-Wilhelm, 2000. "Automatic production control applying control theory," International Journal of Production Economics, Elsevier, vol. 63(1), pages 33-46, January.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Junfeng Wang & Yaqin Huang & Qing Chang & Shiqi Li, 2019. "Event-Driven Online Machine State Decision for Energy-Efficient Manufacturing System Based on Digital Twin Using Max-Plus Algebra," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    2. Geng, D. & Evans, S. & Kishita, Y., 2023. "The identification and classification of energy waste for efficient energy supervision in manufacturing factories," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    3. Junfeng Wang & Zicheng Fei & Qing Chang & Shiqi Li, 2019. "Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net," Energies, MDPI, vol. 12(11), pages 1-17, June.
    4. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Bermeo-Ayerbe, Miguel Angel & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2022. "Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems," Energy, Elsevier, vol. 238(PB).

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