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Multi-level energy efficiency evaluation for die casting workshop based on fog-cloud computing

Author

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  • Cao, Huajun
  • Chen, Erheng
  • Yi, Hao
  • Li, Hongcheng
  • Zhu, Linquan
  • Wen, Xuanhao

Abstract

Die casting is a complex process performed in harsh working environments. Driven by cost and environmental pressure, die casting, as one of the most energy-intensive manufacturing processes, has received increasing attention on enhancing energy efficiency toward greener and more sustainable manufacturing. Energy efficiency evaluation is a starting point for energy audits and analysis of energy-saving scenarios, while complex production conditions in the die casting workshop (e.g. product changeover, technology improvements, and degradation of equipment performance) require even higher real-time and dynamic performance of energy efficiency evaluation. To this end, this paper proposes a multi-level energy efficiency evaluation framework based on fog-cloud computing. Accordingly, real-time parameter identification models and dynamic energy efficiency evaluation method are proposed. An industrial case study of die casting workshop has demonstrated the feasibility and effectiveness of the proposed approach. The results reported that the overall equipment effectiveness and energy utilization ratio of die casting units increased by 3% and 7%, respectively, and energy consumption per kilogram of the die casting workshop was reduced by 7.9%, showing its great potential in identifying energy efficiency improvement opportunities.

Suggested Citation

  • Cao, Huajun & Chen, Erheng & Yi, Hao & Li, Hongcheng & Zhu, Linquan & Wen, Xuanhao, 2021. "Multi-level energy efficiency evaluation for die casting workshop based on fog-cloud computing," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006460
    DOI: 10.1016/j.energy.2021.120397
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    References listed on IDEAS

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    1. May, Gökan & Barletta, Ilaria & Stahl, Bojan & Taisch, Marco, 2015. "Energy management in production: A novel method to develop key performance indicators for improving energy efficiency," Applied Energy, Elsevier, vol. 149(C), pages 46-61.
    2. Liu, Weipeng & Peng, Tao & Tang, Renzhong & Umeda, Yasushi & Hu, Luoke, 2020. "An Internet of Things-enabled model-based approach to improving the energy efficiency of aluminum die casting processes," Energy, Elsevier, vol. 202(C).
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