IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v226y2021ics0360544221006460.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221006460
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120397?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matteo Piccioni & Fabrizio Martini & Chiara Martini & Claudia Toro, 2024. "Evaluation of Energy Performance Indicators and Energy Saving Opportunities for the Italian Rubber Manufacturing Industry," Energies, MDPI, vol. 17(7), pages 1-23, March.
    2. Perroni, Marcos G. & Gouvea da Costa, Sergio E. & Pinheiro de Lima, Edson & Vieira da Silva, Wesley & Tortato, Ubiratã, 2018. "Measuring energy performance: A process based approach," Applied Energy, Elsevier, vol. 222(C), pages 540-553.
    3. Gibb, Duncan & Johnson, Maike & Romaní, Joaquim & Gasia, Jaume & Cabeza, Luisa F. & Seitz, Antje, 2018. "Process integration of thermal energy storage systems – Evaluation methodology and case studies," Applied Energy, Elsevier, vol. 230(C), pages 750-760.
    4. Salvatori, Simone & Benedetti, Miriam & Bonfà, Francesca & Introna, Vito & Ubertini, Stefano, 2018. "Inter-sectorial benchmarking of compressed air generation energy performance: Methodology based on real data gathering in large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 217(C), pages 266-280.
    5. Pedro Roquet & Gustavo Raush & Luis Javier Berne & Pedro-Javier Gamez-Montero & Esteban Codina, 2022. "Energy Key Performance Indicators for Mobile Machinery," Energies, MDPI, vol. 15(4), pages 1-16, February.
    6. Amir Abolhassani & Gale Boyd & Majid Jaridi & Bhaskaran Gopalakrishnan & James Harner, 2023. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants," Energies, MDPI, vol. 16(4), pages 1-35, February.
    7. Adalberto Ospino Castro & Carlos Robles-Algar n & Rafael Pe a Gallardo, 2019. "Analysis of Energy Management and Financial Planning in the Implementation of PV Systems," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 1-11.
    8. Cristian Mejia & Yuya Kajikawa, 2021. "The Academic Landscapes of Manufacturing Enterprise Performance and Environmental Sustainability: A Study of Commonalities and Differences," IJERPH, MDPI, vol. 18(7), pages 1-16, March.
    9. Liu, Weipeng & Zhao, Chunhui & Peng, Tao & Zhang, Zhongwei & Wan, Anping, 2023. "Simulation-assisted multi-process integrated optimization for greentelligent aluminum casting," Applied Energy, Elsevier, vol. 336(C).
    10. Michela Gallo & Desara Malluta & Adriana Del Borghi & Erica Gagliano, 2024. "A Critical Review on Methodologies for the Energy Benchmarking of Wastewater Treatment Plants," Sustainability, MDPI, vol. 16(5), pages 1-18, February.
    11. Anne Immonen & Maria Kopsakangas-Savolainen, 2022. "Capturing Consumers’ Awareness and the Intention to Support Carbon Neutrality through Energy Efficient Consumption," Energies, MDPI, vol. 15(11), pages 1-27, May.
    12. May, Gökan & Stahl, Bojan & Taisch, Marco, 2016. "Energy management in manufacturing: Toward eco-factories of the future – A focus group study," Applied Energy, Elsevier, vol. 164(C), pages 628-638.
    13. Favi, Claudio & Marconi, Marco & Mandolini, Marco & Germani, Michele, 2022. "Sustainable life cycle and energy management of discrete manufacturing plants in the industry 4.0 framework," Applied Energy, Elsevier, vol. 312(C).
    14. Du, Huibin & Matisoff, Daniel C. & Wang, Yangyang & Liu, Xi, 2016. "Understanding drivers of energy efficiency changes in China," Applied Energy, Elsevier, vol. 184(C), pages 1196-1206.
    15. Gokan May & Foivos Psarommatis, 2023. "Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research," Energies, MDPI, vol. 16(10), pages 1-28, May.
    16. Wen, Xuanhao & Cao, Huajun & Hon, Bernard & Chen, Erheng & Li, Hongcheng, 2021. "Energy value mapping: A novel lean method to integrate energy efficiency into production management," Energy, Elsevier, vol. 217(C).
    17. Hasan, A S M Monjurul & Tuhin, Rashedul Amin & Ullah, Mahfuz & Sakib, Taiyeb Hasan & Thollander, Patrik & Trianni, Andrea, 2021. "A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh," Energy, Elsevier, vol. 232(C).
    18. Romaní, Joaquim & Gasia, Jaume & Solé, Aran & Takasu, Hiroki & Kato, Yukitaka & Cabeza, Luisa F., 2019. "Evaluation of energy density as performance indicator for thermal energy storage at material and system levels," Applied Energy, Elsevier, vol. 235(C), pages 954-962.
    19. Wen, Xuanhao & Cao, Huajun & Li, Hongcheng & Zheng, Jie & Ge, Weiwei & Chen, Erheng & Gao, Xi & Hon, Bernard, 2022. "A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques," Energy, Elsevier, vol. 255(C).
    20. Maria Rosaria Guarini & Pierluigi Morano & Francesco Sica, 2019. "Integrated Ecosystem Design: An Evaluation Model to Support the Choice of Eco-Compatible Technological Solutions for Residential Building," Energies, MDPI, vol. 12(14), pages 1-34, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006460. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.