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An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line

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  • Li, Lei
  • Huang, Haihong
  • Zou, Xiang
  • Zhao, Fu
  • Li, Guishan
  • Liu, Zhifeng

Abstract

Energy efficiency is of great significance in manufacturing to lower emissions and costs. Focusing on the production line featured with multi-machine and multi-task, a novel service-oriented energy supplying system where the energy supplying is deemed as a service is developed to improve efficiency. The service-oriented energy supplying system centralizes energy conversion units with different levels of output power as service agents to respond to the energy requirements of individual machines, and each machine requests a service that can match the power demand of the corresponding task. The architecture and mathematical model of all entities in the system were established to reveal the working process. The task-based agent design for the production line with different tasks was further developed to configure the system and construct the response mechanism to ensure the efficiency of energy conversion units. To validate the effectiveness, the system was applied on a production line that consists of four processes to form a clutch shell. Results show that the proposed system owns better energy-saving effects than that of the servo system with the performance of high energy efficiency, i.e., 6.42% of the energy consumption can be saved during a working cycle. The reason for energy saving was analyzed and how to further improve the efficiency of the system from the perspective of agent design was discussed. The proposed system assists in designing and operating multi-machine in a production line with similar tasks to be completed for higher energy efficiency.

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  • Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:appene:v:286:y:2021:i:c:s0306261921000465
    DOI: 10.1016/j.apenergy.2021.116483
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    1. Gökan May & Bojan Stahl & Marco Taisch & Vittal Prabhu, 2015. "Multi-objective genetic algorithm for energy-efficient job shop scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7071-7089, December.
    2. Dehning, Patrick & Blume, Stefan & Dér, Antal & Flick, Dominik & Herrmann, Christoph & Thiede, Sebastian, 2019. "Load profile analysis for reducing energy demands of production systems in non-production times," Applied Energy, Elsevier, vol. 237(C), pages 117-130.
    3. Sun, Cheng & Wang, Yun & McMurtrey, Michael D. & Jerred, Nathan D. & Liou, Frank & Li, Ju, 2021. "Additive manufacturing for energy: A review," Applied Energy, Elsevier, vol. 282(PA).
    4. F. Tao & Y. Cheng & L. Zhang & A. Y. C. Nee, 2017. "Advanced manufacturing systems: socialization characteristics and trends," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1079-1094, June.
    5. Liu, Hongxiang & Han, Ling & Cao, Yue, 2020. "Improving transmission efficiency and reducing energy consumption with automotive continuously variable transmission: A model prediction comprehensive optimization approach," Applied Energy, Elsevier, vol. 274(C).
    6. Lin, Tianliang & Chen, Qiang & Ren, Haoling & Huang, Weiping & Chen, Qihuai & Fu, Shengjie, 2017. "Review of boom potential energy regeneration technology for hydraulic construction machinery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 358-371.
    7. Trianni, Andrea & Cagno, Enrico & Farné, Stefano, 2016. "Barriers, drivers and decision-making process for industrial energy efficiency: A broad study among manufacturing small and medium-sized enterprises," Applied Energy, Elsevier, vol. 162(C), pages 1537-1551.
    8. Cai, Wei & Liu, Fei & Zhang, Hua & Liu, Peiji & Tuo, Junbo, 2017. "Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement," Applied Energy, Elsevier, vol. 202(C), pages 715-725.
    9. Zeng, Zhiqiang & Hong, Mengna & Li, Jigeng & Man, Yi & Liu, Huanbin & Li, Zeeman & Zhang, Huanhuan, 2018. "Integrating process optimization with energy-efficiency scheduling to save energy for paper mills," Applied Energy, Elsevier, vol. 225(C), pages 542-558.
    10. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    11. He, Yan & Wu, Pengcheng & Li, Yufeng & Wang, Yulin & Tao, Fei & Wang, Yan, 2020. "A generic energy prediction model of machine tools using deep learning algorithms," Applied Energy, Elsevier, vol. 275(C).
    12. Dietrich, Bastian & Walther, Jessica & Weigold, Matthias & Abele, Eberhard, 2020. "Machine learning based very short term load forecasting of machine tools," Applied Energy, Elsevier, vol. 276(C).
    13. Saidur, R. & Mekhilef, S., 2010. "Energy use, energy savings and emission analysis in the Malaysian rubber producing industries," Applied Energy, Elsevier, vol. 87(8), pages 2746-2758, August.
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    1. Jin, Rui & Li, Lei & Liang, Xiaoling & Zou, Xiang & Yang, Zeyuan & Ge, Shuzhi Sam & Huang, Haihong, 2024. "Energy-efficient design of the powertrain for mechanical-electro-hydraulic equipment via configuring multidimensional controllable variables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).

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