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Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

Author

Listed:
  • Junfeng Wang

    (Department of Industrial and Manufacturing System Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zicheng Fei

    (Department of Industrial and Manufacturing System Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Qing Chang

    (Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA 22904 USA)

  • Shiqi Li

    (Department of Industrial and Manufacturing System Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

The energy efficient operation of a manufacturing system is important for sustainable development of industry. Apart from the device and process level, energy saving methods at the system level has attracted increasing attention with the rapid growth of the industrial Internet of things technology, which makes it possible to sense and collect real-time data from the production line and provide more opportunities for online control for energy saving purposes. In this paper, a dynamic adaptive fuzzy reasoning Petri net is proposed to decide the machine energy saving state considering the production information of a discrete stochastic manufacturing system. Fuzzy knowledge for energy saving operations of a machine is represented in weighted fuzzy production rules with certain values. The rules describe uncertain, imprecise, and ambiguous knowledge of machine state decisions. This makes an energy saving sleep decision in advance when a machine has the inclination of starvation or blockage, which is based on the real-time production rates and level of connected buffers. A dynamic adaptive fuzzy reasoning Petri net is formally defined to implement the reasoning process of the machine state decision. A manufacturing system case is used to demonstrate the application of our method and the results indicate its effectiveness for energy saving operation purposes.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2216-:d:238798
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    References listed on IDEAS

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    1. Zhang, Xing-Ping & Cheng, Xiao-Mei, 2009. "Energy consumption, carbon emissions, and economic growth in China," Ecological Economics, Elsevier, vol. 68(10), pages 2706-2712, August.
    2. 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.
    3. Azadegan, Arash & Porobic, Lejla & Ghazinoory, Sepehr & Samouei, Parvaneh & Saman Kheirkhah, Amir, 2011. "Fuzzy logic in manufacturing: A review of literature and a specialized application," International Journal of Production Economics, Elsevier, vol. 132(2), pages 258-270, August.
    4. Zhiyang Jia & Liang Zhang & Jorge Arinez & Guoxian Xiao, 2016. "Performance analysis for serial production lines with Bernoulli Machines and Real-time WIP-based Machine switch-on/off control," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6285-6301, November.
    5. Chen Peng & Tao Peng & Yi Zhang & Renzhong Tang & Luoke Hu, 2018. "Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop," Energies, MDPI, vol. 11(12), pages 1-15, December.
    6. Akvile Lawrence & Patrik Thollander & Mariana Andrei & Magnus Karlsson, 2019. "Specific Energy Consumption/Use (SEC) in Energy Management for Improving Energy Efficiency in Industry: Meaning, Usage and Differences," Energies, MDPI, vol. 12(2), pages 1-22, January.
    7. Li, Yufeng & He, Yan & Wang, Yan & Wang, Yulin & Yan, Ping & Lin, Shenlong, 2015. "A modeling method for hybrid energy behaviors in flexible machining systems," Energy, Elsevier, vol. 86(C), pages 164-174.
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    Cited by:

    1. Konstantinos Salonitis, 2020. "Energy Efficiency of Manufacturing Processes and Systems—An Introduction," Energies, MDPI, vol. 13(11), pages 1-5, June.
    2. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
    3. Tangbin Xia & Xiangxin An & Huaqiang Yang & Yimin Jiang & Yuhui Xu & Meimei Zheng & Ershun Pan, 2023. "Efficient Energy Use in Manufacturing Systems—Modeling, Assessment, and Management Strategy," Energies, MDPI, vol. 16(3), pages 1-20, January.
    4. Helena Bulińska-Stangrecka & Anna Bagieńska, 2021. "Culture-Based Green Workplace Practices as a Means of Conserving Energy and Other Natural Resources in the Manufacturing Sector," Energies, MDPI, vol. 14(19), pages 1-21, October.

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