IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v286y2021ics0306261921000465.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921000465
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2021.116483?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. 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).
    2. 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).
    3. 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).
    4. 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.
    5. 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.
    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. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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).
    13. 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.
    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. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    2. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    3. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    4. Andrea Trianni & Davide Accordini & Enrico Cagno, 2020. "Identification and Categorization of Factors Affecting the Adoption of Energy Efficiency Measures within Compressed Air Systems," Energies, MDPI, vol. 13(19), pages 1-51, October.
    5. 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).
    6. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
    7. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
    8. 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).
    9. Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).
    10. Trianni, Andrea & Cagno, Enrico & Accordini, Davide, 2019. "Energy efficiency measures in electric motors systems: A novel classification highlighting specific implications in their adoption," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Katris, Antonios & Turner, Karen, 2021. "Can different approaches to funding household energy efficiency deliver on economic and social policy objectives? ECO and alternatives in the UK," Energy Policy, Elsevier, vol. 155(C).
    12. Muthu Kumaran Gunasegaran & Md Hasanuzzaman & ChiaKwang Tan & Ab Halim Abu Bakar & Vignes Ponniah, 2022. "Energy Analysis, Building Energy Index and Energy Management Strategies for Fast-Food Restaurants in Malaysia," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    13. Uwizeyemungu, Sylvestre & Poba-Nzaou, Placide & St-Pierre, Josée, 2022. "Back-end information technology resources and manufacturing SMEs’ export commitment: An empirical investigation," International Business Review, Elsevier, vol. 31(5).
    14. Yoon, Hae-Sung & Kim, Eun-Seob & Kim, Min-Soo & Lee, Jang-Yeob & Lee, Gyu-Bong & Ahn, Sung-Hoon, 2015. "Towards greener machine tools – A review on energy saving strategies and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 870-891.
    15. Saidur, R. & Abdelaziz, E.A. & Demirbas, A. & Hossain, M.S. & Mekhilef, S., 2011. "A review on biomass as a fuel for boilers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(5), pages 2262-2289, June.
    16. Jalo, Noor & Johansson, Ida & Kanchiralla, Fayas Malik & Thollander, Patrik, 2021. "Do energy efficiency networks help reduce barriers to energy efficiency? -A case study of a regional Swedish policy program for industrial SMEs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    17. Wei Zhang & Jixin Wang & Shaofeng Du & Hongfeng Ma & Wenjun Zhao & Haojie Li, 2019. "Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends," Energies, MDPI, vol. 12(10), pages 1-26, May.
    18. Wang, Jinling & Tian, Yebing & Hu, Xintao & Han, Jinguo & Liu, Bing, 2023. "Integrated assessment and optimization of dual environment and production drivers in grinding," Energy, Elsevier, vol. 272(C).
    19. 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.
    20. Vendrell-Herrero, Ferran & Bustinza, Oscar F. & Opazo-Basaez, Marco, 2021. "Information technologies and product-service innovation: The moderating role of service R&D team structure," Journal of Business Research, Elsevier, vol. 128(C), pages 673-687.

    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:appene:v:286:y:2021:i:c:s0306261921000465. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.