IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v127y2017icp247-257.html
   My bibliography  Save this article

Data-driven modeling and real-time distributed control for energy efficient manufacturing systems

Author

Listed:
  • Zou, Jing
  • Chang, Qing
  • Arinez, Jorge
  • Xiao, Guoxian

Abstract

As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. In this paper, a novel data-driven stochastic manufacturing system modeling method is proposed to identify and predict energy saving opportunities and their impact on production. A real-time distributed feedback production control policy, which integrates the current and predicted system performance, is established to improve the overall profit and energy efficiency. A case study is presented to demonstrate the effectiveness of the proposed control policy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:127:y:2017:i:c:p:247-257
    DOI: 10.1016/j.energy.2017.03.123
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217305157
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.03.123?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. S. P. Sethi & H. Yan & H. Zhang & Q. Zhang, 2002. "Optimal and Hierarchical Controls in Dynamic Stochastic Manufacturing Systems: A Survey," Manufacturing & Service Operations Management, INFORMS, vol. 4(2), pages 133-170.
    2. Bujak, J., 2008. "Energy savings and heat efficiency in the paper industry: A case study of a corrugated board machine," Energy, Elsevier, vol. 33(11), pages 1597-1608.
    3. Brundage, Michael P. & Chang, Qing & Zou, Jing & Li, Yang & Arinez, Jorge & Xiao, Guoxian, 2015. "Energy economics in the manufacturing industry: A return on investment strategy," Energy, Elsevier, vol. 93(P2), pages 1426-1435.
    4. Paul, Sanjoy Kumar & Sarker, Ruhul & Essam, Daryl, 2014. "Real time disruption management for a two-stage batch production–inventory system with reliability considerations," European Journal of Operational Research, Elsevier, vol. 237(1), pages 113-128.
    5. Bouslah, Bassem & Gharbi, Ali & Pellerin, Robert, 2013. "Joint optimal lot sizing and production control policy in an unreliable and imperfect manufacturing system," International Journal of Production Economics, Elsevier, vol. 144(1), pages 143-156.
    6. Schudeleit, Timo & Züst, Simon & Wegener, Konrad, 2015. "Methods for evaluation of energy efficiency of machine tools," Energy, Elsevier, vol. 93(P2), pages 1964-1970.
    7. Wiendahl, Hans-Peter & Breithaupt, Jan-Wilhelm, 2000. "Automatic production control applying control theory," International Journal of Production Economics, Elsevier, vol. 63(1), pages 33-46, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Geng, D. & Evans, S. & Kishita, Y., 2023. "The identification and classification of energy waste for efficient energy supervision in manufacturing factories," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    2. 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.
    3. Junfeng Wang & Yaqin Huang & Qing Chang & Shiqi Li, 2019. "Event-Driven Online Machine State Decision for Energy-Efficient Manufacturing System Based on Digital Twin Using Max-Plus Algebra," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    4. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Bermeo-Ayerbe, Miguel Angel & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2022. "Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems," Energy, Elsevier, vol. 238(PB).

    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. Kruczek, Tadeusz, 2013. "Determination of annual heat losses from heat and steam pipeline networks and economic analysis of their thermomodernisation," Energy, Elsevier, vol. 62(C), pages 120-131.
    2. Zhou, Jing & Liu, Yu & Liang, Decui & Tang, Maochun, 2023. "A new risk analysis approach to seek best production action during new product introduction," International Journal of Production Economics, Elsevier, vol. 262(C).
    3. Hlioui, Rached & Gharbi, Ali & Hajji, Adnène, 2015. "Replenishment, production and quality control strategies in three-stage supply chain," International Journal of Production Economics, Elsevier, vol. 166(C), pages 90-102.
    4. Sun, Jingchao & Na, Hongming & Yan, Tianyi & Che, Zichang & Qiu, Ziyang & Yuan, Yuxing & Li, Yingnan & Du, Tao & Song, Yanli & Fang, Xin, 2022. "Cost-benefit assessment of manufacturing system using comprehensive value flow analysis," Applied Energy, Elsevier, vol. 310(C).
    5. Bouslah, B. & Gharbi, A. & Pellerin, R., 2016. "Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraint," Omega, Elsevier, vol. 61(C), pages 110-126.
    6. A. Thangam, 2017. "Retailer’s optimal replenishment policy in a two-echelon supply chain under two-part delay in payments and disruption in delivery," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 26-46, January.
    7. Palander, Teijo & Haavikko, Hanna & Kärhä, Kalle, 2018. "Towards sustainable wood procurement in forest industry – The energy efficiency of larger and heavier vehicles in Finland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 100-118.
    8. Bao, Xing & Diabat, Ali & Zheng, Zhongliang, 2020. "An ambiguous manager's disruption decisions with insufficient data in recovery phase," International Journal of Production Economics, Elsevier, vol. 221(C).
    9. Dmitry Ivanov & Boris Sokolov & Inna Solovyeva & Alexandre Dolgui & Ferry Jie, 2016. "Dynamic recovery policies for time-critical supply chains under conditions of ripple effect," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7245-7258, December.
    10. Nasreddine Saadouli, 2021. "Stochastic programming model for production planning with stochastic aggregate demand and spreadsheet-based solution heuristics," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(4), pages 117-127.
    11. Ali, Syed Mithun & Rahman, Md. Hafizur & Tumpa, Tasmia Jannat & Moghul Rifat, Abid Ali & Paul, Sanjoy Kumar, 2018. "Examining price and service competition among retailers in a supply chain under potential demand disruption," Journal of Retailing and Consumer Services, Elsevier, vol. 40(C), pages 40-47.
    12. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    13. Jia, Shun & Cai, Wei & Liu, Conghu & Zhang, Zhongwei & Bai, Shuowei & Wang, Qiuyan & Li, Shuoshuo & Hu, Luoke, 2021. "Energy modeling and visualization analysis method of drilling processes in the manufacturing industry," Energy, Elsevier, vol. 228(C).
    14. Avi Herbon & Konstantin Kogan, 2014. "Time-dependent and independent control rules for coordinated production and pricing under demand uncertainty and finite planning horizons," Annals of Operations Research, Springer, vol. 223(1), pages 195-216, December.
    15. Syed Mithun Ali & Asraf Arafin & Md. Abdul Moktadir & Towfique Rahman & Nuzhat Zahan, 2018. "Barriers to Reverse Logistics in the Computer Supply Chain Using Interpretive Structural Model," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(1), pages 53-68, March.
    16. Peymankar, Mahboobe & Dehghanian, Farzad & Ghiami, Yousef & Abolbashari, Mohammad Hassan, 2018. "The effects of contractual agreements on the economic production quantity model with machine breakdown," International Journal of Production Economics, Elsevier, vol. 201(C), pages 203-215.
    17. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    18. Roychaudhuri, Pritam Sankar & Kazantzi, Vasiliki & Foo, Dominic C.Y. & Tan, Raymond R. & Bandyopadhyay, Santanu, 2017. "Selection of energy conservation projects through Financial Pinch Analysis," Energy, Elsevier, vol. 138(C), pages 602-615.
    19. Taleizadeh, Ata Allah & Tafakkori, Keivan & Thaichon, Park, 2021. "Resilience toward supply disruptions: A stochastic inventory control model with partial backordering under the base stock policy," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    20. Sanjoy Kumar Paul & Sobhan Asian & Mark Goh & S. Ali Torabi, 2019. "Managing sudden transportation disruptions in supply chains under delivery delay and quantity loss," Annals of Operations Research, Springer, vol. 273(1), pages 783-814, February.

    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:127:y:2017:i:c:p:247-257. 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.