IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v345y2023ics0306261923006967.html
   My bibliography  Save this article

A deep reinforcement learning based multi-objective optimization for the scheduling of oxygen production system in integrated iron and steel plants

Author

Listed:
  • Che, Gelegen
  • Zhang, Yanyan
  • Tang, Lixin
  • Zhao, Shengnan

Abstract

The oxygen production system in integrated iron and steel plants is a highly energy-intensive sector producing gaseous oxygen for the manufacturing processes. This study investigates an oxygen production system with cryogenic air separation units (ASU), vaporizers, and liquefiers under frequent demand-changing scenarios and various electricity price contracts. A multi-objective optimization model is established to minimize the total operating cost to reduce energy consumption and simultaneously minimize switching times of operating modes to pursue operational stability. The proposed model not only schedules operation modes of ASUs and on/off modes of vaporizers and liquefiers but also determines the production level of these units. To solve the problem, a multi-objective evolutionary algorithm (MOEA) is proposed, in which the proximal policy optimization, one of deep reinforcement learning (DRL) methods, is incorporated to adaptively select the mating individuals and determine the crossover ratio at each evolutionary iteration. Besides, a surrogate model for optimizing the total operating cost is presented to accelerate the training process of the proposed deep reinforcement learning based multi-objective evolutionary algorithm (DRL-MOEA). The performance of the proposed algorithms is demonstrated using practical instances, and experiment results show that it saves the total operating cost up to 0.86% and decreases the operating modes switches as much as 14.41%, compared to MOEA. The model offers the on-site manager solutions to coordinate the supply and demand with a good trade-off between reducing overall energy consumption and pursuing streamlined operating conditions.

Suggested Citation

  • Che, Gelegen & Zhang, Yanyan & Tang, Lixin & Zhao, Shengnan, 2023. "A deep reinforcement learning based multi-objective optimization for the scheduling of oxygen production system in integrated iron and steel plants," Applied Energy, Elsevier, vol. 345(C).
    • Handle: RePEc:eee:appene:v:345:y:2023:i:c:s0306261923006967
    DOI: 10.1016/j.apenergy.2023.121332
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923006967
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121332?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. Kelley, Morgan T. & Pattison, Richard C. & Baldick, Ross & Baldea, Michael, 2018. "An MILP framework for optimizing demand response operation of air separation units," Applied Energy, Elsevier, vol. 222(C), pages 951-966.
    2. Adamson, Richard & Hobbs, Martin & Silcock, Andy & Willis, Mark J., 2017. "Steady-state optimisation of a multiple cryogenic air separation unit and compressor plant," Applied Energy, Elsevier, vol. 189(C), pages 221-232.
    3. Fernández, David & Pozo, Carlos & Folgado, Rubén & Guillén-Gosálbez, Gonzalo & Jiménez, Laureano, 2017. "Multiperiod model for the optimal production planning in the industrial gases sector," Applied Energy, Elsevier, vol. 206(C), pages 667-682.
    4. Kong, Fulin & Liu, Yuxin & Shen, Minghai & Tong, Lige & Yin, Shaowu & Wang, Li & Ding, Yulong, 2023. "A novel economic scheduling of multi-product deterministic demand for co-production air separation system with liquid air energy storage," Renewable Energy, Elsevier, vol. 209(C), pages 533-545.
    5. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    6. Xenos, Dionysios P. & Cicciotti, Matteo & Kopanos, Georgios M. & Bouaswaig, Ala E.F. & Kahrs, Olaf & Martinez-Botas, Ricardo & Thornhill, Nina F., 2015. "Optimization of a network of compressors in parallel: Real Time Optimization (RTO) of compressors in chemical plants – An industrial case study," Applied Energy, Elsevier, vol. 144(C), pages 51-63.
    7. He, Xiufen & Liu, Yunong & Rehman, Ali & Wang, Li, 2021. "A novel air separation unit with energy storage and generation and its energy efficiency and economy analysis," Applied Energy, Elsevier, vol. 281(C).
    8. Qi, Meng & Kim, Minsu & Dat Vo, Nguyen & Yin, Liang & Liu, Yi & Park, Jinwoo & Moon, Il, 2022. "Proposal and surrogate-based cost-optimal design of an innovative green ammonia and electricity co-production system via liquid air energy storage," Applied Energy, Elsevier, vol. 314(C).
    9. Kong, Fulin & Liu, Yuxin & Tong, Lige & Guo, Wei & Qiu, Yinan & Wang, Li, 2022. "Optimization of co-production air separation unit based on MILP under multi-product deterministic demand," Applied Energy, Elsevier, vol. 325(C).
    10. Jiang, Sheng-Long & Peng, Gongzhuang & Bogle, I. David L. & Zheng, Zhong, 2022. "Two-stage robust optimization approach for flexible oxygen distribution under uncertainty in integrated iron and steel plants," Applied Energy, Elsevier, vol. 306(PB).
    11. Xenos, Dionysios P. & Mohd Noor, Izzati & Matloubi, Mitra & Cicciotti, Matteo & Haugen, Trond & Thornhill, Nina F., 2016. "Demand-side management and optimal operation of industrial electricity consumers: An example of an energy-intensive chemical plant," Applied Energy, Elsevier, vol. 182(C), pages 418-433.
    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. Li, Yutong & Hou, Jian & Yan, Gangfeng, 2024. "Exploration-enhanced multi-agent reinforcement learning for distributed PV-ESS scheduling with incomplete data," Applied Energy, Elsevier, vol. 359(C).
    2. Nan Ma & Hongqi Li & Hualin Liu, 2024. "State-Space Compression for Efficient Policy Learning in Crude Oil Scheduling," Mathematics, MDPI, vol. 12(3), pages 1-16, January.
    3. Zhang, Liu & Zheng, Zhong & Chai, Yi & Zhang, Kaitian & Lian, Xiaoyuan & Zhang, Kai & Zhao, Liuqiang, 2024. "Enhancing robustness: Multi-stage adaptive robust scheduling of oxygen systems in steel enterprises under demand uncertainty," Applied Energy, Elsevier, vol. 359(C).

    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. Kong, Fulin & Liu, Yuxin & Shen, Minghai & Tong, Lige & Yin, Shaowu & Wang, Li & Ding, Yulong, 2023. "A novel economic scheduling of multi-product deterministic demand for co-production air separation system with liquid air energy storage," Renewable Energy, Elsevier, vol. 209(C), pages 533-545.
    2. Zhang, Liu & Zheng, Zhong & Chai, Yi & Zhang, Kaitian & Lian, Xiaoyuan & Zhang, Kai & Zhao, Liuqiang, 2024. "Enhancing robustness: Multi-stage adaptive robust scheduling of oxygen systems in steel enterprises under demand uncertainty," Applied Energy, Elsevier, vol. 359(C).
    3. Zhang, Liu & Zheng, Zhong & Chai, Yi & Xu, Zhaojun & Zhang, Kaitian & Liu, Yu & Chen, Sujun & Zhao, Liuqiang, 2023. "ASU model with multiple adjustment types for oxygen scheduling concerning pipe pressure safety in steel enterprises," Applied Energy, Elsevier, vol. 343(C).
    4. Kong, Fulin & Liu, Yuxin & Tong, Lige & Guo, Wei & Qiu, Yinan & Wang, Li, 2022. "Optimization of co-production air separation unit based on MILP under multi-product deterministic demand," Applied Energy, Elsevier, vol. 325(C).
    5. Jiang, Sheng-Long & Wang, Meihong & Bogle, I. David L., 2023. "Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization," Applied Energy, Elsevier, vol. 350(C).
    6. Xiao, Dongliang & Lin, Zhenjia & Chen, Haoyong & Hua, Weiqi & Yan, Jinyue, 2024. "Windfall profit-aware stochastic scheduling strategy for industrial virtual power plant with integrated risk-seeking/averse preferences," Applied Energy, Elsevier, vol. 357(C).
    7. He, Xiufen & Guo, Wei & Liu, Yunong & Zuo, Zhongqi & Wang, Li, 2024. "Utmost substance recovery and utilization for integrated technology of air separation unit and liquid air energy storage and its saving benefits," Renewable Energy, Elsevier, vol. 225(C).
    8. Zhang, Hanxin & Sun, Wenqiang & Li, Weidong & Ma, Guangyu, 2022. "A carbon flow tracing and carbon accounting method for exploring CO2 emissions of the iron and steel industry: An integrated material–energy–carbon hub," Applied Energy, Elsevier, vol. 309(C).
    9. Laing, Harry & O'Malley, Chris & Browne, Anthony & Rutherford, Tony & Baines, Tony & Moore, Andrew & Black, Ken & Willis, Mark J., 2022. "Optimisation of energy usage and carbon emissions monitoring using MILP for an advanced anaerobic digester plant," Energy, Elsevier, vol. 256(C).
    10. Adamson, Richard & Hobbs, Martin & Silcock, Andy & Willis, Mark J., 2017. "Steady-state optimisation of a multiple cryogenic air separation unit and compressor plant," Applied Energy, Elsevier, vol. 189(C), pages 221-232.
    11. Zhang, Liu & Zhang, Kaitian & Zheng, Zhong & Chai, Yi & Lian, Xiaoyuan & Zhang, Kai & Xu, Zhaojun & Chen, Sujun, 2023. "Two-stage distributionally robust integrated scheduling of oxygen distribution and steelmaking-continuous casting in steel enterprises," Applied Energy, Elsevier, vol. 351(C).
    12. Otashu, Joannah I. & Baldea, Michael, 2020. "Scheduling chemical processes for frequency regulation," Applied Energy, Elsevier, vol. 260(C).
    13. Qi, Meng & Park, Jinwoo & Lee, Inkyu & Moon, Il, 2022. "Liquid air as an emerging energy vector towards carbon neutrality: A multi-scale systems perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    14. Severino, Gonzalo & Rivera, José & Parot, Roberto & Otaegui, Ernesto & Fuentes, Andrés & Reszka, Pedro, 2024. "Workforce and task optimization to guarantee oxygen bottling under a COVID-19 pandemic scenario: A Chilean case study," International Journal of Production Economics, Elsevier, vol. 271(C).
    15. Yang, Honghua & Ma, Linwei & Li, Zheng, 2023. "Tracing China's steel use from steel flows in the production system to steel footprints in the consumption system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    16. Heo, SungKu & Byun, Jaewon & Ifaei, Pouya & Ko, Jaerak & Ha, Byeongmin & Hwangbo, Soonho & Yoo, ChangKyoo, 2024. "Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    17. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    18. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    19. Finhold, E. & Gärtner, C. & Grindel, R. & Heller, T. & Leithäuser, N. & Röger, E. & Schirra, F., 2023. "Optimizing the marketing of flexibility for a virtual battery in day-ahead and balancing markets: A rolling horizon case study," Applied Energy, Elsevier, vol. 352(C).
    20. Liu, Shuhan & Sun, Wenqiang, 2023. "Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation," Energy, Elsevier, vol. 262(PA).

    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:345:y:2023:i:c:s0306261923006967. 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.