IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v143y2018icp881-899.html
   My bibliography  Save this article

A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant

Author

Listed:
  • Zeng, Yujiao
  • Xiao, Xin
  • Li, Jie
  • Sun, Li
  • Floudas, Christodoulos A.
  • Li, Hechang

Abstract

Byproduct gases, steam and electricity play an important role in providing energy for production units in the iron and steel industry. Optimal distribution of byproduct gases, steam and electricity in an iron and steel plant can significantly decrease energy cost and reduce CO2 emissions. However, such optimal distribution is not trivial because it involves many production units, steam turbines, combined heat and power units, and waste heat and energy recovery units, and several realistic operational features such as byproduct gas mixing, byproduct gas level control in dedicated gasholders, different levels of steam requirement, minimum heating and energy requirement, and maximum allowable burner switches, resulting in a large complex combinatorial problem. In this paper, we develop a novel multi-period mixed-integer linear programming model for optimal distribution of byproduct gases, steam, and power in an iron and steel plant. The consuming rates of byproduct gases are variable. Different byproduct gases are allowed to be mixed to satisfy minimum heating and energy requirement of production units. The steam is specifically classified as high, medium and low pressure. New binary variables are introduced to determine electricity purchase or sale decision with each having different price. The burner switching operation is correctly modeled with fewer binary variables allowing turning on and off at any time. Several important practical features such as fuel selection, gasholder level control, ramp rate variation, piecewise constant generation rates of byproduct gases, and piecewise constant demand profiles of byproduct gases, steam and electricity are also incorporated. The computational results demonstrate that the optimal operating cost is obtained within 2 CPU seconds for an industrial example using the proposed model, which is reduced by 6% compared to that from actual operation.

Suggested Citation

  • Zeng, Yujiao & Xiao, Xin & Li, Jie & Sun, Li & Floudas, Christodoulos A. & Li, Hechang, 2018. "A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant," Energy, Elsevier, vol. 143(C), pages 881-899.
    • Handle: RePEc:eee:energy:v:143:y:2018:i:c:p:881-899
    DOI: 10.1016/j.energy.2017.10.122
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217318285
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.10.122?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. Liu, Xiong & Chen, Lingen & Feng, Huijun & Qin, Xiaoyong & Sun, Fengrui, 2016. "Constructal design of a blast furnace iron-making process based on multi-objective optimization," Energy, Elsevier, vol. 109(C), pages 137-151.
    2. Kong, Haining & Qi, Ershi & Li, Hui & Li, Gang & Zhang, Xing, 2010. "An MILP model for optimization of byproduct gases in the integrated iron and steel plant," Applied Energy, Elsevier, vol. 87(7), pages 2156-2163, July.
    3. Zhao, Xiancong & Bai, Hao & Lu, Xin & Shi, Qi & Han, Jiehai, 2015. "A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process," Applied Energy, Elsevier, vol. 148(C), pages 142-158.
    4. Bischi, Aldo & Taccari, Leonardo & Martelli, Emanuele & Amaldi, Edoardo & Manzolini, Giampaolo & Silva, Paolo & Campanari, Stefano & Macchi, Ennio, 2014. "A detailed MILP optimization model for combined cooling, heat and power system operation planning," Energy, Elsevier, vol. 74(C), pages 12-26.
    5. Kim, Jong Suk & Edgar, Thomas F., 2014. "Optimal scheduling of combined heat and power plants using mixed-integer nonlinear programming," Energy, Elsevier, vol. 77(C), pages 675-690.
    6. Lixin Tang & Ying Meng & Zhi-Long Chen & Jiyin Liu, 2016. "Coil Batching to Improve Productivity and Energy Utilization in Steel Production," Manufacturing & Service Operations Management, INFORMS, vol. 18(2), pages 262-279, May.
    7. Hasanbeigi, Ali & Morrow, William & Sathaye, Jayant & Masanet, Eric & Xu, Tengfang, 2013. "A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry," Energy, Elsevier, vol. 50(C), pages 315-325.
    8. He, Feng & Zhang, Qingzhi & Lei, Jiasu & Fu, Weihui & Xu, Xiaoning, 2013. "Energy efficiency and productivity change of China’s iron and steel industry: Accounting for undesirable outputs," Energy Policy, Elsevier, vol. 54(C), pages 204-213.
    9. Zhang, Jianling & Wang, Guoshun, 2008. "Energy saving technologies and productive efficiency in the Chinese iron and steel sector," Energy, Elsevier, vol. 33(4), pages 525-537.
    10. Arens, Marlene & Worrell, Ernst & Schleich, Joachim, 2012. "Energy intensity development of the German iron and steel industry between 1991 and 2007," Energy, Elsevier, vol. 45(1), pages 786-797.
    11. Lin, Boqiang & Wang, Xiaolei, 2014. "Promoting energy conservation in China's iron & steel sector," Energy, Elsevier, vol. 73(C), pages 465-474.
    12. Ansari, Nastaran & Seifi, Abbas, 2012. "A system dynamics analysis of energy consumption and corrective policies in Iranian iron and steel industry," Energy, Elsevier, vol. 43(1), pages 334-343.
    13. Mitra, Sumit & Sun, Lige & Grossmann, Ignacio E., 2013. "Optimal scheduling of industrial combined heat and power plants under time-sensitive electricity prices," Energy, Elsevier, vol. 54(C), pages 194-211.
    14. de Oliveira Junior, Valter B. & Pena, João G. Coelho & Salles, José L. Félix, 2016. "An improved plant-wide multiperiod optimization model of a byproduct gas supply system in the iron and steel-making process," Applied Energy, Elsevier, vol. 164(C), pages 462-474.
    15. Zhang, Shaohui & Worrell, Ernst & Crijns-Graus, Wina & Wagner, Fabian & Cofala, Janusz, 2014. "Co-benefits of energy efficiency improvement and air pollution abatement in the Chinese iron and steel industry," Energy, Elsevier, vol. 78(C), pages 333-345.
    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. Sergio García García & Vicente Rodríguez Montequín & Henar Morán Palacios & Adriano Mones Bayo, 2020. "A Mixed Integer Linear Programming Model for the Optimization of Steel Waste Gases in Cogeneration: A Combined Coke Oven and Converter Gas Case Study," Energies, MDPI, vol. 13(15), pages 1-25, July.
    2. Yuan, Yuxing & Na, Hongming & Du, Tao & Qiu, Ziyang & Sun, Jingchao & Yan, Tianyi & Che, Zichang, 2023. "Multi-objective optimization and analysis of material and energy flows in a typical steel plant," Energy, Elsevier, vol. 263(PD).
    3. 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).
    4. Yu Huang & Weizhen Hou & Yiran Huang & Jiayu Li & Qixian Li & Dongfeng Wang & Yan Zhang, 2020. "Multi-Objective Optimal Operation for Steam Power Scheduling Based on Economic and Exergetic Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    5. 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).
    6. Xi, Han & Wu, Xiao & Chen, Xianhao & Sha, Peng, 2021. "Artificial intelligent based energy scheduling of steel mill gas utilization system towards carbon neutrality," Applied Energy, Elsevier, vol. 295(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. Ma, Ding & Chen, Wenying & Yin, Xiang & Wang, Lining, 2016. "Quantifying the co-benefits of decarbonisation in China’s steel sector: An integrated assessment approach," Applied Energy, Elsevier, vol. 162(C), pages 1225-1237.
    2. Hu, Rui & Zhang, Qun, 2015. "Study of a low-carbon production strategy in the metallurgical industry in China," Energy, Elsevier, vol. 90(P2), pages 1456-1467.
    3. Sheinbaum-Pardo, Claudia, 2016. "Decomposition analysis from demand services to material production: The case of CO2 emissions from steel produced for automobiles in Mexico," Applied Energy, Elsevier, vol. 174(C), pages 245-255.
    4. Xu, Bin & Lin, Boqiang, 2016. "Regional differences in the CO2 emissions of China's iron and steel industry: Regional heterogeneity," Energy Policy, Elsevier, vol. 88(C), pages 422-434.
    5. Kun He & Li Wang & Hongliang Zhu & Yulong Ding, 2018. "Energy-Saving Potential of China’s Steel Industry According to Its Development Plan," Energies, MDPI, vol. 11(4), pages 1-16, April.
    6. Xuan, Yanni & Yue, Qiang, 2017. "Scenario analysis on resource and environmental benefits of imported steel scrap for China’s steel industry," Resources, Conservation & Recycling, Elsevier, vol. 120(C), pages 186-198.
    7. Hasanbeigi, Ali & Morrow, William & Sathaye, Jayant & Masanet, Eric & Xu, Tengfang, 2013. "A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry," Energy, Elsevier, vol. 50(C), pages 315-325.
    8. Sergio García García & Vicente Rodríguez Montequín & Marina Díaz Piloñeta & Susana Torno Lougedo, 2021. "Multi-Objective Optimization of Steel Off-Gas in Cogeneration Using the ε-Constraint Method: A Combined Coke Oven and Converter Gas Case Study," Energies, MDPI, vol. 14(10), pages 1-21, May.
    9. Zhao, Xiancong & Bai, Hao & Shi, Qi & Lu, Xin & Zhang, Zhihui, 2017. "Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing," Applied Energy, Elsevier, vol. 195(C), pages 100-113.
    10. Lin, Boqiang & Wang, Xiaolei, 2014. "Promoting energy conservation in China's iron & steel sector," Energy, Elsevier, vol. 73(C), pages 465-474.
    11. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    12. Ke Wang & Linan Che & Chunbo Ma & Yi-Ming Wei, 2017. "The Shadow Price of CO2 Emissions in China's Iron and Steel Industry," CEEP-BIT Working Papers 105, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    13. Zhang, Qi & Zhao, Xiaoyu & Lu, Hongyou & Ni, Tuanjie & Li, Yu, 2017. "Waste energy recovery and energy efficiency improvement in China’s iron and steel industry," Applied Energy, Elsevier, vol. 191(C), pages 502-520.
    14. Filippini, Massimo & Geissmann, Thomas & Karplus, Valerie J. & Zhang, Da, 2020. "The productivity impacts of energy efficiency programs in developing countries: Evidence from iron and steel firms in China," China Economic Review, Elsevier, vol. 59(C).
    15. Zhang, Shaohui & Worrell, Ernst & Crijns-Graus, Wina & Wagner, Fabian & Cofala, Janusz, 2014. "Co-benefits of energy efficiency improvement and air pollution abatement in the Chinese iron and steel industry," Energy, Elsevier, vol. 78(C), pages 333-345.
    16. Zhang, Qi & Xu, Jin & Wang, Yujie & Hasanbeigi, Ali & Zhang, Wei & Lu, Hongyou & Arens, Marlene, 2018. "Comprehensive assessment of energy conservation and CO2 emissions mitigation in China’s iron and steel industry based on dynamic material flows," Applied Energy, Elsevier, vol. 209(C), pages 251-265.
    17. Wu, Junnian & Pu, Guangying & Guo, Yan & Lv, Jingwen & Shang, Jiangwei, 2018. "Retrospective and prospective assessment of exergy, life cycle carbon emissions, and water footprint for coking network evolution in China," Applied Energy, Elsevier, vol. 218(C), pages 479-493.
    18. Xueying Sun & Zhuo Wang & Jingtao Hu, 2018. "Fuzzy Byproduct Gas Scheduling in the Steel Plant Considering Uncertainty and Risk Analysis," Energies, MDPI, vol. 11(10), pages 1-14, October.
    19. Chen, Wenying & Yin, Xiang & Ma, Ding, 2014. "A bottom-up analysis of China’s iron and steel industrial energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 136(C), pages 1174-1183.
    20. Santos, Maria Izabel & Uturbey, Wadaed, 2018. "A practical model for energy dispatch in cogeneration plants," Energy, Elsevier, vol. 151(C), pages 144-159.

    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:143:y:2018:i:c:p:881-899. 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.