IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v164y2016icp462-474.html
   My bibliography  Save this article

An improved plant-wide multiperiod optimization model of a byproduct gas supply system in the iron and steel-making process

Author

Listed:
  • de Oliveira Junior, Valter B.
  • Pena, João G. Coelho
  • Salles, José L. Félix

Abstract

In an integrated steel mill, the byproduct gases of steel production processes can be recovered as fuel for the plant itself and can be used to generate electricity and steam in power plants. This work addresses the development of a mixed integer linear programming (MILP) model to solve the problem of byproduct fuel distribution, with the goal of maximizing energy utilization. Improvements are made in the formulation of the model and a heuristic procedure is proposed to assign appropriate weights to the objective function penalties, resulting in an increase in the operational performance of the fuel distribution system. Through case studies, a comparison is performed among a previous MILP-based optimization model found in the literature, the improved one proposed in this work and the real-world behavior of a fuel distribution system run by the operators without the aid of a computational tool.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:164:y:2016:i:c:p:462-474
    DOI: 10.1016/j.apenergy.2015.11.043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915014932
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.11.043?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. 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.
    2. 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.
    3. Worrell, Ernst & Laitner, John A & Ruth, Michael & Finman, Hodayah, 2003. "Productivity benefits of industrial energy efficiency measures," Energy, Elsevier, vol. 28(11), pages 1081-1098.
    4. Porzio, Giacomo Filippo & Nastasi, Gianluca & Colla, Valentina & Vannucci, Marco & Branca, Teresa Annunziata, 2014. "Comparison of multi-objective optimization techniques applied to off-gas management within an integrated steelwork," Applied Energy, Elsevier, vol. 136(C), pages 1085-1097.
    5. Gopal P. Sinha & B. S. Chandrasekaran & Niloy Mitter & Goutam Dutta & Sudhir B. Singh & Aditya Roy Choudhury & P. N. Roy, 1995. "Strategic and Operational Management with Optimization at Tata Steel," Interfaces, INFORMS, vol. 25(1), pages 6-19, February.
    6. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    7. Quader, M. Abdul & Ahmed, Shamsuddin & Ghazilla, Raja Ariffin Raja & Ahmed, Shameem & Dahari, Mahidzal, 2015. "A comprehensive review on energy efficient CO2 breakthrough technologies for sustainable green iron and steel manufacturing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 594-614.
    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 & 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.
    2. 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.
    3. Chen Ya & Zhang Xintian & Liu Haoxiang, 2021. "Investigating the Impact of Capacity Utilization on Carbon Dioxide Emission: Evidence from China’s Iron and Steel Industry," Journal of Systems Science and Information, De Gruyter, vol. 9(6), pages 681-703, December.
    4. 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.
    5. 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.
    6. Zetterholm, J. & Ji, X. & Sundelin, B. & Martin, P.M. & Wang, C., 2017. "Dynamic modelling for the hot blast stove," Applied Energy, Elsevier, vol. 185(P2), pages 2142-2150.
    7. Xu, Bin & Lin, Boqiang, 2017. "Assessing CO2 emissions in China's iron and steel industry: A nonparametric additive regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 325-337.
    8. 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).
    9. 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.
    10. Xia Wang & Lijun Zhang & Yaochen Qin & Jingfei Zhang, 2020. "Analysis of China’s Manufacturing Industry Carbon Lock-In and Its Influencing Factors," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    11. e Silva, Danilo P. & Félix Salles, José L. & Fardin, Jussara F. & Rocha Pereira, Maxsuel M., 2020. "Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data," Applied Energy, Elsevier, vol. 278(C).
    12. 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. Chen, Qianqian & Gu, Yu & Tang, Zhiyong & Wei, Wei & Sun, Yuhan, 2018. "Assessment of low-carbon iron and steel production with CO2 recycling and utilization technologies: A case study in China," Applied Energy, Elsevier, vol. 220(C), pages 192-207.
    2. Juxian Hao & Xiancong Zhao & Hao Bai, 2017. "Collaborative Scheduling between OSPPs and Gasholders in Steel Mill under Time-of-Use Power Price," Energies, MDPI, vol. 10(8), pages 1-10, August.
    3. 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.
    4. 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).
    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. Liu, Kun & Guan, Xiaohong & Gao, Feng & Zhai, Qiaozhu & Wu, Jiang, 2015. "Self-balancing robust scheduling with flexible batch loads for energy intensive corporate microgrid," Applied Energy, Elsevier, vol. 159(C), pages 391-400.
    7. 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.
    8. 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.
    9. 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.
    10. Na, Hongming & Sun, Jingchao & Qiu, Ziyang & He, Jianfei & Yuan, Yuxing & Yan, Tianyi & Du, Tao, 2021. "A novel evaluation method for energy efficiency of process industry — A case study of typical iron and steel manufacturing process," Energy, Elsevier, vol. 233(C).
    11. 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.
    12. 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.
    13. Kalantzis, Fotios & Niczyporuk, Hanna, 2021. "Can European businesses achieve productivity gains from investments in energy efficiency?," EIB Working Papers 2021/07, European Investment Bank (EIB).
    14. Matino, Ismael & Dettori, Stefano & Colla, Valentina & Weber, Valentine & Salame, Sahar, 2019. "Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Sun, Minmin & Zhang, Jianliang & Li, Kejiang & Barati, Mansoor & Liu, Zhibin, 2022. "Co-gasification characteristics of coke blended with hydro-char and pyro-char from bamboo," Energy, Elsevier, vol. 241(C).
    16. J. F. F. Almeida & S. V. Conceição & L. R. Pinto & B. R. P. Oliveira & L. F. Rodrigues, 2022. "Optimal sales and operations planning for integrated steel industries," Annals of Operations Research, Springer, vol. 315(2), pages 773-790, August.
    17. Wang, Can & Zheng, Xinzhu & Cai, Wenjia & Gao, Xue & Berrill, Peter, 2017. "Unexpected water impacts of energy-saving measures in the iron and steel sector: Tradeoffs or synergies?," Applied Energy, Elsevier, vol. 205(C), pages 1119-1127.
    18. F. Wang & G. H. Huang & Y. Fan & Y. P. Li, 2020. "Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3199-3217, August.
    19. Yue, Hui & Worrell, Ernst & Crijns-Graus, Wina, 2021. "Impacts of regional industrial electricity savings on the development of future coal capacity per electricity grid and related air pollution emissions – A case study for China," Applied Energy, Elsevier, vol. 282(PB).
    20. Ye, Lian & Zhang, Jianliang & Wang, Guangwei & Wang, Chen & Mao, Xiaoming & Ning, Xiaojun & Zhang, Nan & Teng, Haipeng & Li, Jinhua & Wang, Chuan, 2023. "Feasibility analysis of plastic and biomass hydrochar for blast furnace injection," Energy, Elsevier, vol. 263(PD).

    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:164:y:2016:i:c:p:462-474. 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.