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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

Author

Listed:
  • Zhao, Xiancong
  • Bai, Hao
  • Lu, Xin
  • Shi, Qi
  • Han, Jiehai

Abstract

In integrated steel works, byproduct gases are generated in the iron and steel making process, which accounts for approximately 30% of the total energy involved. The efficient utilisation of these gases is significant for energy saving and CO2 reduction in the iron and steel industry. In this paper, a mixed integer linear programming (MILP) model was proposed to optimise the byproduct gas management for the minimisation of operation costs and the efficient usage of energy. Compared with previous models, this proposed model considered the influence of the boiler penalty factor (BPF) and gasholder penalty factor (GPF) on optimisation results. The sum of the standard deviation volume (SSDV) and total switching times (TST) are defined to evaluate the effect of penalty factors on gasholder and boiler stability. The results of a case study indicate that the SSDV and TST are sensitive to the GPF and BPF, i.e., penalty factors have a large impact on optimisation results. Because the SSDV and TST are two confronted variables, Pareto optimality was applied to identify reasonable penalty factors which were used in the MILP model to obtain reasonable optimisation of the byproduct gas system. The optimisation results demonstrate that, compared with manual operation, the planning of the optimal distribution of byproduct gases proposed in this study can reduce the fluctuation of the volume of the gasholders and the load of the boilers to make the operation of the byproduct gas system safe and stable. Furthermore, according to sensitivity analysis, the stability of gasholders and boilers are sensitive to electricity price change.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:148:y:2015:i:c:p:142-158
    DOI: 10.1016/j.apenergy.2015.03.046
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    9. 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.
    10. 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.
    11. Waldemarsson, Martin & Lidestam, Helene & Karlsson, Magnus, 2017. "How energy price changes can affect production- and supply chain planning – A case study at a pulp company," Applied Energy, Elsevier, vol. 203(C), pages 333-347.
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. 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).
    17. 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).
    18. 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.

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