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The study of heat-mass transfer characteristics and multi-objective optimization on electric arc furnace

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  • Zhang, Xuan-Kai
  • He, Ya-Ling
  • Li, Meng-Jie
  • Hu, Xin

Abstract

It is crucial to reveal the energy conversion and heat and mass transfer characteristics in the electric arc furnaces (EAFs). In this paper, a transient mathematical model was built to adequately capture the distribution of multi-physical fields in EAF. The numerical modeling method was verified against brown corundum smelting experiment data. In addition, the influence on the brawn corundum smelting process of the typical operating parameters has been examined, and the major problems which limit the performance improvement of smelting were analyzed. Moreover, four indicators were proposed to comprehensively evaluate the EAF performance, consisting of liquid volume fraction β, energy efficiency η, temperature rise index ς, and temperature uniformity index ξ. Furthermore, the optimal combination of operating parameters was selected automatically based on the back propagation (BP) neural network predicting model and non-domestic sorting genetic algorithm II (NSGA-II). The results showed that the local overheating phenomenon is serious and that homogenizing the energy distribution in the furnace is the key to improving the melting performance. Increasing electrode immersion and voltage can significantly increase the rate of heat and mass transfer. In the case of ψ = 0.75, υ = 0.6, when χ is increased from 0.2 to 0.6, τmax is shortened by 13.3% and ηmax is increased by 12.0%. While with ψ = 0.75 and χ = 0.4, τmax is reduced by 36.0% when υ increases from 0.2 to 1.0, but ηmax is also decreased by 7.0%. It was found that decreasing the height diameter ratio will improve the overall EAF smelting performance, but it also increases the area of heat loss. In the case of χ = 0.4, υ = 0.6, ξave and ηmax improved by 3.1 and 14.5%, respectively, when ψ decreases from 1.0 to 0.5. The EAF performances are considerably enhanced in comparison to the operating basic parameters (ψ = 0.75, χ = 0.4 and υ = 0.6), subject to optimal operation parameters (ψ = 0.61, χ = 0.59 and υ = 0.59). In this case, τmax and ςmax reduced by 6.2 and 4.1%, respectively, while ηmax and ξave increased by 6.8 and 1.0%, respectively.

Suggested Citation

  • Zhang, Xuan-Kai & He, Ya-Ling & Li, Meng-Jie & Hu, Xin, 2022. "The study of heat-mass transfer characteristics and multi-objective optimization on electric arc furnace," Applied Energy, Elsevier, vol. 317(C).
    • Handle: RePEc:eee:appene:v:317:y:2022:i:c:s0306261922005220
    DOI: 10.1016/j.apenergy.2022.119147
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    References listed on IDEAS

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    1. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    2. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    3. Kirschen, Marcus & Risonarta, Victor & Pfeifer, Herbert, 2009. "Energy efficiency and the influence of gas burners to the energy related carbon dioxide emissions of electric arc furnaces in steel industry," Energy, Elsevier, vol. 34(9), pages 1065-1072.
    4. An, Runying & Yu, Biying & Li, Ru & Wei, Yi-Ming, 2018. "Potential of energy savings and CO2 emission reduction in China’s iron and steel industry," Applied Energy, Elsevier, vol. 226(C), pages 862-880.
    5. Xu, Yang & Ren, Qinlong & Zheng, Zhang-Jing & He, Ya-Ling, 2017. "Evaluation and optimization of melting performance for a latent heat thermal energy storage unit partially filled with porous media," Applied Energy, Elsevier, vol. 193(C), pages 84-95.
    6. Jiang, Han & Xi, Zhongli & A. Rahman, Anas & Zhang, Xiaoqing, 2020. "Prediction of output power with artificial neural network using extended datasets for Stirling engines," Applied Energy, Elsevier, vol. 271(C).
    7. Wei, Li & Yan, Fuwu & Hu, Jie & Xi, Guangwei & Liu, Bo & Zeng, Jiawei, 2017. "Nox conversion efficiency optimization based on NSGA-II and state-feedback nonlinear model predictive control of selective catalytic reduction system in diesel engine," Applied Energy, Elsevier, vol. 206(C), pages 959-971.
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