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Dynamic Multi-objective Operation Optimization of Blast Furnace Based on Evolutionary Algorithm

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2061))

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Abstract

Blast furnaces play a critical role in the steel industry, and their operational optimization is crucial for energy conservation and emissions reduction. This paper examines the impact of changes in operational conditions on blast furnace performance. We propose a dynamic multi-objective optimization algorithm based on multiple short time series (MT-DC-RVEA) to solve the constructed dynamic multi-objective operational optimization model for blast furnaces. Experimental results validate the effectiveness of the proposed algorithm in solving the operational optimization model for blast furnace operations.

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Acknowledgements

This research was supported by the Fund for the National Natural Science Foundation of China (62073067, 62303102).

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Authors and Affiliations

  1. Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, Shenyang, 110819, China

    Yumeng Zhao, Jingchuan Zhang, Meng Jiang, Kai Fu & Qiyuan Deng

  2. Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Shenyang, 110819, China

    Yumeng Zhao, Jingchuan Zhang, Meng Jiang, Kai Fu & Qiyuan Deng

  3. National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China

    Xianpeng Wang

  4. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang, 110819, China

    Xianpeng Wang

Authors
  1. Yumeng Zhao
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  2. Jingchuan Zhang
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  3. Meng Jiang
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  4. Kai Fu
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  5. Qiyuan Deng
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  6. Xianpeng Wang
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Corresponding author

Correspondence to Xianpeng Wang .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Central South University, Changsha, China

    Yong Wang

  3. Huazhong University of Science and Technology, Wuhan, China

    Jianqing Lin

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhao, Y., Zhang, J., Jiang, M., Fu, K., Deng, Q., Wang, X. (2024). Dynamic Multi-objective Operation Optimization of Blast Furnace Based on Evolutionary Algorithm. In: Pan, L., Wang, Y., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2023. Communications in Computer and Information Science, vol 2061. Springer, Singapore. https://doi.org/10.1007/978-981-97-2272-3_19

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  • DOI: https://doi.org/10.1007/978-981-97-2272-3_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2271-6

  • Online ISBN: 978-981-97-2272-3

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