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Research on Energy-Saving Scheduling of a Forging Stock Charging Furnace Based on an Improved SPEA2 Algorithm

Author

Listed:
  • Fei He

    (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210000, China)

  • Kang Shen

    (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210000, China)

  • Li Guan

    (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210000, China)

  • Mingming Jiang

    (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210000, China)

Abstract

In order to help the forging enterprise realize energy conservation and emission reduction, the scheduling problem of furnace heating was improved in this paper. Aiming at the charging problem of continuous heating furnace, a multi-objective furnace charging model with minimum capacity difference and waiting time was established in this paper. An improved strength Pareto evolutionary algorithm 2 (SPEA2) algorithm was designed to solve this problem. The original fitness assignment strategy, crossover operator and population selection mechanism of SPEA2 are replaced with DOPGA (Domination Power of an Individual Genetic Algorithm), adaptive cross operator, and elitist strategy. Finally, the effectiveness and feasibility of the improved SPEA2 was verified by actual arithmetic example. The comparison of results gained from three methods shows the superiority of the improved SPEA2 in solving this problem. Compared with strength Pareto evolutionary algorithm (SPEA) and SPEA2, the improved SPEA2 can get a better solution without increasing time complexity, the heating time is reduced by total 93 min, and can save 7533 GJ energy. The research in this paper can help the forging enterprise improve furnace utilization, reduce heating time and unnecessary heating preservation time, as well as achieve sustainable energy savings and emissions reduction.

Suggested Citation

  • Fei He & Kang Shen & Li Guan & Mingming Jiang, 2017. "Research on Energy-Saving Scheduling of a Forging Stock Charging Furnace Based on an Improved SPEA2 Algorithm," Sustainability, MDPI, vol. 9(11), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2154-:d:119891
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    References listed on IDEAS

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    1. Engin Ergul & Ilyas Eminoglu, 2014. "DOPGA: a new fitness assignment scheme for multi-objective evolutionary algorithms," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 407-426.
    2. Si, Minxing & Thompson, Shirley & Calder, Kurtis, 2011. "Energy efficiency assessment by process heating assessment and survey tool (PHAST) and feasibility analysis of waste heat recovery in the reheat furnace at a steel company," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2904-2908, August.
    3. Yifei Tong & Jingwei Li & Shai Li & Dongbo Li, 2016. "Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise," Sustainability, MDPI, vol. 8(2), pages 1-17, February.
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    Cited by:

    1. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.

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