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The cooling efficiency and quality of continuous castingContinuous casting slab are closely related to the volume of secondary cooling water, and the optimizationOptimizations of the secondary cooling water volume in the secondary cooling zone is the guarantee of slab quality. In this paper, minimizing the average temperatureTemperature difference between the surface center temperatureTemperature and the target temperatureTemperature of each cooling zone is taken as the objective function, and the metallurgical criteria are taken as constraints. Differential evolution, particle swarm optimizationOptimizations and firefly algorithm are used to establish the optimal model of secondary cooling water volume. Those models are verified with the production data of a steel plant. The results show that the average temperatureTemperature difference of PSO is the lowest, which is 3.9 °C. By optimizing the hyperparameters of PSO, the final average temperatureTemperature difference is reduced to 2.0 °C, and the model can meet the requirements of the production.

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Intelligent Optimization Algorithm-Based Optimization Model of Water Volume in Secondary Cooling Zone of Continuous Casting

  • 会议论文

  • 发表日期：

    2025年

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  • Cheng-Hong Li,
  • Ming-Mei Zhu,
  • Xian-Wu Zhang,
  • Zhen-Jiang Yang,
  • Kun-Chi Jiang

摘要

The cooling efficiency and quality of continuous castingContinuous casting slab are closely related to the volume of secondary cooling water, and the optimizationOptimizations of the secondary cooling water volume in the secondary cooling zone is the guarantee of slab quality. In this paper, minimizing the average temperatureTemperature difference between the surface center temperatureTemperature and the target temperatureTemperature of each cooling zone is taken as the objective function, and the metallurgical criteria are taken as constraints. Differential evolution, particle swarm optimizationOptimizations and firefly algorithm are used to establish the optimal model of secondary cooling water volume. Those models are verified with the production data of a steel plant. The results show that the average temperatureTemperature difference of PSO is the lowest, which is 3.9 °C. By optimizing the hyperparameters of PSO, the final average temperatureTemperature difference is reduced to 2.0 °C, and the model can meet the requirements of the production.