关于连铸二次冷却水量优化预测仿真

为了有效控制钢厂中连铸二次冷却水量,减少生产成本,提高铸坯质量,提出一种基于结合交叉变异的粒子群算法优化最小二乘支持向量机参数的连铸二次冷却水量预测方法。首先利用提出的一种交叉变异机制对标准粒子群算法进行优化,并将优化后的参数引入最小二乘支持向量机的预测模型中,带入生产中的水量历史数据作为输入样本集进行学习,使模型在训练过程中有效地提高了准确性,其输出的训练结果描述了水量的变化。仿真结果表明,上述模型具有很好的稳定性和泛化性,能够获得高精度的二冷预测水量,为实际生产中连铸二冷配水的优化提供参考。

Prediction Simulation of Secondary Cooling Water Quantity in Continuous Casting

In order to effectively control the secondary cooling water quantity in the steel mill, reduce the production cost and improve the quality of the slab, this paper proposes a prediction method for continuous cooling secondary cooling water quantity based on the particle swarm optimization algorithm combined with the cross-variation to optimize the parameters of the least squares support vector machine. Firstly, the proposed particle swarm optimization algorithm was optimized using a proposed cross-variation mechanism, the optimized parameters were introduced into the prediction model of the least squares support vector machine, and the water quantity data brought into the historical production was used as the input sample set for learning. The model effectively improves the accuracy during the training process, and the output training results describe the change in water volume. The simulation results show that the model has good stability and generalization, and can obtain high-precision predicted water volume of the second cold, which provides a reference for the optimization of continuous cooling and cold water distribution in actual production.