针对热轧带钢的厚度数学模型耦合性强、精度低等问题,提出了一种带钢在线厚度预测算法.首先使用轧制数据,利用轻量级的梯度提升机(light gradient boosting machine,LightGBM)算法建立在线厚度预测模型;然后采用改进蝙蝠优化算法(improved bat algorithm,IBA)改善LightGBM的模型参数,并通过自学习系统优化结果;最后对比预测结果和真实厚度,验证预测模型准确性.实验结果表明,IBA-LightGBM模型能够快速高精度在线预测带钢厚度,在预测2 mm、3 mm、4 mm和5.65 mm规格的带钢时,均方根误差ERMS(root mean square error,RMSE)可以分别控制在11.0μm、11.5μm、11.6 μm和16.4μm以内.结果可改善热轧带钢的厚度数学模型的精度,提高厚度控制系统的水平.
Online thickness prediction of hot rolling strip based on IBA-LightGBM
An online thickness prediction algorithm for strip steel was proposed to address issues of strong coupling and low accuracy in the thickness mathematical model.Firstly,the rolling data is used to establish an online thickness prediction model based on light gradient boosting machine(LightG-BM)model.Then improved bat algorithm(IBA)is applied to improve the LightGBM model parame-ters,and a self-learning system is deployed to optimize the results.Finally,the predicted results are compared with the actual thickness to verify the accuracy of the prediction model.The experimental results show that the online thickness prediction algorithm can quickly and accurately predict the strip thickness.When the IBA-LightGBM model was used to predict the 2 mm,3 mm,4 mm,and 5.65 mm strips,root mean square error ERMS(RMSE)can be controlled within 11.0 μm,11.5 μm,11.6 μm and 16.4 μm respectively.The results can improve the accuracy of the thickness mathematical model for hot rolling strip and enhance the level of the thickness control system.
hot rollingmachine learningLightGBMIBAonline thickness prediction
万方数据
维普
