基于改进GWO-ELM的热轧带钢卷取温度预测

Improved GWO-ELM based hot rolled strip coiling temperature prediction

卷取温度控制精度是影响带钢产品性能的主要因素之一,提高卷取温度控制精度和保证卷取命中率是热轧领域的重点问题。针对某钢厂现有的卷取温度设定模型中存在个别钢种命中率低的问题,结合数据挖掘及现场专家经验,提出了一种基于灰狼优化极限学习机的新建模思路,并引入Henon映射、小孔成像策略和权重因子策略来改进灰狼算法,建立了基于改进灰狼优化极限学习机(IGWO-ELM)的热轧带钢卷取温度预测模型,并与ELM模型、GA-ELM模型和GWO-ELM模型进行对比。模型结果表明:建立的IGWO-ELM模型,预测卷取温度在±3℃之内的命中率为91.1%,在±4℃之内的命中率为96.7%,均好于对比模型,具有广泛的实际应用前景。

Coiling temperature control precision is the main elements influencing the presentation of strip steel items,and further developing curling temperature control exactness and guaranteeing winding hit rate is a main point of contention in the field of hot rolling.To resolve the issue of low hit pace of individual steel grades in the current coiling temperature setting model of a steel mill,a new modeling idea based on gray wolf optimized extreme learning machine is proposed combining data mining and field master insight,and Henon mapping,small-hole imaging strategy and weight factor strategy are introduced to improve the gray wolf algorithm,and a hot-rolled based on improved gray wolf optimized extreme learning machine(IGWO-ELM)is established.Strip coiling temperature prediction model based on the improved gray wolf optimized extreme learning machine(IGWO-ELM)and contrasted and ELM model,GA-ELM model and GWO-ELM model.The model results show that the established IGWO-ELM model has a hit rate of 91.1%for predicting the coiling temperature within±3℃and 96.7%for predicting the coiling temperature within±4℃,both of which are better than the comparison models and have a wide range of pragmatic application prospects.