基于GA-XGBoost算法的高炉可解释铁水产量预测模型  

GA-XGBoost-based interpretable model for predicting molten iron yield of blast furnace

作　　者：孟凯 刘小杰 伊凤永 段一凡 陈树军 刘二浩 MEGN Kai;LIU Xiaojie;YI Fengyong;DUAN Yifan;CHEN Shujun;LIU Erhao(Chengde Branch,HBIS Group Co.,Ltd.,Chengde 067000,China;School of Metallurgy and Energy,North China University of Science and Technology,Tangshan 063210,China)

机构地区：[1]河钢集团有限公司承德分公司,河北承德067000 [2]华北理工大学冶金与能源学院,河北唐山063210

出　　处：《冶金自动化》2024年第6期108-121,共14页Metallurgical Industry Automation

基　　金：唐山市科技局项目(23130202E);河北省科技研发平台建设专项(23560301D)。

摘　　要：针对高炉出铁前铁水产量未知导致铁水包难以高效中转与调度的问题,使用遗传算法优化的极度梯度提升树(genetic algorithm optimized extreme gradient boosting,GA-XGBoost)算法构建并训练了铁水产量预测模型。经过测试与多模型对比,所提方法在多特征数据集的铁水产量预测问题中具有一定优势,在误差10 t的范围内取得89.64%的预测准确率。首先修正了实验数据集的缺失值和异常值,在归一化后获得结构化的数据用于模型训练;然后,采用灰色关联分析方法筛选了铁水产量的主要影响因素,并结合工艺原理去除冗余参数;最后确定15个特征变量用于构建模型的输入向量。同时,针对预测结果,采用沙普利值可加性解释(Shapley additive explanations,SHAP)原理量化了不同操作参数对铁水产量的贡献程度,为高炉的参数调控工作提供数据支持。本研究实现了基于炉次特征的铁水产量预测任务,不仅有利于更高效的高炉调控以促进铁水产量的提高,同时结合预测结果,工作人员可以提前部署铁水包的运输路线,减少铁水包的热量耗散,进一步实现高炉冶炼的降本增效。To address the issue of unknown molten iron yield prior to tapping in blast furnaces,which leads to inefficiencies in transportation and scheduling of molten iron ladles,a prediction model about molten iron yield constructed and trained using the GA-XGBoost algorithm was proposed.After testing and comparing multiple models,the proposed method demonstrates a certain advantage in predicting molten iron yield from a multi-feature dataset,achieving an accuracy of 89.64%within a±10 t error range.Firstly,the missing and anomalous values in the experimental dataset is corrected.After normalization,the structured data is used for model training.The grey relational analysis method is then used to identify the key influencing factors of molten iron yield,and redundant parameters are removed based on process principles.In the end,15 feature variables are selected to form the input vector for model construction.Additionally,to quantify the impact of different operational parameters on molten iron yield,the SHAP calculation framework is employed,providing data support for regulating parameter in blast furnaces.This study achieves the prediction task of molten iron yield of blast furnace based on furnace characteristics,contributing to more efficient blast furnace regulation to improve production.Furthermore,by leveraging the prediction results,workers can pre-plan the transportation routes for the ladles,reducing heat dissipation and thus improving the cost-efficiency of blast furnace smelting.

关 键 词：高炉冶炼 铁水产量预测 GA-XGBoost算法 灰色关联分析 SHAP图 

分 类 号：TF53[冶金工程—钢铁冶金]