摘要
在烧结生产过程中,烧结矿粒度是评价烧结矿质量的重要指标之一。为提高烧结生产水平,针对烧结过程检测的滞后性和粒级占比调控预测问题,建立基于整体生产参数的烧结矿粒级占比预测模型;将经过特征选择之后的12个关键参数作为输入变量,对应的烧结矿粒级占比作为输出变量;通过缺失数据填补、数据增强以及异常点替换等数据预处理方法,获取用于预测烧结矿粒级占比的高质量数据集;采用鱼鹰优化算法(OOA)和类别特征梯度提升算法(CatBoost)构建预测模型。结果表明,OOA-CatBoost算法模型的平均绝对误差(MAE)为0.2769,均方误差(MSE)为0.0433,决定系数(R^(2))为0.9499。对比侏儒猫鼬优化(DMO)算法、麻雀搜索算法优化(SSA)、鱼鹰优化算法优化的随机森林(RF)、轻量梯度提升机(LightGBM)以及极限梯度提升算法(XGBoost)等其他11个机器学习模型,本文模型取得良好的预测效果。基于工业实测数据,OOA-CatBoost算法对烧结矿粒级占比的平均预测误差达到0.0852,可为优化原料配比、混合料参数和烧结机参数调控提供理论指导,从而提升优质烧结矿的粒级占比。
In the process of sintering production,sinter grade is one of the important indicators to evaluate the quality of sinter.In order to improve the level of sintering production,a prediction model of sinter grade proportion based on the overall production parameters is established in order to solve the problems of hysteresis and grade proportion control and prediction in the sintering process.The 12 key parameters after feature selection are taken as input variables,and the corresponding sinter grade proportion is used as the output variable.Through data preprocessing methods such as missing data filling,data augmentation,and outlier point replacement,high-quality datasets for predicting the proportion of sinter fraction are obtained.The Osprey Optimization Algorithm(OOA)and Categorical Feature Gradient Boosting Algorithm(CatBoost)are used to construct the prediction model.The results show that the mean absolute error(MAE)of the OOA-CatBoost algorithm model is 0.2769,the mean square error(MSE)is 0.0433,and the coefficient of determination(R^(2))is 0.9499.Compared with 11 other machine learning models,such as the dwarf mongoose optimization(DMO)algorithm,the sparrow search algorithm optimization(SSA),the random forest(RF)optimized by the osprey optimization algorithm,the lightweight gradient booster(LightGBM)and the extreme gradient boosting algorithm(XGBoost),the proposed model achieves good prediction results.Based on the industrial measured data,the average prediction error of the OOA-CatBoost algorithm for the proportion of sinter grade can reach 0.0852,which can provide theoretical guidance for optimizing the ratio of raw materials,mixing parameters and sintering machine parameters,so as to improve the grade proportion of high-quality sinter.
作者
李喆
王猛
董振
姜娟娟
李杰
杨爱民
LI Zhe;WANG Meng;DONG Zhen;JIANG Juanjuan;LI Jie;YANG Aimin(North China University of Science and Technology Institute of Metallurgy and Energy,Tangshan 063210,Hebei,China;North China University of Science and Technology College of Science,Tangshan 063210,Hebei,China;North China University of Science and Technology Hebei Engineering Research Center for Iron Ore Optimization and Pre-Iron Process Intelligence,Tangshan 063210,Hebei,China)
出处
《烧结球团》
北大核心
2025年第3期47-58,共12页
Sintering and Pelletizing
基金
国家自然科学基金资助项目(52074126)
河北省自然科学基金资助项目(E2022209110)。
