叠加泛化集成的烧结水分预测LGBM-RF-XGB混合模型

A Stacked Generalization Ensemble-based Hybrid LGBM-RF-XGB Model for Sintering Moisture Prediction

针对烧结混合料水分预测的问题,引入了基于叠加泛化集成的建模方法,提出了Robust Scaler-Rank Gauss(RS-RG)混合算法对输入叠加模型的数据进行处理,进而建立了基于叠加泛化集成的烧结混合料水分预测轻量梯度提升机-随机森林-极端梯度提升机(LGBM-RF-XGB)混合模型,可以在烧结料混合前预知水分值.LGBM-RF-XGB叠加模型的内部机制是先从LGBM和RF模型生成元数据,再使用XGB模型计算最终预测.结合烧结现场数据,将提出的叠加模型与多个基准模型进行了对比仿真实验.结果表明,所提出叠加模型的精度和误差均优于用于对比的基准模型,满足烧结工艺要求,可以用于实际生产中的烧结混合料水分提前预测,为加水量自动控制提供理论依据.

In order to predict the moisture of the mixture,a modeling method based on stacked generalization ensemble is introduced,and a Robust Scaler-Rank Gauss(RS-RG)hybrid algorithm is proposed to process the data input to the stacking model,and then the LGBM-RF-XGB hybrid model is established for sintered mixture moisture prediction based on stacked generalization ensemble,which can predict the moisture value before sinter mixture mixing.The internal mechanism of the LGBM-RF-XGB overlay model consists of generating metadata from the LGBM and RF models to calculate the final prediction using the XGB model.The proposed stacking model was simulated in comparison with several reference models by combining the sintering site data.The results show that the accuracy and error of the proposed stacking model are better than those of the reference models used for comparison,which meets the sintering process requirements.The proposed algorithm can be used for advance prediction of sinter mix moisture in actual production and provides a theoretical basis for automatic control of water addition.