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

doi:10.12068/j.issn.1005-3026.2023.09.004

Abstract

Abstract: 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.

Key words: moisture of sintering mixture (MSM); light gradient boosting machine (LGBM); random forest (RF); extreme gradient boosting machine (XGB); RS-RG data processing

CLC Number:

TP301.6

HUANG Chuan-qi， REN Yu-qian， WU Zhao-xia. A Stacked Generalization Ensemble-based Hybrid LGBM-RF-XGB Model for Sintering Moisture Prediction[J]. Journal of Northeastern University(Natural Science), 2023, 44(9): 1245-1250.

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