GA-XGBoost-based interpretable model for predicting molten iron yield of blast furnace
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 nor-malization,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 re-moved 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 regula-ting 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 im-prove production.Furthermore,by leveraging the prediction results,workers can pre-plan the trans-portation routes for the ladles,reducing heat dissipation and thus improving the cost-efficiency of blast furnace smelting.
blast furnace smeltingprediction of molten iron yieldGA-XGBoost algorithmgrey corre-lation analysisSHAP diagram
NETL
NSTL
万方数据
