Slag viscosity plays a crucial role in the smelting process.A slag viscosity prediction model was developed by integrating hyperparameter optimization algorithms,machine learning,and SHapley Additive exPlanations(SHAP...Slag viscosity plays a crucial role in the smelting process.A slag viscosity prediction model was developed by integrating hyperparameter optimization algorithms,machine learning,and SHapley Additive exPlanations(SHAP)analysis.The developed slag viscosity prediction models were evaluated using multiple statistical metrics,leading to the identification of the optimal model—Bayesian optimization-based categorical boosting(BO-CatBoost).And this model was further compared with existing models,including NPL model,FactSage+Roscoe-Einstein(RE)equation,artificial neural network model+RE equation,Riboud model+RE equation,and Zhang model.The results indicate that the slag viscosity prediction model based on BO-CatBoost outperforms all other models,achieving a coefficient of determination of 0.9897,a root mean square error of 1.0619,a mean absolute error of 0.6133,and a hit ratio of 95.1%.The global interpretability analysis of SHAP analysis was used to reveal the importance degree of different features on slag viscosity.The local interpretability analysis of SHAP analysis was used to obtain the quantitative influence of different features on slag viscosity in specific samples.The high-accuracy and interpretable slag viscosity prediction model developed is beneficial to the intelligent design of slag composition.展开更多
基金funded by the National Natural Science Foundation of China(No.52374321)the National Key Research and Development Program of China(No.2024YFB3713602)the Youth Science and Technology Innovation Fund of Jianlong Group-University of Science and Technology Beijing(No.20231235).
文摘Slag viscosity plays a crucial role in the smelting process.A slag viscosity prediction model was developed by integrating hyperparameter optimization algorithms,machine learning,and SHapley Additive exPlanations(SHAP)analysis.The developed slag viscosity prediction models were evaluated using multiple statistical metrics,leading to the identification of the optimal model—Bayesian optimization-based categorical boosting(BO-CatBoost).And this model was further compared with existing models,including NPL model,FactSage+Roscoe-Einstein(RE)equation,artificial neural network model+RE equation,Riboud model+RE equation,and Zhang model.The results indicate that the slag viscosity prediction model based on BO-CatBoost outperforms all other models,achieving a coefficient of determination of 0.9897,a root mean square error of 1.0619,a mean absolute error of 0.6133,and a hit ratio of 95.1%.The global interpretability analysis of SHAP analysis was used to reveal the importance degree of different features on slag viscosity.The local interpretability analysis of SHAP analysis was used to obtain the quantitative influence of different features on slag viscosity in specific samples.The high-accuracy and interpretable slag viscosity prediction model developed is beneficial to the intelligent design of slag composition.
