Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by XGBoost and SHAP-A conscious-lab development

Surprisingly, no investigation has been explored relationships between operating variables and metallurgical responses of coal column flotation (CF) circuits based on industrial databases for under operation plants. As a novel approach, this study implemented a conscious-lab CL for filling this gap. In this approach, for developing the CL dedicated to an industrial CF circuit, SHapley Additive exPlanations (SHAP) and extreme gradient boosting (XGBoost) were powerful unique machine learning systems for the first time considered. These explainable artificial intelligence models could effectively convert the dataset to a basis that improves human capabilities for better understanding, reasoning, and planning the unit. SHAP could provide precise multivariable correlation assessments between the CF dataset by using the Tabas Parvadeh coal plant (Kerman, Iran), and showed the importance of solid percentage and washing water on the metallurgical responses of the coal CF circuit. XGBoost could predict metallurgical responses (R-square > 0.88) based on operating variables that showed quite higher accuracy than typical modeling methods (Random Forest and support vector regression). (C) 2021 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

Automated summary

This study introduced a conscious-lab to fill the crucial data gap in industrial coal column flotation circuits, and conducted experiments using SHAP and XGBoost machine learning systems. The research found that converting data into explainable artificial intelligence models can enhance human understanding and planning of the unit, and XGBoost's predictions of metallurgical responses in coal CF circuits are highly accurate.