Data for: Investigating occurrences of valuable trace elements in African coals as potential byproducts of coal and coal combustion products

Analysis of African coal

Data for: Investigating occurrences of valuable trace elements in African coals as potential byproducts of coal and coal combustion products

Analysis of African coalsTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Modeling of particle sizes for industrial HPGR products by a unique explainable AI tool- A “Conscious Lab” development

High-Pressure Grinding Rolls (HPGR), as a modified type of roll crushers, could intensively reduce the energy consumptions in the mineral processing comminution units. However, several problems counted for their operational modeling, especially in the industrial scales. Expanding a conscious laboratory (CL) as a recently developed concept based on the recorded datasets from the HPGR operational variables could be tackled those complications and fill the gap. Moreover, constructing such a CL base on explainable artificial intelligence (EAI) systems would be an innovative point for the digitalizing powder technology industries. Using a robust EAI model as a strategic approach could significantly improve system transparency and trustworthiness to convert any complicated black-box machine learning to a logical human basis system. This study introduced the SHapley Additive exPlanations (SHAP) and extreme gradient boosting (XGBoost) as the latest powerful EAI tool for the CL modeling of the particle sizes produced by an industrial HPGR (P80) in the Fakoor Sanat iron ore processing plant (Kerman, Iran). SHAP precisely assessed multivariable relationships between the monitored operational variables and correlated them with the HPGR P80. SHAP values showed relationship magnitudes among variables and ranked them based on their effectiveness on the P80 prediction. The working gap demonstrated the highest importance for the P80 prediction. XGBoost could precisely predict the P80 and showed higher accuracy than typical machine learning methods (random forest and support vector regression) for constructing the CL of HPGR. These significant outcomes would open a new window for robust consideration of the EAI models within powder technology

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)