ABSTRACT Conditioning process intensification is necessary for improving the fly ash flotation effect. Understanding the relationship between conditioning parameters and flotation results is crucial for optimizing the conditioning-flotation process. This study developed a machine learning framework for predicting the yield, loss on ignition, and removal rate of unburned carbon using data from conditioning-flotation experiments. Feature variables of conditioning speed, impeller type, stirred tank diameter, collector dosage, and frother dosage were employed to develop five machine learning models. After tuning the hyper-parameters with GridSearchCV technique, it was found that Random Forest Regression model, eXtreme Gradient Boosting Regression model, and Gradient Boosting Regression model demonstrated good agreements between the predicted data and test data for the yield, loss on ignition, and removal rate of unburned carbon. The maximum R2 values were 0.9184, 0.8339, and 0.9434, respectively, while the minimum MSE values were 3.30, 5.82, and 20.59, respectively. The interpretable analysis using SHapley Additive exPlanations (SHAP) method indicated that conditioning speed was the most significant feature variable affecting the yield, loss on ignition, and removal rate of unburned carbon, while impeller type had a secondary impact. This investigation is expected to reveal the important role of conditioning process intensification in optimizing fly ash flotation.
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