Interpretable Prediction of Silica Content in Iron Ore Flotation Using Machine Learning

This work presents the development of an interpretable machine learning model for predicting silica content in the concentrate of an iron ore flotation process. The explainable boosting machine (EBM) algorithm was chosen for its ability to provide interpretable insights into the rationale behind each prediction, allowing operators to gain a clearer understanding of process conditions and make more informed adjustments. The model was trained using industrial data collected over 18 months from an iron ore flotation plant in Brazil. It achieved 87% recall and 73% precision in detecting the critical class of high silica level, with an overall accuracy of 76%.