A Copper Flotation Concentrate Grade Prediction Method Based on an Improved Extreme Gradient Boosting Algorithm

The flotation stage is a critical segment of mineral processing production. In copper concentrate flotation, predicting the concentrate grade is essential for maintaining a stable flotation process, ensuring concentrate quality, and enhancing profits. To improve the prediction accuracy for the concentrate grade, we propose a prediction method based on an improved eXtreme Gradient Boosting (XGBoost) model using real copper concentrate flotation data in the paper. To address the issues of outliers and missing values in the collected dataset, we firstly present an outlier detection and imputation method using the Inter-Quartile Range (IQR) method and the MissForest (MF) algorithm. An XGBoost-based model is developed for predicting the copper concentrate grade. The model is trained using some key indicators, including feed grade, ore throughput, reagent concentration, pulp flow rate, air flow rate, level, and pH value, as the input features. Moreover, hyper-parameter tuning is optimized based on a Tree-Structured Parzen Estimator (TPE). Combining the IQR/MissForest with TPE-optimized XGBoost can enable an end-to-end prediction pipeline for the copper concentrate grade in the flotation process to address the issues of data anomalies and missing values in the flotation process, as well as the low efficiency of multi-parameter tuning, ensuring the accuracy of data processing and the effectiveness of model training. The experimental results demonstrate that compared with some traditional prediction methods, such as support vector machines, the proposed method achieves about a 25.3% reduction in the Root Mean Square Error (RMSE), indicating our method’s superior performance.