Abstract:The comprehensive energy consumption of tin smelting process accounts for 90% of the entire tin production process, which has great energy-saving potential. Address to the difficulty in establishing the comprehensive energy consumption mechanism model of tin smelting process and the low prediction accuracy, the Gray Wolf Optimization Support Vector Machine Regression (GWO-SVR) model was proposed to predict the comprehensive energy consumption of tin smelting process. Taking a tin smelter as an example, the proposed model was compared with the SVR, RF (Random Forest), BP (Back Propagation Neural Network) and LR (Linear Regression) models. The results show that the GWO-SVR model yields the most desirable prediction results, and has great advantages over other machine learning algorithms in terms of prediction accuracy. Furthermore, using SHAP values to explain the GWO-SVR model from both global interpretation and single-sample interpretation and visualize the contribution of features to the output increases the interpretability of GWO-SVR, and thus develops a reliable energy-saving strategy.