Explainable Surrogate-Based Knowledge Extraction from DEM Simulations: Cross-System Algorithm Selection and SHAP Interpretability for Granular Material Handling Optimization

Extracting transferable design knowledge from Discrete Element Method (DEM) simulations remains challenging in granular material handling. We develop an explainable surrogate framework for two solar-panel-recycling subsystems: a silo discharge system (outlet width 42–111 mm; hopper half-angle 30–60°) and an inclined belt conveyor (fin height 20–50 mm; belt velocity 0.109–0.627 m/s). Five algorithms—Response Surface Methodology (RSM), Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting Machine (GBM), and Gaussian Process Regression (GPR)—were evaluated by leakage-free grouped five-fold cross-validation using 34 design points per system (22 factorial expanded by Latin Hypercube Sampling). The best surrogate is response-specific: ANN was most accurate for silo discharge (R2 = 0.964, RMSE = 0.91 kg/s); GPR gave the highest raw belt-MFR accuracy (R2 = 0.976 ± 0.023, RMSE = 0.43 kg/s), though the interpretable RSM was near-equivalent and was adopted for optimization; and RSM was near-perfect for belt discharge angle (R2 = 0.999 ± 0.001, RMSE = 0.057°). GPR performed the worst for silo discharge (R2 = 0.384), confirming the algorithm selection must match the response complexity. SHAP analysis identified outlet width (78.6%) and belt velocity (58.9–76.8%) as dominant predictors. The proposed SHAP Asymmetry Ratio (SAR) offers an exploratory diagnostic for algorithm pre-selection in expensive simulation-driven surrogate workflows.