Modeling the working pressure of a cement vertical roller mill using SHAP-XGBoost: A “conscious lab of grinding principle” approach

Abstract

The optimization and control of Vertical Roller Mill (VRM) circuits are critical for industrial processes, yet limited modeling has slowed progress in operator training, error minimization, and laboratory cost savings. To overcome limitations, the innovative “Conscious Lab” (CL) was introduced, utilizing industrial datasets and Explainable Artificial Intelligence (XAI) techniques. For the first time, CL combines Shapley Additive Explanations (SHAP) with machine learning models, such as XGBoost and Random Forest, to optimize VRM operations. Differential pressure and feed rate were identified as the most influential parameters of working pressure, essential for maintaining stable operations. Robust linear correlations (coefficients: 0.94 for feed rate, 0.84 for main drive power, and 0.83 for differential pressure) and nonlinear marginal plots (0.95, 0.81, and 0.81) highlighted how increases in these parameters significantly raise working pressure. The XGBoost model achieved remarkable prediction accuracy (0.99 for training and 0.98 for testing/validation) with a low RMSE (0.01), confirmed by 5-fold cross-validation. SHAP analysis further verified the relationship between working pressure and key parameters, aligning with VRM grinding principles. The CL approach introduces a data-driven control system enabling real-time decision-making, process optimization, and improved production efficiency, showcasing the transformative potential of advanced data analytics for industrial applications.