High-Pressure Grinding Rolls (HPGR), as a modified type of roll crushers, could intensively reduce the energy consumptions in the mineral processing comminution units. However, several problems counted for their operational modeling, especially in the industrial scales. Expanding a conscious laboratory (CL) as a recently developed concept based on the recorded datasets from the HPGR operational variables could be tackled those complications and fill the gap. Moreover, constructing such a CL base on explainable artificial intelligence (EAI) systems would be an innovative point for the digitalizing powder technology industries. Using a robust EAI model as a strategic approach could significantly improve system transparency and trustworthiness to convert any complicated black-box machine learning to a logical human basis system. This study introduced the SHapley Additive exPlanations (SHAP) and extreme gradient boosting (XGBoost) as the latest powerful EAI tool for the CL modeling of the particle sizes produced by an industrial HPGR (P₈₀) in the Fakoor Sanat iron ore processing plant (Kerman, Iran). SHAP precisely assessed multivariable relationships between the monitored operational variables and correlated them with the HPGR P₈₀. SHAP values showed relationship magnitudes among variables and ranked them based on their effectiveness on the P₈₀ prediction. The working gap demonstrated the highest importance for the P₈₀ prediction. XGBoost could precisely predict the P80 and showed higher accuracy than typical machine learning methods (random forest and support vector regression) for constructing the CL of HPGR. These significant outcomes would open a new window for robust consideration of the EAI models within powder technology.

高压磨辊（HPGR）作为一种改进型的辊式破碎机，可以大大降低选矿粉碎单元的能耗。然而，有几个问题对他们的操作建模很重要，尤其是在工业规模上。扩展意识实验室 ( CL ) 作为最近开发的基于 HPGR 操作变量记录数据集的概念可以解决这些并发症并填补空白。此外，构建这样一个CL基于可解释的人工智能 (EAI) 系统将成为数字化粉末技术行业的创新点。使用强大的 EAI 模型作为战略方法可以显着提高系统透明度和可信度，将任何复杂的黑盒机器学习转换为逻辑人类基础系统。本研究介绍了夏普利添加剂解释（SHAP）和极端梯度升压（XGBoost）作为最新的强大EAI工具为CL粒径由工业HPGR（P产生的建模₈₀在Fakoor萨纳特铁矿石加工厂）（克尔曼, 伊朗）。SHAP 精确评估了受监控操​​作变量之间的多变量关系，并将它们与 HPGR P ₈₀相关联. SHAP 值显示变量之间的关系大小，并根据它们对 P ₈₀预测的有效性对它们进行排序。工作间隙证明了 P ₈₀预测的最高重要性。XGBoost 可以精确地预测 P80，并显示出比构建 HPGR CL的典型机器学习方法（随机森林和支持向量回归）更高的准确性。这些重要的成果将为在粉末技术中对 EAI 模型的稳健考虑打开一扇新窗口。