Undoubtedly hydrocyclones play a critical role in powder technology, which can considerably affect the plants' process efficiency. However, hydrocyclones were rarely modeled on an industrial scale, where a model can be used to train operators and minimize potential scale-up errors and lab costs. The novel approach for filling such a gap would be using conscious lab “CL” as a new concept that builds based on an industrial dataset and explainable artificial intelligence (XAI). As a novel approach, this study developed a CL and explored the interactions between hydrocyclone variables by the most recent XAI method called “SHapley Additive exPlanations (SHAP)”, and a novel machine-learning model, “CatBoost”. The hydrocyclone output and the particle size of the plant magnetic separator were modeled by SHAP-CatBoost. SHAP could successfully model all the relationships, and CatBoost could predict the O₈₀ and K₈₀ where outcomes had a higher accuracy (R² ∼ 0.90) than other conventional AIs.