The mineral industry relies on comminution processes, such as crushing and milling, to reduce ore size for further treatment. Crushers play a central role in this stage, yet their performance is strongly influenced by the lithology of the incoming ore, as different rock types exhibit distinct mechanical properties. Despite its importance, the literature on lithology characterization in crushing circuits is scarce, with most efforts focused on milling processes through the use of machine vision and few works addressing lithology characterization in crushing circuits. To bridge this gap, we propose a novel data-driven soft sensor for estimating the probability distribution of multiclass lithology in real time for crushing circuits. The method combines measurements of crusher motor current and rotational speed with signal processing and lightweight machine learning algorithms, ensuring deployment feasibility in resource-constrained environments, such as industrial Programmable Logic Controllers (PLCs). Model evaluation was conducted using Kullback–Leibler (KL) divergence and cosine similarity between true and predicted lithology distributions. The Extra Trees-based soft sensor achieved the best performance, with an average KL divergence of 0.065 and a cosine similarity of 0.98, demonstrating the effectiveness of this approach for lithology characterization in crushing circuits.
