**Sebastian Avalos 1,\*, Willy Kracht 2,3 and Julian M. Ortiz <sup>1</sup>**


Received: 1 August 2020; Accepted: 18 August 2020; Published: 20 August 2020

**Abstract:** Ore hardness plays a critical role in comminution circuits. Ore hardness is usually characterized at sample support in order to populate geometallurgical block models. However, the required attributes are not always available and suffer for lack of temporal resolution. We propose an operational relative-hardness definition and the use of real-time operational data to train a Long Short-Term Memory, a deep neural network architecture, to forecast the upcoming operational relative-hardness. We applied the proposed methodology on two SAG mill datasets, of one year period each. Results show accuracies above 80% on both SAG mills at a short upcoming period of times and around 1% of misclassifications between soft and hard characterization. The proposed application can be extended to any crushing and grinding equipment to forecast categorical attributes that are relevant to downstream processes.

**Keywords:** semi-autogenous grinding mill; operational hardness; energy consumption; mining; deep learning; long short-term memory
