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Open AccessArticle

An LSTM Approach for SAG Mill Operational Relative-Hardness Prediction

1
The Robert M. Buchan Department of Mining, Queen’s University, Kingston, ON K7L 3N6, Canada
2
Department of Mining Engineering, Universidad de Chile, Santiago 8370448, Chile
3
Advanced Mining Technology Center, AMTC, Universidad de Chile, Santiago 8370451, Chile
*
Author to whom correspondence should be addressed.
Minerals 2020, 10(9), 734; https://doi.org/10.3390/min10090734
Received: 1 August 2020 / Revised: 15 August 2020 / Accepted: 18 August 2020 / Published: 20 August 2020
(This article belongs to the Special Issue Comminution in the Minerals Industry)
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. View Full-Text
Keywords: semi-autogenous grinding mill; operational hardness; energy consumption; mining; deep learning; long short-term memory semi-autogenous grinding mill; operational hardness; energy consumption; mining; deep learning; long short-term memory
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MDPI and ACS Style

Avalos, S.; Kracht, W.; Ortiz, J.M. An LSTM Approach for SAG Mill Operational Relative-Hardness Prediction. Minerals 2020, 10, 734. https://doi.org/10.3390/min10090734

AMA Style

Avalos S, Kracht W, Ortiz JM. An LSTM Approach for SAG Mill Operational Relative-Hardness Prediction. Minerals. 2020; 10(9):734. https://doi.org/10.3390/min10090734

Chicago/Turabian Style

Avalos, Sebastian; Kracht, Willy; Ortiz, Julian M. 2020. "An LSTM Approach for SAG Mill Operational Relative-Hardness Prediction" Minerals 10, no. 9: 734. https://doi.org/10.3390/min10090734

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