An LSTM Approach for SAG Mill Operational Relative-Hardness Prediction
Author
dc.contributor.author
Ávalos, Sebastián
Author
dc.contributor.author
Kracht Gajardo, Willy
Author
dc.contributor.author
Ortiz, Julian
Admission date
dc.date.accessioned
2021-04-05T20:05:33Z
Available date
dc.date.available
2021-04-05T20:05:33Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Minerals 2020, 10, 734
es_ES
Identifier
dc.identifier.other
10.3390/min10090734
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/178928
Abstract
dc.description.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.
es_ES
Patrocinador
dc.description.sponsorship
Natural Sciences and Engineering Research Council of Canada (NSERC)
RGPIN-2017-04200
RGPAS-2017-507956
Chilean National Commission for Scientific and Technological Research (CONICYT), through CONICYT/PIA Project
AFB180004
Chilean National Commission for Scientific and Technological Research (CONICYT), through CONICYT/FONDAP Project
15110019