Random forest model predictive control for paste thickening

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dc.contributor.authorDiaz, Pablo
dc.contributor.authorSalas, Juan C.
dc.contributor.authorCipriano, Aldo
dc.contributor.authorNunez, Felipe
dc.date.accessioned2025-01-20T22:02:35Z
dc.date.available2025-01-20T22:02:35Z
dc.date.issued2021
dc.description.abstractAs processes involved in mineral processing operations increase their complexity, automation and control become critical to ensure an economically viable and environmentally sustainable operation. In the context of modern mineral processing, paste thickening stands out as a relatively new method for producing high density slurries that has proven challenging for standard control algorithms. In this setting, the use of machine-learning-based models within a predictive control strategy arises as an appealing alternative. This work presents a Random Forest Model Predictive Control scheme for paste thickening based on a purely data-driven approach for modeling and evolutionary strategies for solving the associated optimization problem. Results show that the proposed strategy outperforms conventional predictive control both qualitatively and quantitatively.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.mineng.2020.106760
dc.identifier.issn0892-6875
dc.identifier.urihttps://doi.org/10.1016/j.mineng.2020.106760
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/93989
dc.identifier.wosidWOS:000756243000009
dc.language.isoen
dc.revistaMinerals engineering
dc.rightsacceso restringido
dc.subjectPaste thickening
dc.subjectModel predictive control
dc.subjectRandom forest
dc.subjectMachine learning
dc.subject.ods06 Clean Water and Sanitation
dc.subject.odspa06 Agua limpia y saneamiento
dc.titleRandom forest model predictive control for paste thickening
dc.typeartículo
dc.volumen163
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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