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Prediction and optimisation of copper recovery in the rougher flotation circuit

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dc.contributor.authorAmankwaa Kyeremeh, B.
dc.contributor.authorMcCamley, C.
dc.contributor.authorZanin, M.
dc.contributor.authorGreet, C.
dc.contributor.authorEhrig, K.
dc.contributor.authorAsamoah, R.K.
dc.date.issued2024
dc.description.abstractIn this work, the prediction and optimisation of copper flotation has been conducted in the rougher flotation circuit. The copper-recovery prediction involved the application of support vector machine (SVM), Gaussian process regression (GPR), multi-layer perceptron artificial neural network (ANN), linear regression (LR), and random forest (RF) algorithms on 15 rougher flotation variables at the BHP Olympic Dam. The predictive models’ performance was assessed using linear correlation (𝑟), root mean square error (RMSE), mean absolute percentage error (MAPE), and variance accounted for (VAF). A simulated annealing (SA) optimisation algorithm, particle swarm optimisation (PSO) algorithm, surrogate optimisation (SO) algorithm, and genetic algorithm (GA) were investigated, using the GPR predictive function, to determine the optimal operating condition for maximising copper recovery. The predictive function of the best-performing model was extracted and used in optimising the flotation circuit. The results showed that the GPR model developed with the matern 3/2 kernel function makes the most precise copper-recovery prediction as compared to the other investigated predictive models, obtaining 𝑟 values > 0.96, RMSE values < 0.42, MAPE values < 0.25%, and VAF values > 94%. A hypothetical optimisation solution assessment showed that SA provides the best set of solutions for the maximisation of rougher copper recovery, obtaining a throughput of 638.02 t/h and a total net gain percentage of 14%–15.5% over the other optimisation algorithms with a maximum copper recovery of 94.76%. The operational benefits of implementing these algorithms have been highlighted.
dc.identifier.citationMinerals, 2024; 14(1):1-31
dc.identifier.doi10.3390/min14010036
dc.identifier.issn2075-163X
dc.identifier.issn2075-163X
dc.identifier.orcidAsamoah, R.K. [0000-0002-6871-6027]
dc.identifier.urihttps://hdl.handle.net/11541.2/37611
dc.language.isoen
dc.publisherMDPIAG
dc.relation.fundingARC CE200100009
dc.relation.fundingAISRF
dc.relation.fundingFII
dc.rightsCopyright 2023 The Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. (https://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.3390/min14010036
dc.subjectcopper recovery
dc.subjectfroth flotation
dc.subjectgaussian process regression (GPR)
dc.subjectmachine learning
dc.subjectprocess optimisation
dc.subjectrandom forest
dc.subjectsimulated annealing (SA) optimisation
dc.titlePrediction and optimisation of copper recovery in the rougher flotation circuit
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12281649720001831 13281649710001831 Open Access Published Version
ror.mmsid9916828031401831

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