Investigating the selective flotation of auriferous arsenian pyrite from refractory ores using thionocarbamate

Forson, Philip; Skinner, William; Asamoah, Richmond K.

doi:10.1016/j.powtec.2023.118649

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Froth flotation has seen remarkable widespread applications in the mineral industry for different highly valuable commodities (e.g., copper, gold, zinc, and rare earth elements) [14][15][16][17][18][19][20][21]. The interdependence of the process variables extends performance challenges to the flotation process where a change in feed mineralogy requires a corresponding change in the other flotation variables for an optimal outcome [22][23][24][25][26][27][28][29][30][31]. Failure to properly adjust the flotation conditions may result in a concentrate dilution and recovery of waste minerals which could contribute negatively to downstream unit efficacy and financial loss [28,32,33].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimisation of Copper Recovery in the Rougher Flotation Circuit

Amankwaa-Kyeremeh,

McCamley,

Zanin

et al. 2023

Minerals

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In 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.

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