Decoupling pyrite and arsenopyrite in flotation using thionocarbamate collector

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

doi:10.1016/j.powtec.2021.02.057

Cited by 26 publications

(6 citation statements)

References 23 publications

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“…Similar to the chalcopyrite, the ζ potential of pure pyrite also decreased with increasing pH and the IEP was located at about 3.5, which agreed well with the published papers. 29 , 30 After addition of SH, the ζ potential of pyrite shifted negatively, indicating the adsorption of SH on the pyrite surface. However, the shift was larger (more than 19.0 mV) for the pyrite than for the chalcopyrite, suggesting that the SH adsorption on the pyrite surface was much stronger and more pronounced.…”

Section: Resultsmentioning

confidence: 99%

Selective Separation of Chalcopyrite from Pyrite Using Sodium Humate: Flotation Behavior and Adsorption Mechanism

Sun,

Li,

et al. 2023

ACS Omega

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Flotation separation of chalcopyrite from pyrite using lime or cyanides as depressants results in serious problems, such as the blockage of pipelines and environmental pollution. Eco-friendly organics are a future trend for beneficiation plants. In this research, the eco-friendly organic depressant sodium humate (SH) was chosen as a depressant to separate chalcopyrite from pyrite by flotation. The results indicated that SH could selectively depress pyrite owing to the oxidation species (FeOOH, Fe2(SO4)3) on its surface. The oxidation species were the adsorption sites for the COO– in the SH structure and impeded the subsequent collector potassium ethyl xanthate (KEX) adsorption. However, chalcopyrite was slightly oxidized with fewer oxidation species for SH adsorption, and KEX could be adsorbed and functioned effectively. This research suggested that SH could be an effective and eco-friendly depressant in chalcopyrite–pyrite flotation separation, which had potential use in the industry.

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Section: Resultsmentioning

confidence: 99%

Selective Separation of Chalcopyrite from Pyrite Using Sodium Humate: Flotation Behavior and Adsorption Mechanism

Sun,

Li,

et al. 2023

ACS Omega

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“…Similar to the goal of every regression problem, each output variable y is considered to be related to an underlying arbitrary function t(x) that comes with an additive independent identically distributed Gaussian noise from the data, as expressed in Equation (16).…”

Section: Gaussian Process Regression Algorithmmentioning

confidence: 99%

“…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].…”

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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“…Going through the literature, it was observed that the various machine learning models that have been developed for predicting the metallurgical outputs of froth flotation do not include comprehensive data on pulp chemistry, and, even when they do, it is only on pH, which is just a component of pulp chemistry (Table 1). Pulp chemical conditions, especially those pertaining to electrochemistry, are known to significantly impact froth flotation, owing to their role in mineral collector interactions [36][37][38][39][40]. Studies have shown that pulp redox potential and pulp oxygen content are factors that strongly affect overall flotation performance [15,17,19,41,42].…”

Section: Introductionmentioning

confidence: 99%

Pulp Chemistry Variables for Gaussian Process Prediction of Rougher Copper Recovery

Amankwaa-Kyeremeh

Ehrig

Greet³

et al. 2023

Minerals

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Insight about the operation of froth flotation through modelling has been in existence since the early 1930s. Irrespective of the numerous industrial models that have been developed over the years, modelling of the metallurgical outputs of froth flotation often do not involve pulp chemistry variables. As such, this work investigated the influence of pulp chemistry variables (pH, Eh, dissolved oxygen and temperature) on the prediction performance of rougher copper recovery using a Gaussian process regression algorithm. Model performance assessed with linear correlation coefficient (r), root mean square error (RMSE), mean absolute percentage error (MAPE) and scatter index (SI) indicated that pulp chemistry variables are essential in predicting rougher copper recovery, and obtaining r values > 0.98, RMSE values < 0.32, MAPE values < 0.20 and SI values < 0.0034. RNCA feature weights reveal the pulp chemistry relevance in the order dissolved oxygen > pH > Eh > temperature.

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Decoupling pyrite and arsenopyrite in flotation using thionocarbamate collector

Cited by 26 publications

References 23 publications

Selective Separation of Chalcopyrite from Pyrite Using Sodium Humate: Flotation Behavior and Adsorption Mechanism

Selective Separation of Chalcopyrite from Pyrite Using Sodium Humate: Flotation Behavior and Adsorption Mechanism

Prediction and Optimisation of Copper Recovery in the Rougher Flotation Circuit

Pulp Chemistry Variables for Gaussian Process Prediction of Rougher Copper Recovery

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