Characterisation of Metal Debris in Grinding and Flotation Circuits

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pulp Chemistry Variables for Gaussian Process Prediction of Rougher Copper Recovery

Amankwaa-Kyeremeh

Ehrig

Greet³

et al. 2023

Self Cite

“…In industrial practice, Greet et al [18] reported that metal debris, generated during grinding, has a potential influence on downstream flotation. The increase in metallic debris can significantly alter the chemistry of the pulp [19]. Apparently, the chemical substances released by the galvanic interaction between the grinding media and the mineral play an important role in the hydrophobicity of the mineral.…”

Section: Introductionmentioning

confidence: 99%

Changing the Pulp Properties and Surface Hydrophilicity of Galena and Pyrite by Selecting the Appropriate Grinding Media Towards Their Selective Separation

Zhai,

Liu,

et al. 2023

The grinding process plays a crucial role in the floatability of mineral. The present study proposes a scheme to assist in the flotation separation of galena and pyrite by optimizing the choice of grinding media. The effects of grinding media (cast iron balls, steel balls containing 18% chromium, and steel balls containing 12% chromium) on the pulp properties and hydrophilicity of galena and pyrite were evaluated using grinding tests, micro-flotation tests, X-ray photoelectron spectroscopy, and adsorption capacity analyses. The results showed significant differences in the pH, potential, and dissolved oxygen content of the galena or pyrite pulp with different grinding media, resulting in significant differences in the flotation behavior of galena and pyrite. The use of cast iron balls in the grinding process was more favorable for the flotation separation of galena and pyrite. The pyrite surfaces adsorbed less butyl xanthate when cast iron balls were used as grinding media due to the presence of more hydrophilic hydroxyl iron. Therefore, cast iron balls as grinding media are favorable for broadening the surface hydrophilic differences between galena and pyrite.

“…A study conducted by Calvo et al [3] revealed that the average copper grade is constantly reducing overtime (25% reduction in just 10 years) with increasing energy consumption (46% energy increase) and total material production (30% production increase). The continuous increase in production (2%-3% per year forecasted production increase between 2010 and 2050) due to rising demand [4] requires more efficient methods across the mineral-processing value chain (e.g., comminution, flotation, and hydrometallurgy) while eliminating process waste [5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimisation of Copper Recovery in the Rougher Flotation Circuit

Amankwaa-Kyeremeh,

McCamley,

Zanin

et al. 2023

Self Cite

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.