Cobalt Dissolution from Concentrate in Sulfuric Acid—Ferrous Sulfate System: Process Parameters Optimization by Response Surface Methodology (RSM)

Mwanat, Mbal Henock-Michel; Kasongo, Kubangala Brest

doi:10.1007/s40831-021-00460-1

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Cobalt in most cases is extracted as a by-product of copper and nickel in nickel containing laterites and nickel-copper sulphide deposits [10]. The extraction of cobalt or copper from oxidized minerals is achieved using different leaching reagents such as sulfuric acid (H 2 SO 4 ), nitric acid (HNO 3 ), hydrochloric acid (HCl), ammoniacal system, and hydrofluoric acid (HF) or mixtures of these agents [6]. The use of sulfuric acid is prominent, as this is a result of the corrosiveness of the equipment and the selectivity criteria.…”

Section: A the Leaching Of Cobaltmentioning

confidence: 99%

“…These studies were able to show that it is possible to dissolve Co (III) using Fe 2+ ions from a Febearing mineral. The conventional leaching of cobalt from ores or concentrates involves the presence of acidic-ferrous sulphate of which various physiochemical parameters such as pulp density, stirring speed, temperature, pH, reaction time, and ferrous sulphate dosage govern the leaching process [13]- [15].…”

Section: A the Leaching Of Cobaltmentioning

confidence: 99%

See 1 more Smart Citation

Supervised Training and Unsupervised Machine Learning Testing for Cobalt Dissolution from Complex Cu-Co Ores in Acid Lixiviants

2023

Nov. 16-17, 2023 Johannesburg (South Africa)Back 39th JOHANNESBURG International Conference on “Chemical, Biological and Enviro

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The ever-increasing application of machine learning and artificial intelligence techniques in research has had a pivotal impact on the existing body of knowledge. In the presence of huge data set available, application of machine learning probes a better handling of the data. As such, this paper discusses the philosophies involved within machine learning. The paper the discusses the supervised training and unsupervised testing stages. The study focused on a supervised model (logistic linear regression) to perform a classification predictive analysis on cobalt dissolution. It further detailed the stages of data preparation and handling of the data upon developing the model. The model's performance was validated by using performance metrics such as accuracy, precision, and recall. The model performed good obtaining an accuracy value of 75%, the precision for the "Low Yield" class is 45,45%. The recall for the "Low Yield" class is 62,5%. The precision for the "High Yield" class is 88%. The recall for the "High Yield" class is approximately 78,57%. The model showed to perform well when predicting high yield instances as compared to low yield instances. As a result, the values obtained showed that the model requires some improvements but its implementation towards the dissolution of cobalt proved to be significant as it can describe high or low yield outcomes based on specific dissolution conditions.

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Section: A the Leaching Of Cobaltmentioning

confidence: 99%

Section: A the Leaching Of Cobaltmentioning

confidence: 99%

Supervised Training and Unsupervised Machine Learning Testing for Cobalt Dissolution from Complex Cu-Co Ores in Acid Lixiviants

2023

Nov. 16-17, 2023 Johannesburg (South Africa)Back 39th JOHANNESBURG International Conference on “Chemical, Biological and Enviro

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“…The implementation of laboratory results on a pilot scale can help to industrialize the optimal conditions obtained for the recovery of precious metals 31 . Response surface methodology is based on the design of experiments, includes mathematical and statistical tools, and optimizes the effective parameters of process 32 , 33 . Based on this, the recovery model of Cadmium and Zinc from the acid leaching process is determined by RSM 34 .…”

Section: Introductionmentioning

confidence: 99%

Optimization of atmospheric leaching parameters for cadmium and zinc recovery from low-grade waste by response surface methodology (RSM)

Kolbadinejad,

Ghaemi

2024

Sci Rep

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This study is focused on the optimization of effective parameters on Cadmium and Zinc recovery by atmospheric acid leaching of low-grade waste by response surface methodology (RSM) and using the Central Composite Design (CCD) method. The effects of parameters including time (0.5–2.5 h), temperature (40–80 °C), solid/liquid (S/L) (0.05–0.09 g/cc), particle size (174–44 mic), oxygen injection (0–1%) and pH (0.5–4.5) were statistically investigated at 5 surfaces. The sample of low-grade waste used in this study was mainly zinc factory waste. Two quadratic models for the correlation of independent parameters for the maximum recovery were proposed. The properties of waste were evaluated by X-ray diffraction (XRD) and X-ray fluorescence (XRF). Atomic absorption spectroscopy was used to determine the amount of Cadmium and Zinc in the leaching solution. The correlation coefficient (R2) for the predicted and experimental data of Cadmium and Zinc are 0.9837 and 0.9368, respectively. Time, S/L and size were the most effective parameters for the recovery efficiency of cadmium and zinc. 75.05% of Cadmium and 86.13% of Zinc were recovered in optimal conditions of leaching: S/L 0.08, pH 2.5, size 88 µm, 70 °C and 2.5 h. with air injection.

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Simulation of Simultaneous Leaching of Copper and Cobalt Minerals in Acid-Reductive Media: Sensitivity Analysis and Optimization

Mwanat

Kasongo

Muliangala

et al. 2022

J. Sustain. Metall.

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Cobalt Dissolution from Concentrate in Sulfuric Acid—Ferrous Sulfate System: Process Parameters Optimization by Response Surface Methodology (RSM)

Cited by 11 publications

References 25 publications

Supervised Training and Unsupervised Machine Learning Testing for Cobalt Dissolution from Complex Cu-Co Ores in Acid Lixiviants

Supervised Training and Unsupervised Machine Learning Testing for Cobalt Dissolution from Complex Cu-Co Ores in Acid Lixiviants

Optimization of atmospheric leaching parameters for cadmium and zinc recovery from low-grade waste by response surface methodology (RSM)

Simulation of Simultaneous Leaching of Copper and Cobalt Minerals in Acid-Reductive Media: Sensitivity Analysis and Optimization

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