ORCID Profile
0000-0002-6871-6027
Current Organisation
University of South Australia
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Publisher: Elsevier BV
Date: 03-2021
Publisher: MDPI AG
Date: 19-01-2021
DOI: 10.3390/MIN11010093
Abstract: This paper presents a research overview, reconciling key and useful case study findings, towards uncovering major causes of gold refractoriness and maximising extraction performance of specific gold flotation and bio-oxidation products. Through systematic investigation of the ore mineralogical and gold deportment properties, leaching mechanisms, and kinetic behaviour and pulp rheology, it was observed that the predominant cause of the poor extraction efficacy of one bio-oxidised product is the presence of recalcitrant sulphate minerals (e.g., jarosite and gypsum) produced during the oxidation process. This was followed by carbonaceous matter and other gangue minerals such as muscovite, quartz, and rutile. The underpining leaching mechanism and kinetics coupled with the pulp rheology were influenced by the feed mineralogy/chemistry, time, agitation/shear rate, interfacial chemistry, pH modifier type, and mechano-chemical activation. For instance, surface exposure of otherwise unavailable gold particles by mechano-chemical activation enhanced the gold leaching rate and yield. This work reflect the remarkable impact of subtle deposit feature changes on extraction performance.
Publisher: Elsevier BV
Date: 08-2023
Publisher: MDPI AG
Date: 27-05-2023
DOI: 10.3390/MIN13060731
Abstract: 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.
Publisher: Informa UK Limited
Date: 24-06-2021
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 05-2021
Publisher: OMICS Publishing Group
Date: 2018
Publisher: Elsevier BV
Date: 06-2019
Publisher: Elsevier BV
Date: 08-2018
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 04-2022
Publisher: MDPI AG
Date: 27-06-2023
DOI: 10.3390/MIN13070868
Abstract: In a large-scale operation, feed ores are introduced into the AG/SAG mill in a continuous mode at a given flow rate to replace the discharging slurry. Nonetheless, the variations in the feed characteristics, typically hardness and size distribution, could cause sudden disruption to the mill operation. This would be challenging to detect in practice, owing to the hostile environment of the mill. In this work, an acoustic sensing-based monitoring technique was utilized in a laboratory-scale AG/SAG mill locked-cycle study to keep track of fluctuations caused by feed ore heterogeneity. Analysis of the recorded mill acoustic response using statistical root mean square (RMS) and mill discharge sizes showed that the introduction of fresh feed with varying hardness and size distribution considerably altered the mill product undersize of −150 μm and acoustic emission. Overall, the acoustic sensing technique demonstrated that the AG/SAG mill stability as well as disturbances caused by different feed size fractions and hardness can be monitored using the mill acoustic response, an indication of real-time monitoring and optimisation.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 09-2021
Publisher: Science Publishing Group
Date: 2014
Publisher: Informa UK Limited
Date: 09-10-2020
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 05-2022
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier BV
Date: 08-2020
Publisher: MDPI AG
Date: 11-04-2023
Abstract: The harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, the need for continuous monitoring of mill parameters for optimal operation. In this paper, an acoustic-based sensing method is employed to estimate, in real time, a snapshot of the different feed size fractions presented to a laboratory-scale SAG mill. Employing the MATLAB 2020b programme, the mill acoustic signal is processed using various transform techniques such as power spectral density estimate (PSDE) by Welch’s method, discrete wavelet transform (DWT), wavelet packet transform (WPT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). Different fractional bandpowers are obtained from the PSDE spectrum, while the statistical root mean square values are further extracted from DWT, WPT, EMD, and VMD as feature vectors. The features are used as input features in different machine-learning classification algorithms for different mill feed size fractions predictions. The various transform techniques and feed size fraction predictions are evaluated using the various performance indicators obtained from the confusion matrix such as accuracy, precision, sensitivity and F1 score. The study showed that the acoustic signal feature extraction techniques used in conjunction with the Support Vector Machine (SVM), linear discriminant analysis (LDA), and ensemble with subclass discriminant machine learning algorithms demonstrated improved performance for predicting feed size variations.
Publisher: Informa UK Limited
Date: 06-06-2021
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 09-2022
No related grants have been discovered for Richmond Asamoah.