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Recent Developments in Sensors, Machine Learning, Data Analytics, and Process...
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Recent Developments in Sensors, Machine Learning, Data Analytics, and Process Optimisation in Minerals Processing and Extractive Metallurgy

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Processing and Extractive Metallurgy".

Deadline for manuscript submissions: closed (17 October 2023) | Viewed by 3602

Special Issue Editors

Dr. Richmond K. Asamoah

E-Mail Website
Guest Editor
Minerals and Resource Engineering, Future Industries Institute, University of South Australia, Mawson Lakes Campus, Adelaide, SA 5095, Australia
Interests: mineral processing and extractive metallurgy; data analytics and machine learning; biohydrometallurgy; surface and interfacial science; environmetal science (ESG)
Special Issues, Collections and Topics in MDPI journals
Dr. Jixue Liu

E-Mail Website
Guest Editor
UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
Interests: text and sequence data analytics; data integration and entity resolution; dependency theory and discovery; privacy; XML; database and data mining

Special Issue Information

Dear Colleagues,

Integration of advanced sensors and machine learning techniques is crucial in mineral processing and extractive metallurgy, especially considering the increasing demand for highly valued, precious, and critical metals and the production of big-scale processing equipment. Advanced sensors (e.g., acoustics or vibration sensors and electro/chemical sensors) are required to maximise process efficiencies and avoid instabilities due to changing ore characteristics and concomitant process variables. Integrated machine learning, data analytics, and process optimisation strategies are also required for rapid decision making together with apt process control both at the micro and macro scales. This Special Issue is organised into three sections and invites contributions accordingly:

  • Section 1: Advanced sensors, machine learning, and data analytics in the processing of various metal commodities. A critical focus will be on highly valued precious and critical metals.
  • Section 2: Advanced sensors, machine learning, and data analytics in the (bio)hydrometallurgical extraction of metals.
  • Section 3: Process optimisation and integration across the mineral processing and extractive metallurgy value chain.

Dr. Richmond K. Asamoah
Dr. Jixue Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advances in mineral processing sensors
  • machine learning and data analytics
  • process optimisation and integration
  • new technologies for rapid decision making
  • future developments

Published Papers (3 papers)

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Research

18 pages, 5385 KiB  
Article
Monitoring of Mineral Processing Operations with Isolation Forests
by Chris Aldrich and Xiu Liu
Minerals 2024, 14(1), 76; https://doi.org/10.3390/min14010076 - 8 Jan 2024
Viewed by 1046
Abstract
Reliable monitoring of mineral process systems is key to more efficient plant operation. Multivariate statistical process control based on principal component analysis is well-established in industry but may not be effective when dealing with dynamic nonlinear or transient processes, where process behavior may [...] Read more.
Reliable monitoring of mineral process systems is key to more efficient plant operation. Multivariate statistical process control based on principal component analysis is well-established in industry but may not be effective when dealing with dynamic nonlinear or transient processes, where process behavior may change rapidly from time to time. Although a large variety of nonlinear models have been proposed to address these problems, the monitoring of complex dynamic process systems remains challenging. Isolation forests are unsupervised machine learning models that provide an interesting approach to process monitoring that has not been explored extensively yet. In this investigation, this approach is compared with traditional multivariate statistical process monitoring based on principal component models. Three real-world case studies are considered. The first case study is based on coal flotation, the second is based on features extracted from a platinum group metal flotation froth; and the third is based on data from an industrial semi-autogenous grinding circuit. In each case, the models were trained on data representing normal operating conditions and then tested on new process data that were generally different from the training data to test their ability to identify these data as out-of-control. The isolation forest models performed better than the principal component models when the data were nonlinear, but not when the data associated with normal operation and faulty conditions were linearly separable, as was the case with the flotation data. Full article
(This article belongs to the Special Issue Recent Developments in Sensors, Machine Learning, Data Analytics, and Process Optimisation in Minerals Processing and Extractive Metallurgy)
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11 pages, 1816 KiB  
Article
Acoustic Sensing of Fresh Feed Disturbances in a Locked-Cycle Laboratory AG/SAG Mill
by Kwaku Boateng Owusu, William Skinner, Christopher Greet and Richmond K. Asamoah
Minerals 2023, 13(7), 868; https://doi.org/10.3390/min13070868 - 27 Jun 2023
Viewed by 943
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Developments in Sensors, Machine Learning, Data Analytics, and Process Optimisation in Minerals Processing and Extractive Metallurgy)
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16 pages, 3473 KiB  
Article
Pulp Chemistry Variables for Gaussian Process Prediction of Rougher Copper Recovery
by Bismark Amankwaa-Kyeremeh, Kathy Ehrig, Christopher Greet and Richmond Asamoah
Minerals 2023, 13(6), 731; https://doi.org/10.3390/min13060731 - 27 May 2023
Cited by 1 | Viewed by 1095
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Developments in Sensors, Machine Learning, Data Analytics, and Process Optimisation in Minerals Processing and Extractive Metallurgy)
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