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Minerals
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Novel Advanced Machine Learning Methods in Mineral Processing
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Novel Advanced Machine Learning Methods in Mineral Processing

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 (20 September 2020) | Viewed by 24005

Special Issue Editors

Prof. Dr. Saeed Chehreh Chelgani

E-Mail Website
Guest Editor
Minerals and Metallurgical Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, SE-971 87 Luleå, Sweden
Interests: mineral processing; flotation; surface chemistry; rare earth processing; coal preparation; graphite processing; leaching; modeling; neural network; random forest
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jan Rosenkranz

E-Mail Website
Guest Editor
Minerals and Metallurgical Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, SE-971 87 Luleå, Sweden
Interests: mineral processing; grinding; flotation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In mineral processing, there are several linear and nonlinear relationships between material properties and process and design parameters. These correlations can be assessed based on various experimental and numerical methods. Over the last three decades, different intelligent computing and statistical methods, such as genetic algorithms, artificial neural networks, various types of multivariable regression, and tree-based systems, have been introduced in order to describe the complex and sometimes nonlinear relationships. This has resulted in the generation of various intelligent models for the prediction of process responses, i.e., recovery, grade, and comminution or separation efficiency. This special issue will explore the application of “novel advanced machine learning methods in mineral processing”.

Prof. Dr. Saeed Chehreh Chelgani
Prof. Dr. Jan Rosenkranz
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

  • ANN
  • Deep learning
  • Random forest
  • Regression
  • Support vector machine
  • Boosted
  • ANFIS
  • Mineral processing
  • Grinding
  • Magnetic separation
  • Gravity separation
  • Leaching
  • Flotation

Published Papers (5 papers)

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Research

28 pages, 15905 KiB  
Article
Dynamic Monitoring of Grinding Circuits by Use of Global Recurrence Plots and Convolutional Neural Networks
by Jacques Olivier and Chris Aldrich
Minerals 2020, 10(11), 958; https://doi.org/10.3390/min10110958 - 27 Oct 2020
Cited by 8 | Viewed by 3460
Abstract
Reliable control of grinding circuits is critical to more efficient operation of concentrator plants. In many cases, operators still play a key role in the supervisory control of grinding circuits but are not always able to act timely to deal with disturbances, such [...] Read more.
Reliable control of grinding circuits is critical to more efficient operation of concentrator plants. In many cases, operators still play a key role in the supervisory control of grinding circuits but are not always able to act timely to deal with disturbances, such as changes in the mill feed. Reliable process monitoring can play a major role in assisting operators to take more timely and reliable action. These monitoring systems need to be able to deal with what could be complex nonlinear dynamic behavior of comminution circuits. To this end, a dynamic process monitoring approach is proposed based on the use of convolutional neural networks. To take advantage of the availability of pretrained neural networks, the grinding circuit variables are treated as time series which can be converted into images. Features extracted from these networks are subsequently analyzed in a multivariate process monitoring framework with an underlying principal component model. Two variants of the approach based on convolutional neural networks are compared with dynamic principal component analysis on a simulated and real-world case studies. In the first variant, the pretrained neural network is used as a feature extractor without any further training. In the second variant, features are extracted following further training of the network in a synthetic binary classification problem designed to enhance the extracted features. The second approach yielded nominally better results than what could be obtained with dynamic principal component analysis and the approach using features extracted by transfer learning. Full article
(This article belongs to the Special Issue Novel Advanced Machine Learning Methods in Mineral Processing)
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22 pages, 9546 KiB  
Article
Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing
by Natsuo Okada, Yohei Maekawa, Narihiro Owada, Kazutoshi Haga, Atsushi Shibayama and Youhei Kawamura
Minerals 2020, 10(9), 809; https://doi.org/10.3390/min10090809 - 13 Sep 2020
Cited by 29 | Viewed by 6248
Abstract
In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize [...] Read more.
In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%. Full article
(This article belongs to the Special Issue Novel Advanced Machine Learning Methods in Mineral Processing)
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15 pages, 4372 KiB  
Article
An Interpretable Extreme Gradient Boosting Model to Predict Ash Fusion Temperatures
by Maciej Rzychoń, Alina Żogała and Leokadia Róg
Minerals 2020, 10(6), 487; https://doi.org/10.3390/min10060487 - 27 May 2020
Cited by 9 | Viewed by 2387
Abstract
The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or [...] Read more.
The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or to be able to predict value of this parameter. In this study a non-linear model predicting the HT value, based on ash oxides content for 360 coal samples from the Upper Silesian Coal Basin, was developed. The proposed model was established using the machine learning method—extreme gradient boosting (XGBoost) regressor. An important feature of models based on the XGBoost algorithm is the ability to determine the impact of individual input parameters on the predicted value using the feature importance (FI) technique. This method allowed the determination of ash oxides having the greatest impact on the projected HT. Then, the partial dependence plots (PDP) technique was used to visualize the effect of individual oxides on the predicted value. The results indicate that proposed model could estimate value of HT with high accuracy. The coefficient of determination (R2) of the prediction has reached satisfactory value of 0.88. Full article
(This article belongs to the Special Issue Novel Advanced Machine Learning Methods in Mineral Processing)
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17 pages, 4370 KiB  
Article
Process Variable Importance Analysis by Use of Random Forests in a Shapley Regression Framework
by Chris Aldrich
Minerals 2020, 10(5), 420; https://doi.org/10.3390/min10050420 - 08 May 2020
Cited by 31 | Viewed by 6480
Abstract
Linear regression is often used as a diagnostic tool to understand the relative contributions of operational variables to some key performance indicator or response variable. However, owing to the nature of plant operations, predictor variables tend to be correlated, often highly so, and [...] Read more.
Linear regression is often used as a diagnostic tool to understand the relative contributions of operational variables to some key performance indicator or response variable. However, owing to the nature of plant operations, predictor variables tend to be correlated, often highly so, and this can lead to significant complications in assessing the importance of these variables. Shapley regression is seen as the only axiomatic approach to deal with this problem but has almost exclusively been used with linear models to date. In this paper, the approach is extended to random forests, and the results are compared with some of the empirical variable importance measures widely used with these models, i.e., permutation and Gini variable importance measures. Four case studies are considered, of which two are based on simulated data and two on real world data from the mineral process industries. These case studies suggest that the random forest Shapley variable importance measure may be a more reliable indicator of the influence of predictor variables than the other measures that were considered. Moreover, the results obtained with the Gini variable importance measure was as reliable or better than that obtained with the permutation measure of the random forest. Full article
(This article belongs to the Special Issue Novel Advanced Machine Learning Methods in Mineral Processing)
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14 pages, 1243 KiB  
Article
Performance Improvements during Mineral Processing Using Material Fingerprints Derived from Machine Learning—A Conceptual Framework
by Jeroen R. van Duijvenbode, Mike W.N. Buxton and Masoud Soleymani Shishvan
Minerals 2020, 10(4), 366; https://doi.org/10.3390/min10040366 - 18 Apr 2020
Cited by 5 | Viewed by 4055
Abstract
Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected data describing material [...] Read more.
Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected data describing material attributes could help to forecast the behaviour of mineral processing. This paper proposes a conceptual framework that creates a data-driven link between ore and the processing behaviour through the creation of material “fingerprints”. A fingerprint is a machine learning-based classification of measured material attributes compared to the range of attributes found within the mine’s mineral reserves. The outcome of the classification acts as a label for a machine learning model and contains relevant information, which may identify the root cause of measured differences in processing behaviour. Therefore, this class label can forecast the associated behaviour of mineral processing. Furthermore, insight is given into the confidence of available data originating from different analytical techniques. Taken together, this enhances the understanding of how differences in geology impact metallurgical plant performance. Targeted measurements at low-confidence unit processes and for specific attributes would upgrade the confidence in fingerprints and capabilities to predict plant performance. Full article
(This article belongs to the Special Issue Novel Advanced Machine Learning Methods in Mineral Processing)
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