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Minerals
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The Application of Machine Learning in Mineral Processing
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The Application of Machine Learning 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 (23 July 2021) | Viewed by 20914

Special Issue Editors

Dr. Lidia Auret

E-Mail Website
Guest Editor
1. Stone Three, Somerset West, South Africa
2. Department of Process Engineering, Stellenbosch University, Stellenbosch, South Africa
Interests: mineral processing; machine learning; process monitoring; fault diagnosis; machine vision; soft sensors and data-based modelling; industrial applications
Dr. Kevin Brooks

E-Mail Website
Guest Editor
1. School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa
2. Hatch, Johannesburg, South Africa
Interests: model-predictive control; system identification; use of historical data; soft sensors; steady-state and dynamic modelling; mineral processing

Special Issue Information

Dear Colleagues,

Advances in machine learning algorithms, as well as accessibility to ever-increasing computational power and data storage (including through cloud-based computation/storage-as-a-service), have resulted in marked performance improvements in language processing, image recognition, autonomous robots and complex game playing.

Many opportunities and challenges exist for the application of machine learning in mineral processing. Recent research publications include data-based modelling, machine vision and fault diagnosis applications, but predominantly on simulated, laboratory scale, or (to a much lesser extent) historical industrial data. We would like to specifically invite contributions of machine learning applications in industrial contexts (opportunity identification from historical data, design of digital systems that include machine learning models/results, as well as reporting on embedded industrialized machine learning solutions).

In order for machine learning to provide sustainable value to the mineral processing industry, machine learning best practices from other fields need to gain traction in mineral processing machine learning research. Such best practices include partitioning of historical data into training/validation/testing sets; discussions on hyperparameter sensitivity and selection; comparisons to simple models to ensure added value of complex models; domain knowledge guided interpretation of machine learning results; domain knowledge inspired feature engineering and hybrid modelling; consideration of deployment practicalities, and cost–benefit analysis. The CRISP-DM (cross-industry standard process for data mining) is one framework that can be considered by researchers to ensure structured and comprehensive studies.

We look forward to your contributions to this Special Issue.

Dr. Lidia Auret
Dr. Kevin Brooks
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

  • Mineral processing
  • Machine learning
  • Process monitoring
  • Fault diagnosis
  • Machine vision
  • Soft sensors and data-based modelling
  • Industrial applications

Published Papers (7 papers)

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Research

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18 pages, 4412 KiB  
Article
One-Dimensional Convolutional Auto-Encoder for Predicting Furnace Blowback Events from Multivariate Time Series Process Data—A Case Study
by Carl Daniel Theunissen, Steven Martin Bradshaw, Lidia Auret and Tobias Muller Louw
Minerals 2021, 11(10), 1106; https://doi.org/10.3390/min11101106 - 09 Oct 2021
Cited by 1 | Viewed by 1616
Abstract
Modern industrial mining and mineral processing applications are characterized by large volumes of historical process data. Hazardous events occurring in these processes compromise process safety and therefore overall viability. These events are recorded in historical data and are often preceded by characteristic patterns. [...] Read more.
Modern industrial mining and mineral processing applications are characterized by large volumes of historical process data. Hazardous events occurring in these processes compromise process safety and therefore overall viability. These events are recorded in historical data and are often preceded by characteristic patterns. Reconstruction-based data-driven models are trained to reconstruct the characteristic patterns of hazardous event-preceding process data with minimal residuals, facilitating effective event prediction based on reconstruction residuals. This investigation evaluated one-dimensional convolutional auto-encoders as reconstruction-based data-driven models for predicting positive pressure events in industrial furnaces. A simple furnace model was used to generate dynamic multivariate process data with simulated positive pressure events to use as a case study. A one-dimensional convolutional auto-encoder was trained as a reconstruction-based model to recognize the data preceding the hazardous events, and its performance was evaluated by comparing it to a fully-connected auto-encoder as well as a principal component analysis reconstruction model. This investigation found that one-dimensional convolutional auto-encoders recognized event-preceding patterns with lower detection delays, higher specificities, and lower missed alarm rates, suggesting that the one-dimensional convolutional auto-encoder layout is superior to the fully connected auto-encoder layout for use as a reconstruction-based event prediction model. This investigation also found that the nonlinear auto-encoder models outperformed the linear principal component model investigated. While the one-dimensional auto-encoder was evaluated comparatively on a simulated furnace case study, the methodology used in this evaluation can be applied to industrial furnaces and other mineral processing applications. Further investigation using industrial data will allow for a view of the convolutional auto-encoder’s absolute performance as a reconstruction-based hazardous event prediction model. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
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23 pages, 3801 KiB  
Article
Learning to Tune a Class of Controllers with Deep Reinforcement Learning
by William John Shipman
Minerals 2021, 11(9), 989; https://doi.org/10.3390/min11090989 - 09 Sep 2021
Viewed by 1861
Abstract
Control systems require maintenance in the form of tuning their parameters in order to maximize their performance in the face of process changes in minerals processing circuits. This work focuses on using deep reinforcement learning to train an agent to perform this maintenance [...] Read more.
Control systems require maintenance in the form of tuning their parameters in order to maximize their performance in the face of process changes in minerals processing circuits. This work focuses on using deep reinforcement learning to train an agent to perform this maintenance continuously. A generic simulation of a first-order process with a time delay, controlled by a proportional-integral controller, was used as the training environment. Domain randomization in this environment was used to aid in generalizing the agent to unseen conditions on a physical circuit. Proximal policy optimization was used to train the agent, and hyper-parameter optimization was performed to select the optimal agent neural network size and training algorithm parameters. Two agents were tested, examining the impact of the observation space used by the agent and concluding that the best observation consists of the parameters of an auto-regressive with exogenous input model fitted to the measurements of the controlled variable. The best trained agent was deployed at an industrial comminution circuit where it was tested on two flow rate control loops. This agent improved the performance of one of these control loops but decreased the performance of the other control loop. While deep reinforcement learning does show promise in controller tuning, several challenges and directions for further study have been identified. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
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19 pages, 1050 KiB  
Article
Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods
by Kevin Brooks, Derik le Roux, Yuri A. W. Shardt and Chris Steyn
Minerals 2021, 11(9), 954; https://doi.org/10.3390/min11090954 - 31 Aug 2021
Cited by 3 | Viewed by 1792
Abstract
With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of [...] Read more.
With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
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18 pages, 2489 KiB  
Article
A Hierarchical Approach to Improve the Interpretability of Causality Maps for Plant-Wide Fault Identification
by Natali van Zijl, Steven Martin Bradshaw, Lidia Auret and Tobias Muller Louw
Minerals 2021, 11(8), 823; https://doi.org/10.3390/min11080823 - 29 Jul 2021
Cited by 2 | Viewed by 1882
Abstract
Modern mineral processing plants utilise fault detection and diagnosis to minimise time spent under faulty conditions. However, a fault originating in one plant section (PS) can propagate throughout the entire plant, obscuring its root cause. Causality analysis identifies the cause–effect relationships between process [...] Read more.
Modern mineral processing plants utilise fault detection and diagnosis to minimise time spent under faulty conditions. However, a fault originating in one plant section (PS) can propagate throughout the entire plant, obscuring its root cause. Causality analysis identifies the cause–effect relationships between process variables and presents them in a causality map to inform root cause identification. This paper presents a novel hierarchical approach for plant-wide causality analysis that decreases the number of nodes in a causality map, improving interpretability and enabling causality analysis as a tool for plant-wide fault diagnosis. Two causality maps are constructed in subsequent stages: first, a dimensionally reduced, plant-wide causality map used to localise the fault to a PS; second, a causality map of the identified PS used to identify the root cause. The hierarchical approach accurately identified the true root cause in a well-understood case study; its plant-wide map consisted of only three nodes compared to 15 nodes in the standard causality map and its transitive reduction. The plant-wide map required less fault-state data, time series in the order of hours or days instead of weeks or months, further motivating its application in rapid root cause analysis. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
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16 pages, 1187 KiB  
Article
Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network
by Philipp Menesklou, Tabea Sinn, Hermann Nirschl and Marco Gleiss
Minerals 2021, 11(7), 755; https://doi.org/10.3390/min11070755 - 13 Jul 2021
Cited by 10 | Viewed by 2086
Abstract
Continuously operating decanter centrifuges are often applied for solid-liquid separation in the chemical and mining industries. Simulation tools can assist in the configuration and optimisation of separation processes by, e.g., controlling the quality characteristics of the product. Increasing computation power has led to [...] Read more.
Continuously operating decanter centrifuges are often applied for solid-liquid separation in the chemical and mining industries. Simulation tools can assist in the configuration and optimisation of separation processes by, e.g., controlling the quality characteristics of the product. Increasing computation power has led to a renewed interest in hybrid models (subsequently named grey box model), which combine parametric and non-paramteric models. In this article, a grey box model for the simulation of the mechanical dewatering of a finely dispersed product in decanter centrifuges is discussed. Here, the grey box model consists of a mechanistic model (as white box model) presented in a previous research article and a neural network (as black box model). Experimentally determined data is used to train the neural network in the area of application. The mechanistic approach considers the settling behaviour, the sediment consolidation, and the sediment transport. In conclusion, the settings of the neural network and the results of the grey box model and white box model are compared and discussed. Now, the overall grey box model is able to increase the accuracy of the simulation and physical effects that are not modelled yet are integrated by training of a neural network using experimental data. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
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13 pages, 8480 KiB  
Article
Image Process of Rock Size Distribution Using DexiNed-Based Neural Network
by Haijie Li, Gauti Asbjörnsson and Mats Lindqvist
Minerals 2021, 11(7), 736; https://doi.org/10.3390/min11070736 - 07 Jul 2021
Cited by 11 | Viewed by 3081
Abstract
In an aggregate crushing plant, the crusher performances will be affected by the variation from the incoming feed size distribution. Collecting accurate measurements of the size distribution on the conveyors can help both operators and control systems to make the right decisions in [...] Read more.
In an aggregate crushing plant, the crusher performances will be affected by the variation from the incoming feed size distribution. Collecting accurate measurements of the size distribution on the conveyors can help both operators and control systems to make the right decisions in order to reduce overall power consumption and avoid undesirable operating conditions. In this work, a particle size distribution estimation method based on a DexiNed edge detection network, followed by the application of contour optimization, is proposed. The proposed framework was carried out in the four main steps. The first step, after image preprocessing, was to utilize a modified DexiNed convolutional neural network to predict the edge map of the rock image. Next, morphological transformation and watershed transformation from the OpenCV library were applied. Then, in the last step, the mass distribution was estimated from the pixel contour area. The accuracy and efficiency of the DexiNed method were demonstrated by comparing it with the ground-truth segmentation. The PSD estimation was validated with the laboratory screened rock samples. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
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Review

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17 pages, 378 KiB  
Review
AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing
by Amit Kumar Mishra
Minerals 2021, 11(10), 1118; https://doi.org/10.3390/min11101118 - 12 Oct 2021
Cited by 3 | Viewed by 5919
Abstract
In the last few years, jargon, such as machine learning (ML) and artificial intelligence (AI), have been ubiquitous in both popular science media as well as the academic literature. Many industries have tried the current suite of ML and AI algorithms with various [...] Read more.
In the last few years, jargon, such as machine learning (ML) and artificial intelligence (AI), have been ubiquitous in both popular science media as well as the academic literature. Many industries have tried the current suite of ML and AI algorithms with various degrees of success. Mineral processing, as an industry, is looking at AI for two reasons. First of all, as with other industries, it is pertinent to know if AI algorithms can be used to enhance productivity. The second reason is specific to the mining industry. Of late, the grade of ores is reducing, and the demand for ethical mining (with as little effect on ecology as possible) is increasing. Thus, mineral processing industries also want to explore the possible use of AI in solving these challenges. In this review paper, first, the challenges in mineral processing that can potentially be solved by AI are presented. Then, some of the most pertinent developments in the domain of ML and AI (applied in the domain of mineral processing) are discussed. Lastly, a top-level modus operandi is presented for a mineral processing industry that might want to explore the possibilities of using AI in its processes. Following are some of the new paradigms added by this review. This review presents a holistic view of the domain of mineral processing with an AI lens. It is also one of the first reviews in this domain to thoroughly discuss the use of AI in ethical, green, and sustainable mineral processing. The AI process proposed in this paper is a comprehensive one. To ensure the relevance to industry, the flow was made agile with the spiral system engineering flow. This is expected to drive rapid and agile investigation of the potential of applying ML and AI in different mineral processing industries. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
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