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New Frontiers in IOT and Computational Intelligence Applications in the...
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New Frontiers in IOT and Computational Intelligence Applications in the Mining Industry, Volume II

A special issue of Minerals (ISSN 2075-163X).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 7300

Special Issue Editors

Prof. Dr. Rajive Ganguli

E-Mail Website
Guest Editor
Department of Mining Engineering, University of Utah, Salt Lake City, UT 84112-0102, USA
Interests: machine learning; artificial intelligence; mining engineering; systems engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Pratt Rogers

E-Mail Website
Guest Editor
Department of Mining Engineering, University of Utah, Salt Lake City, UT 84112-0102, USA
Interests: production or operations management; operations research; machine learning; information or data centers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In our first volume, we showed that computational intelligence (machine learning, deep learning, genetic algorithms, etc.) is a mature and constantly evolving force within mining and mineral processing systems.  However, gauging and assessing this evolution is an important task to properly evaluate progress and disseminate knowledge to a wider audience.

The minerals industry is large and diverse. Companies of all sizes are attempting to efficiently extract wealth from natural resources. This process is enabled by technology of various kinds. Although many companies are technologically mature within their operations, others are technology laggards. Industrial internet of things (IOT) applications are growing the market of technology by reducing costs and, thus, expanding scale. These systems continue to close the tracking gaps of operations and improve the scalability of computational intelligence (CI) and site optimization. These new data acquisition points will continue to push the need for enhanced CI tools to handle and analyze these new data streams.

Therefore, in this Special Issue, we would like to continue to compile the state of the art in real-world mining industry IOT applications and CI. What is being applied at mine sites? What real-world tools are being developed for mining industry applications? What is working? Why do you think it is working? What is not working? What are the challenges?

Papers can be on any aspect of the mining industry. They must, however, be about IOT applications and/or computational intelligence applications that disrupt existing processes.  They must describe the application, benefits being seen, and/or the challenges to their deployment or development. Papers should not look like advertisements for commercial products. They should have sufficient technical information to satisfy reviewers, as well as readers.

Prof. Dr. Rajive Ganguli
Dr. Pratt Rogers
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

  • mining industry
  • machine learning
  • mine planning
  • IOT Applications
  • Web 3 technologies
  • system enabling technology
  • milling and processing
  • rock classification

Related Special Issue

Published Papers (5 papers)

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Research

11 pages, 839 KiB  
Article
Contextual Representation in NLP to Improve Success in Accident Classification of Mine Safety Narratives
by Rambabu Pothina and Rajive Ganguli
Minerals 2023, 13(6), 770; https://doi.org/10.3390/min13060770 - 03 Jun 2023
Cited by 1 | Viewed by 1140
Abstract
Contextual representation has taken center stage in Natural Language Processing (NLP) in the recent past. Models such as Bidirectional Encoder Representations from Transformers (BERT) have found tremendous success in the arena. As a first attempt in the mining industry, in the current work, [...] Read more.
Contextual representation has taken center stage in Natural Language Processing (NLP) in the recent past. Models such as Bidirectional Encoder Representations from Transformers (BERT) have found tremendous success in the arena. As a first attempt in the mining industry, in the current work, BERT architecture is adapted in developing the MineBERT model to accomplish the classification of accident narratives from the US Mine Safety and Health Administration (MSHA) data set. In the past multi-year research, several machine learning (ML) methods were used by authors to improve classification success rates in nine significant MSHA accident categories. Out of nine, for four major categories (“Type Groups”) and five “narrow groups”, Random Forests (RF) registered 75% and 42% classification success rates, respectively, on average, while keeping the false positives under 5%. Feature-based innovative NLP methods such as accident-specific expert choice vocabulary (ASECV) and similarity score (SS) methods were developed to improve upon the RF success rates. A combination of all these methods (“Stacked” approach) is able to slightly improve success over RF (71%) to 73.28% for the major category “Caught-in”. Homographs in narratives are identified as the major problem that was preventing further success. Their presence was creating ambiguity problems for classification algorithms. Adaptation of BERT effectively solved the problem. When compared to RF, MineBERT implementation improved success rates among major and narrow groups by 13% and 32%, respectively, while keeping the false positives under 1%, which is very significant. However, BERT implementation in the mining industry, which has unique technical aspects and jargon, brought a set of challenges in terms of preparation of data, selection of hyperparameters, and fine-tuning the model to achieve the best performance, which was met in the current research. Full article
(This article belongs to the Special Issue New Frontiers in IOT and Computational Intelligence Applications in the Mining Industry, Volume II)
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19 pages, 3598 KiB  
Article
Technology Upgrade Assessment for Open-Pit Mines through Mine Plan Optimization and Discrete Event Simulation
by Aldo Quelopana, Javier Órdenes, Ryan Wilson and Alessandro Navarra
Minerals 2023, 13(5), 642; https://doi.org/10.3390/min13050642 - 05 May 2023
Viewed by 1409
Abstract
Digital technologies are continually gaining traction in the mining and mineral processing industries. Several studies have shown the benefits of their application to help improve various aspects of the mineral value chain. Nevertheless, quantitatively assessing new technologies using a holistic approach is vital [...] Read more.
Digital technologies are continually gaining traction in the mining and mineral processing industries. Several studies have shown the benefits of their application to help improve various aspects of the mineral value chain. Nevertheless, quantitatively assessing new technologies using a holistic approach is vital to evaluate whether the potential localized benefits ultimately translate to an overall increase in project net present value (NPV). This study develops an integrated system-wide methodology for open-pit mines, supporting the technoeconomic assessment of implementing new technology that impacts strategic and operational timeframes. The first part of the framework relies on a state-of-the-art mine plan optimization algorithm that incorporates geological uncertainty. The resulting outputs are then fed into the discrete event simulation portion of the framework (second part) to maximize plant throughput using alternate modes of operation (blending strategy) and operational stockpiles to deal with unexpected changes in ore feed attributes. Sample calculations loosely based on a gold deposit located in the Maricunga belt, Chile, are presented in the context of evaluating different intelligent ore sorting technology options. Full article
(This article belongs to the Special Issue New Frontiers in IOT and Computational Intelligence Applications in the Mining Industry, Volume II)
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23 pages, 6860 KiB  
Article
Multi-Objective Scheduling Strategy of Mine Transportation Robot Based on Three-Dimensional Loading Constraint
by Xuanxuan Yan, Guorong Wang, Kuosheng Jiang, Ziming Kou, Kaisong Wang and Lixiang Zhang
Minerals 2023, 13(3), 431; https://doi.org/10.3390/min13030431 - 17 Mar 2023
Cited by 1 | Viewed by 1196
Abstract
In an attempt to solve the problems of the low intelligent distribution degree and high working intensity of auxiliary transportation systems in underground coal mines, an intelligent distribution strategy of materials in the whole mine is put forward. Firstly, combined with the characteristics [...] Read more.
In an attempt to solve the problems of the low intelligent distribution degree and high working intensity of auxiliary transportation systems in underground coal mines, an intelligent distribution strategy of materials in the whole mine is put forward. Firstly, combined with the characteristics of materials and standard containers, a three-dimensional loading model is established with the goal of maximizing the space utilization of standard containers, and a three-dimensional space segmentation heuristic algorithm is used to solve the material loading scheme. Then, the multi-objective optimization model of distribution parameters is established with the goal of the shortest delivery distance, the shortest delay time, and the fewest number of delivery vehicles, and the dual-layer genetic algorithm is used to solve the distribution scheme. Finally, the spatiotemporal conversion coefficient is designed to solve the task list by hierarchical clustering, and the solution time is reduced by 30%. The results show that the dual-layer genetic algorithm based on hierarchical clustering has good adaptability in complex material scheduling scenarios. Full article
(This article belongs to the Special Issue New Frontiers in IOT and Computational Intelligence Applications in the Mining Industry, Volume II)
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13 pages, 9943 KiB  
Article
IoT-Enabled Wearable Fatigue-Tracking System for Mine Operators
by W. Pratt Rogers, Joao Marques, Elaheh Talebi and Frank A. Drews
Minerals 2023, 13(2), 287; https://doi.org/10.3390/min13020287 - 18 Feb 2023
Viewed by 1437
Abstract
This study explores the possibility of investigating operator fatigue via the use of off-the-shelf wearable devices and custom applications. Fatigue is a complex biological phenomenon, and both subjective and objective data are needed to assess it properly. The development of any application and [...] Read more.
This study explores the possibility of investigating operator fatigue via the use of off-the-shelf wearable devices and custom applications. Fatigue is a complex biological phenomenon, and both subjective and objective data are needed to assess it properly. The development of any application and the assessments of fatigue should be guided by psychological insights. The methods used to conceptualize and develop a fatigue-tracking application on a wearable device are presented. Subjective fatigue data are collected using the Karolinska Sleepiness Scale, while the objective data are collected using reaction time measurements. The development and testing of the application are presented in this paper. Data collected with the system suggest that such a system can potentially replace other, more expensive and intrusive approaches to measure fatigue. Future work on IoT applications will need to examine organizational culture and support to assess the effectiveness of such an approach. Full article
(This article belongs to the Special Issue New Frontiers in IOT and Computational Intelligence Applications in the Mining Industry, Volume II)
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14 pages, 2382 KiB  
Article
Gaining Insight from Semi-Variograms into Machine Learning Performance of Rock Domains at a Copper Mine
by Narmandakh Sarantsatsral and Rajive Ganguli
Minerals 2022, 12(9), 1062; https://doi.org/10.3390/min12091062 - 23 Aug 2022
Cited by 1 | Viewed by 1288
Abstract
Machine learning (ML) is increasingly being leveraged by the mining industry to understand how rock properties vary at a mine site. In previously published work, the rock type, granodiorite, was predicted with high accuracy by the random forest (RF) ML method at the [...] Read more.
Machine learning (ML) is increasingly being leveraged by the mining industry to understand how rock properties vary at a mine site. In previously published work, the rock type, granodiorite, was predicted with high accuracy by the random forest (RF) ML method at the Erdenet copper mine in Mongolia. As a result of the optimistic results (86% overall success rate), this paper extended the research to determine if ML would be successful in modeling rock domains. Rock domains are groups of rocks that occur together. There were two additional goals. One was to determine if the variograms could predict or help understand how ML methods would perform on the data. The second was to determine if 2D modeling would perform well given the disseminated nature of the deposit. ML methods, multilayer perceptron (MLP), k-nearest neighborhood (KNN) and RF, were applied to model six rock domains, D0–D5, in 2D and 3D. Modeling performance was poor in 2D. Prediction performance accuracy was high in 3D for the domains D1 (92–94%), D2 (94–96%) and D4 (85–98%). Note that the domains D1 and D2 together constituted about 80% of the samples. Conclusions drawn in this paper are based on the results of 3D modeling since 2D modeling performance was poor. Prediction performance appeared to depend on two factors. It was better for a domain when the domain was not a minuscule proportion of the sample. It was also better for domains whose indicator semi-variogram (ISV) range was high. For example, though D4 only contributed 15% of the samples, the range was high. MLP did not perform as well as KNN and RF, with RF performing the best. The hyperparameters of KNN and RF suggested that performance was best when only a small number of samples were used to make a prediction. One overall summary conclusion is that the two most important domains, D1 and D2, could be predicted with high accuracy using ML. The second summary conclusion is that semi-variograms can provide insight into ML performance. Full article
(This article belongs to the Special Issue New Frontiers in IOT and Computational Intelligence Applications in the Mining Industry, Volume II)
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