Editorial

3 pages, 171 KiB

Open AccessEditorial

Introduction to the Special Issue “Advances in Computational Intelligence Applications in the Mining Industry”

by Rajive Ganguli, Sean Dessureault and Pratt Rogers

Minerals 2022, 12(1), 67; https://doi.org/10.3390/min12010067 - 05 Jan 2022

Viewed by 1263

This is an exciting time for the mining industry, as it is on the cusp of a change in efficiency as it gets better at leveraging data [...] Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

Research

Jump to: Editorial, Review

17 pages, 65312 KiB

Open AccessArticle

Model Scaling in Smartphone GNSS-Aided Photogrammetry for Fragmentation Size Distribution Estimation

by Zedrick Paul L. Tungol, Hisatoshi Toriya, Narihiro Owada, Itaru Kitahara, Fumiaki Inagaki, Mahdi Saadat, Hyong Doo Jang and Youhei Kawamura

Minerals 2021, 11(12), 1301; https://doi.org/10.3390/min11121301 - 23 Nov 2021

Cited by 7 | Viewed by 2119

Abstract

Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control [...] Read more.

Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data. Full article

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17 pages, 3416 KiB

Open AccessArticle

Application of Deep Learning in Petrographic Coal Images Segmentation

by Sebastian Iwaszenko and Leokadia Róg

Minerals 2021, 11(11), 1265; https://doi.org/10.3390/min11111265 - 13 Nov 2021

Cited by 11 | Viewed by 3109

Abstract

The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it [...] Read more.

The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various technological processes. This paper considers the application of convolutional neural networks for coal petrographic images segmentation. The U-Net-based model for segmentation was proposed. The network was trained to segment inertinite, liptinite, and vitrinite. The segmentations prepared manually by a domain expert were used as the ground truth. The results show that inertinite and vitrinite can be successfully segmented with minimal difference from the ground truth. The liptinite turned out to be much more difficult to segment. After usage of transfer learning, moderate results were obtained. Nevertheless, the application of the U-Net-based network for petrographic image segmentation was successful. The results are good enough to consider the method as a supporting tool for domain experts in everyday work. Full article

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19 pages, 4298 KiB

Open AccessArticle

Applied Machine Learning for Geometallurgical Throughput Prediction—A Case Study Using Production Data at the Tropicana Gold Mining Complex

by Christian Both and Roussos Dimitrakopoulos

Minerals 2021, 11(11), 1257; https://doi.org/10.3390/min11111257 - 12 Nov 2021

Cited by 12 | Viewed by 3305

Abstract

With the increased use of digital technologies in the mining industry, the amount of centrally stored production data is continuously growing. However, datasets in mines and processing plants are not fully utilized to build links between extracted materials and metallurgical plant performances. This [...] Read more.

With the increased use of digital technologies in the mining industry, the amount of centrally stored production data is continuously growing. However, datasets in mines and processing plants are not fully utilized to build links between extracted materials and metallurgical plant performances. This article shows a case study at the Tropicana Gold mining complex that utilizes penetration rates from blasthole drilling and measurements of the comminution circuit to construct a data-driven, geometallurgical throughput prediction model of the ball mill. Several improvements over a previous publication are shown. First, the recorded power draw, feed particle and product particle size are newly considered. Second, a machine learning model in the form of a neural network is used and compared to a linear model. The article also shows that hardness proportions perform 6.3% better than averages of penetration rates for throughput prediction, underlining the importance of compositional approaches for non-additive geometallurgical variables. When adding ball mill power and product particle size, the prediction error (RMSE) decreases by another 10.6%. This result can only be achieved with the neural network, whereas the linear regression shows improvements of 4.2%. Finally, it is discussed how the throughput prediction model can be integrated into production scheduling. Full article

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22 pages, 5021 KiB

Open AccessArticle

Diagnosis of Problems in Truck Ore Transport Operations in Underground Mines Using Various Machine Learning Models and Data Collected by Internet of Things Systems

by Sebeom Park, Dahee Jung, Hoang Nguyen and Yosoon Choi

Minerals 2021, 11(10), 1128; https://doi.org/10.3390/min11101128 - 14 Oct 2021

Cited by 12 | Viewed by 2229

Abstract

This study proposes a method for diagnosing problems in truck ore transport operations in underground mines using four machine learning models (i.e., Gaussian naïve Bayes (GNB), k-nearest neighbor (kNN), support vector machine (SVM), and classification and regression tree (CART)) and data collected by [...] Read more.

This study proposes a method for diagnosing problems in truck ore transport operations in underground mines using four machine learning models (i.e., Gaussian naïve Bayes (GNB), k-nearest neighbor (kNN), support vector machine (SVM), and classification and regression tree (CART)) and data collected by an Internet of Things system. A limestone underground mine with an applied mine production management system (using a tablet computer and Bluetooth beacon) is selected as the research area, and log data related to the truck travel time are collected. The machine learning models are trained and verified using the collected data, and grid search through 5-fold cross-validation is performed to improve the prediction accuracy of the models. The accuracy of CART is highest when the parameters leaf and split are set to 1 and 4, respectively (94.1%). In the validation of the machine learning models performed using the validation dataset (1500), the accuracy of the CART was 94.6%, and the precision and recall were 93.5% and 95.7%, respectively. In addition, it is confirmed that the F1 score reaches values as high as 94.6%. Through field application and analysis, it is confirmed that the proposed CART model can be utilized as a tool for monitoring and diagnosing the status of truck ore transport operations. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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12 pages, 1201 KiB

Open AccessArticle

A Case Study of Rock Type Prediction Using Random Forests: Erdenet Copper Mine, Mongolia

by Narmandakh Sarantsatsral, Rajive Ganguli, Rambabu Pothina and Batmunkh Tumen-Ayush

Minerals 2021, 11(10), 1059; https://doi.org/10.3390/min11101059 - 28 Sep 2021

Cited by 6 | Viewed by 2308

Abstract

In a mine, knowledge of rock types is often desired as they are important indicators of grade, mineral processing complications, or geotechnical attributes. It is common to model the rock types with visual graphics tools using geologist-generated rock type information in exploration drillhole [...] Read more.

In a mine, knowledge of rock types is often desired as they are important indicators of grade, mineral processing complications, or geotechnical attributes. It is common to model the rock types with visual graphics tools using geologist-generated rock type information in exploration drillhole databases. Instead of this manual approach, this paper used random forest (RF), a machine learning (ML) algorithm, to model the rock type at Erdenet Copper Mine, Mongolia. Exploration drillhole data was used to develop the RF models and predict the rock type based on the coordinates of locations. Data selection and model evaluation methods were designed to ensure applicability for real life scenarios. In the scenario where rock type is predicted close to locations where information is available (such as in blocks being blasted), RF did very well with an overall success rate (OSR) of 89%. In the scenario where rock type was predicted for two future benches (i.e., 30 m below known locations), the best OSR was 86%. When an exploration program was simulated, performance was poor with a OSR of 59%. The results indicate that EMC can leverage RF models for short-term and long-term planning by predicting rock types within drilling blocks or future blocks quite accurately. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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20 pages, 3728 KiB

Open AccessArticle

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

by Leszek Chomacki, Janusz Rusek and Leszek Słowik

Minerals 2021, 11(9), 958; https://doi.org/10.3390/min11090958 - 01 Sep 2021

Cited by 7 | Viewed by 2079

Abstract

This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area of artificial intelligence (AI) methods. Ultimately, two [...] Read more.

This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area of artificial intelligence (AI) methods. Ultimately, two models of damage risk assessment were built using the Naive Bayes classifier (NBC) and Bayesian Networks (BN). The first model was used to compare results obtained using the more computationally advanced Bayesian network methodology. In the case of the Bayesian network, the unknown Directed Acyclic Graph (DAG) structure was extracted using Chow-Liu’s Tree Augmented Naive Bayes (TAN-CL) algorithm. Thus, one of the methods involving Bayesian Network Structure Learning from data (BNSL) was implemented. The application of this approach represents a novel scientific contribution in the interdisciplinary field of mining and civil engineering. The models created were verified with respect to quality of fit to observed data and generalization properties. The connections in the Bayesian network structure obtained were also verified with respect to the observed relations occurring in engineering practice concerning the assessment of the damage intensity to masonry buildings in mining areas. This allowed evaluation of the model and justified the utility of the conducted research in the field of protection of mining areas. The possibility of universal application of the Bayesian network, both in the case of damage prediction and diagnosis of its potential causes, was also pointed out. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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21 pages, 8627 KiB

Open AccessArticle

Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems

by Brian Bino Sinaice, Narihiro Owada, Mahdi Saadat, Hisatoshi Toriya, Fumiaki Inagaki, Zibisani Bagai and Youhei Kawamura

Minerals 2021, 11(8), 846; https://doi.org/10.3390/min11080846 - 05 Aug 2021

Cited by 11 | Viewed by 3375

Abstract

Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating [...] Read more.

Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating Hyperspectral Imaging, Neighbourhood Component Analysis (NCA) and Machine Learning (ML) as a combined system that can identify rocks and minerals. Modestly put, hyperspectral imaging gathers electromagnetic signatures of the rocks in hundreds of spectral bands. However, this data suffers from what is termed the ‘dimensionality curse’, which led to our employment of NCA as a dimensionality reduction technique. NCA, in turn, highlights the most discriminant feature bands, number of which being dependent on the intended application(s) of this system. Our envisioned application is rock and mineral classification via unmanned aerial vehicle (UAV) drone technology. In this study, we performed a 204-hyperspectral to 5-band multispectral reduction, because current production drones are limited to five multispectral bands sensors. Based on these bands, we applied ML to identify and classify rocks, thereby proving our hypothesis, reducing computational costs, attaining an ML classification accuracy of 71%, and demonstrating the potential mining industry optimisations attainable through this integrated system. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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13 pages, 245 KiB

Open AccessArticle

Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine

by Rajive Ganguli, Preston Miller and Rambabu Pothina

Minerals 2021, 11(7), 776; https://doi.org/10.3390/min11070776 - 17 Jul 2021

Cited by 10 | Viewed by 2786

Abstract

To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis [...] Read more.

To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

30 pages, 2265 KiB

Open AccessArticle

Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin

by Ryan Wilson, Patrick H. J. Mercier, Bussaraporn Patarachao and Alessandro Navarra

Minerals 2021, 11(7), 689; https://doi.org/10.3390/min11070689 - 26 Jun 2021

Cited by 14 | Viewed by 3023

Abstract

Oil remains a major contributor to global primary energy supply and is, thus, fundamental to the continued functioning of modern society and related industries. Conventional oil and gas reserves are finite and are being depleted at a relatively rapid pace. With alternative fuels [...] Read more.

Oil remains a major contributor to global primary energy supply and is, thus, fundamental to the continued functioning of modern society and related industries. Conventional oil and gas reserves are finite and are being depleted at a relatively rapid pace. With alternative fuels and technologies still unable to fill the gap, research and development of unconventional petroleum resources have accelerated markedly in the past 20 years. With some of the largest bitumen deposits in the world, Canada has an active oil mining and refining industry. Bitumen deposits, also called oil sands, are formed in complex geological environments and subject to a host of syn- and post-depositional processes. As a result, some ores are heterogeneous, at both individual reservoir and regional scales, which poses significant problems in terms of extractive processing. Moreover, with increased environmental awareness and enhanced governmental regulations and industry best practices, it is critical for oil sands producers to improve process efficiencies across the spectrum. Discrete event simulation (DES) is a computational paradigm to develop dynamic digital twins, including the interactions of critical variables and processes. In the case of mining systems, the digital twin includes aspects of geological uncertainty. The resulting simulations include alternate operational modes that are characterized by separate operational policies and tactics. The current DES framework has been customized to integrate predictive modelling data, generated via partial least squares (PLS) regression, in order to evaluate system-wide response to geological uncertainty. Sample computations that are based on data from Canada’s oil sands are presented, showing the framework to be a powerful tool to assess and attenuate operational risk factors in the extractive processing of bitumen deposits. Specifically, this work addresses blending control strategies prior to bitumen extraction and provides a pathway to incorporate geological variation into decision-making processes throughout the value chain. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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22 pages, 7538 KiB

Open AccessArticle

Modelling Large Heaped Fill Stockpiles Using FMS Data

by Aaron Young and William Pratt Rogers

Minerals 2021, 11(6), 636; https://doi.org/10.3390/min11060636 - 15 Jun 2021

Cited by 4 | Viewed by 4626

Abstract

The frequent best practice for managing large low-grade run-of-mine (ROM) stockpiles is to average the entire stockpile to only one grade. Modern ore control and mineral processing procedures need better precision. Low-precision models hinder the ability to create a digital mine-to-mill model and [...] Read more.

The frequent best practice for managing large low-grade run-of-mine (ROM) stockpiles is to average the entire stockpile to only one grade. Modern ore control and mineral processing procedures need better precision. Low-precision models hinder the ability to create a digital mine-to-mill model and optimize the holistic mining process. Prior to processing, poorly characterized stockpiles are often drilled and sampled, despite there being no geological reason for relationships between samples to exist. Stockpile management is also influenced by reserve accounting and lacks a common operational workflow. This paper provides a review of base and precious metal run-of-mine (ROM) pre-crusher stockpiles in the mining industry, and demonstrates how to build a spatial model of a large long-term stockpile using fleet management system (FMS) data and geostatistical code in Python and R Studio. We demonstrate a framework for modelling a stockpile believed to be readily workable for most modern mines through use of established geostatistical modelling techniques applied to the type of data generated in a FMS. In the method presented, each bench of the stockpile is modeled as its own geological domain. Size of dump loads is assumed to contain the same volume of material and grade values that match those of the grade data tracked in the FMS. Despite the limitations of these inputs, existing interpolation techniques can lead to increased understanding of the grade distribution within stockpiles. Using the framework demonstrated in this paper, engineers and stockpile managers will be able to leverage operational data into valuable insight for empowered decision making and smoother operations. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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22 pages, 3958 KiB

Open AccessArticle

Modeling Mine Workforce Fatigue: Finding Leading Indicators of Fatigue in Operational Data Sets

by Elaheh Talebi, W. Pratt Rogers, Tyler Morgan and Frank A. Drews

Minerals 2021, 11(6), 621; https://doi.org/10.3390/min11060621 - 10 Jun 2021

Cited by 9 | Viewed by 3087

Abstract

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety [...] Read more.

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue’s impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners’ understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide “lagging indicators” of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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20 pages, 10086 KiB

Open AccessArticle

Study of the Influence of Non-Deposit Locations in Data-Driven Mineral Prospectivity Mapping: A Case Study on the Iskut Project in Northwestern British Columbia, Canada

by Alix Lachaud, Adam Marcus, Slobodan Vučetić and Ilija Mišković

Minerals 2021, 11(6), 597; https://doi.org/10.3390/min11060597 - 01 Jun 2021

Cited by 6 | Viewed by 2223

Abstract

The accuracy of data-driven predictive mineral prospectivity models relies heavily on the training datasets used. These models are usually trained using data for “known” deposit locations as well as “non-deposit” locations that are based on randomly generated point patterns. In this study, data [...] Read more.

The accuracy of data-driven predictive mineral prospectivity models relies heavily on the training datasets used. These models are usually trained using data for “known” deposit locations as well as “non-deposit” locations that are based on randomly generated point patterns. In this study, data related to the Seabridge Gold Inc Iskut project, an epithermal Au deposit in northwestern British Columbia (BC), Canada, are used to test the utility of data-driven mineral prospectivity modeling. The input spatial dataset is comprised mostly of publicly available data. Data for 18 vein and epithermal Au known mineral occurrences (KMO) are obtained from the BC Geological Survey’s MINFILE repository and selected as training deposit locations. A total of eleven sets of non-deposit locations (NDL) were also created, including one set of selected non-prospective KMO for Au deposits from the MINFILE and ten sets of random point patterns. Given the scale of this study, most of the KMO recorded on the property are of the epithermal deposit type. Hence, they could not be used as a selection criterion. Data-driven mineral potential models are generated using the random forest (RF) algorithm and trained on multiple data sets. The comparison of RF models demonstrated that using non-prospective KMO generates more accurate predictions than the random point pattern. The produced mineral prospectivity maps delineated multiple areas with higher discovery potential, which matched viable targets for the Au-Cu epithermal-porphyry system identified through previous Seabridge Gold Inc. (Toronto, ON, Canada) field reconnaissance and drilling programs. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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21 pages, 7885 KiB

Open AccessArticle

Use of Decision Trees for the Development of Decision Support Systems for the Control of Grinding Circuits

by Jacques Olivier and Chris Aldrich

Minerals 2021, 11(6), 595; https://doi.org/10.3390/min11060595 - 31 May 2021

Cited by 9 | Viewed by 3207

Abstract

Grinding circuits can exhibit strong nonlinear behaviour, which may make automatic supervisory control difficult and, as a result, operators still play an important role in the control of many of these circuits. Since the experience among operators may be highly variable, control of [...] Read more.

Grinding circuits can exhibit strong nonlinear behaviour, which may make automatic supervisory control difficult and, as a result, operators still play an important role in the control of many of these circuits. Since the experience among operators may be highly variable, control of grinding circuits may not be optimal and could benefit from automated decision support. This could be based on heuristics from process experts, but increasingly could also be derived from plant data. In this paper, the latter approach, based on the use of decision trees to develop rule-based decision support systems, is considered. The focus is on compact, easy to understand rules that are well supported by the data. The approach is demonstrated by means of an industrial case study. In the case study, the decision trees were not only able to capture operational heuristics in a compact intelligible format, but were also able to identify the most influential variables as reliably as more sophisticated models, such as random forests. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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17 pages, 4021 KiB

Open AccessArticle

Integrating Production Planning with Truck-Dispatching Decisions through Reinforcement Learning While Managing Uncertainty

by Joao Pedro de Carvalho and Roussos Dimitrakopoulos

Minerals 2021, 11(6), 587; https://doi.org/10.3390/min11060587 - 31 May 2021

Cited by 18 | Viewed by 3643

Abstract

This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization [...] Read more.

This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization in a mining complex context. Several sources of operational uncertainty arising from the loading, hauling and dumping activities can influence the dispatching strategy. Given a fixed sequence of extraction of the mining blocks provided by the short-term plan, a discrete event simulator model emulates the interaction arising from these mining operations. The continuous repetition of this simulator and a reward function, associating a score value to each dispatching decision, generate sample experiences to train a deep Q-learning reinforcement learning model. The model learns from past dispatching experience, such that when a new task is required, a well-informed decision can be quickly taken. The approach is tested at a copper–gold mining complex, characterized by uncertainties in equipment performance and geological attributes, and the results show improvements in terms of production targets, metal production, and fleet management. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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Review

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30 pages, 3977 KiB

Open AccessReview

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

by Nelson K. Dumakor-Dupey, Sampurna Arya and Ankit Jha

Minerals 2021, 11(6), 601; https://doi.org/10.3390/min11060601 - 03 Jun 2021

Cited by 27 | Viewed by 9084

Abstract

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and [...] Read more.

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence Applications in the Mining Industry)

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