Special Issue "Emerging Technologies for Harnessing the Fourth Industrial Revolution in the Energy and Mineral Industries"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy".

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editor

Prof. Dr. Yosoon Choi
Website
Guest Editor
Department of Energy Resources Engineering, Pukyong National University, Busan 48513, South Korea
Interests: smart mining; renewables in mining; space mining; AICBM (AI, IoT, cloud, big data, and mobile) convergence; unmanned aerial vehicle; mine planning and design; open pit mining operation; mine safety; geographic information systems; 3D geo-modeling; geostatistics; hydrological analysis; energy analysis and simulation; design of solar energy conversion systems; renewable energy systems
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Special Issue Information

Dear Colleagues,

The Fourth Industrial Revolution provides new ways in which technology can become embedded within societies and even the human body. As the Fourth Industrial Revolution gathers pace, innovations are becoming faster, more efficient, and more widely accessible than before. Technology is also becoming increasingly connected; in particular, there are emerging technologies including the Internet of Things (IoT), cloud computing, big data analytics, mobile and wearable devices, augmented and virtual realities, 3D printing, robotics, autonomous vehicles, and artificial intelligence (AI) breakthroughs in various fields. This Special Issue aims at encouraging researchers to address the emerging technologies for harnessing the Fourth Industrial Revolution in the energy and mineral industries. Articles providing examples of the improvements brought by the emerging technologies in the energy and mineral sectors can be included.

Assoc. Prof. Dr. Yosoon Choi
Guest Editor

Manuscript Submission Information

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Published Papers (7 papers)

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Research

Open AccessArticle
Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines
Appl. Sci. 2020, 10(5), 1657; https://doi.org/10.3390/app10051657 - 01 Mar 2020
Abstract
This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration [...] Read more.
This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively. Full article
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Open AccessArticle
Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach
Appl. Sci. 2020, 10(4), 1267; https://doi.org/10.3390/app10041267 - 13 Feb 2020
Abstract
Due to the rapid development of shale gas, a system has been established that can utilize a considerable amount of data using the database system. As a result, many studies using various machine learning techniques were carried out to predict the productivity of [...] Read more.
Due to the rapid development of shale gas, a system has been established that can utilize a considerable amount of data using the database system. As a result, many studies using various machine learning techniques were carried out to predict the productivity of shale gas reservoirs. In this study, a comprehensive analysis is performed for a machine learning method based on data-driven approaches that evaluates productivity for shale gas wells by using various parameters such as hydraulic fracturing and well completion in Eagle Ford shale gas field. Two techniques are used to improve the performance of the productivity prediction machine learning model developed in this study. First, the optimal input variables were selected by using the variables importance method (VIM). Second, cluster analysis was used to analyze the similarities in the datasets and recreate the machine learning models for each cluster to compare the training and test results. To predict productivity, we used random forest (RF), gradient boosting tree (GBM), and support vector machine (SVM) supervised learning models. Compared to other supervised learning models, RF, which is applied with the VIM, has the best prediction performance. The retraining model through cluster analysis has excellent predictive performance. The developed model and prediction workflow are considered useful for reservoir engineers planning of field development plan. Full article
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Open AccessArticle
A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer
Appl. Sci. 2020, 10(2), 635; https://doi.org/10.3390/app10020635 - 16 Jan 2020
Cited by 1
Abstract
In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree [...] Read more.
In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research. Full article
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Open AccessArticle
A Mining Technology Collaboration Platform Theory and Its Product Development and Application to Support China’s Digital Mine Construction
Appl. Sci. 2019, 9(24), 5373; https://doi.org/10.3390/app9245373 - 09 Dec 2019
Abstract
At present, data exchange in China’s digital mine construction process is still based on paper media or electronic documents. The problems of “information islands,” “information versions,” “information faults” and “information preservation” are serious. There are many problems associated with across time and space [...] Read more.
At present, data exchange in China’s digital mine construction process is still based on paper media or electronic documents. The problems of “information islands,” “information versions,” “information faults” and “information preservation” are serious. There are many problems associated with across time and space and multidisciplinary collaborations, blocked business processes and unclear job responsibilities. These problems have seriously hindered the construction of China’s digital mine, thus restricting the safe, efficient production and sustainable development of China’s mining enterprises. Therefore, this paper proposes the concept, connotation, characteristics, architecture and technical requirements of the mining technology collaboration platform and uses it to guide the research and development and implementation of the mining technology collaboration platform of Fujian Makeng Mining Co., Ltd. The results show that the mining technology collaboration platform can solve the information and management problems existing in China’s digital mine construction and realize the centralized storage, interoperability and high sharing of all data, the integration of all involved business, business software and its participants, the clarification of responsibilities and its input and output data of each post and standardization and automation of business process. Therefore, it improves the ability to collaborate across time and space and multidisciplinary among participants, departments and professional posts, ensures high-speed flow of business processes and also improves the working efficiency and quality of mining enterprises and significantly reduces the time for business processing and business process flow and reduces production costs. Full article
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Open AccessArticle
Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model
Appl. Sci. 2019, 9(21), 4554; https://doi.org/10.3390/app9214554 - 27 Oct 2019
Cited by 5
Abstract
Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, [...] Read more.
Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure. Full article
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Open AccessArticle
Simulation of Truck Haulage Operations in an Underground Mine Using Big Data from an ICT-Based Mine Safety Management System
Appl. Sci. 2019, 9(13), 2639; https://doi.org/10.3390/app9132639 - 29 Jun 2019
Cited by 2
Abstract
Information communication technology (ICT)-based mine safety management systems are being introduced at numerous mining sites to track the location of equipment and workers in real time and monitor environmental changes. This paper presents the results of a case study in which the big [...] Read more.
Information communication technology (ICT)-based mine safety management systems are being introduced at numerous mining sites to track the location of equipment and workers in real time and monitor environmental changes. This paper presents the results of a case study in which the big data created by an ICT-based mine safety management system are used for simulating truck haulage operations. An underground limestone mine located in Danyang, South Korea was studied, and the data generated over three months, from October 1 to December 31, 2018, were analyzed. Truck tag packet data recognized by relays were extracted and analyzed to calculate the averages and standard deviations of the truck travel times of each mine segment. A discrete event simulation program that simulates truck haulage operations in the study area was developed. Haulage times, the number of haulage operations, production output, and truck delay times were predicted, and results were compared with the actual operation results that were obtained on January 2 and 9, 2019. The difference between the predicted and actual results for the total amount of loaded ore was 30 tons for January 2 and 0 tons for January 9. The mean absolute error between the predicted and observed truck travel times was 0.13 min for January 2 and 0.14 min for January 9. The truck travel times that were measured differently according to the data aggregation period were set as temporal factors, and truck haulage simulations were performed. The results showed that more reliable simulation results were obtained as data accumulation time increased. Full article
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Open AccessFeature PaperArticle
Performance Comparison of User Interface Devices for Controlling Mining Software in Virtual Reality Environments
Appl. Sci. 2019, 9(13), 2584; https://doi.org/10.3390/app9132584 - 26 Jun 2019
Cited by 1
Abstract
Recently, many studies have been conducted to apply virtual reality (VR) technology to the mining industry. To accomplish this, it is necessary to develop user interface devices that can effectively control software. Most VR content in the mining industry requires precise device control [...] Read more.
Recently, many studies have been conducted to apply virtual reality (VR) technology to the mining industry. To accomplish this, it is necessary to develop user interface devices that can effectively control software. Most VR content in the mining industry requires precise device control for equipment operation or accident response. In this study, we compare the performance of four user interface devices (a 2D mouse, 2D & 3D mice, a VR controller, and a Kinect (Microsoft) sensor and bend-sensing data glove) for controlling mining industry software in a VR environment. The total working time, number of device clicks and click accuracy, were analyzed based on 10 experimenters performing 3D orebody modeling, using each device in the VR environment. Furthermore, we conducted a survey to evaluate the ease of learning, ease of use, immersion and fatigue of each device after the experiment. The results show that the 2D mouse yields a high performance in terms of its working time, click accuracy, ease of learning and ease of use. However, the 2D mouse did not completely leverage the VR environment, owing to low user immersion. The Kinect sensor and bend-sensing data glove could control the software efficiently while maximizing user immersion. Our findings are expected to provide a useful reference for the future development of user interface devices in the mining industry. Full article
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