Simulation of Truck Haulage Operations in an Underground Mine Using Big Data from an ICT-Based Mine Safety Management System

Baek, Jieun; Choi, Yosoon

doi:10.3390/app9132639

Cited by 17 publications

(12 citation statements)

References 37 publications

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“…Several studies have been conducted to simulate the truck haulage system using data derived from the mine safety management system. For instance, Baek and Choi [72] presented a knowledge-based simulation methodology for truck haulage systems in underground mines by considering the truck travel time, which was extracted from big data of a mine safety management system. Moreover, Baek and Choi [73] developed a deep neural network (DNN) model, which was trained using a large set of truck haulage system operation conditions and truck cycle times to predict the ore production and crusher utilization of a truck haulage system in an underground mine.…”

Section: Future Direction Of Gis In Miningmentioning

confidence: 99%

Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management

2020

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In this study, geographic information system (GIS)-based methods and applications utilized for mine development were reviewed. Three types of GIS-based studies, namely studies on mine planning, operation, and environmental management, were examined to describe the role of GIS as a decision-making support tool in mine development. This review was conducted by classifying previous GIS-based studies into several subtopics that pertain to mine development activities and the range of environments to be managed. Because the use of GIS is appropriate for spatial data management related to ore deposits and mine environment conditions at various scales, the applications of GIS-based methods in mine development could be expanded further.

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Section: Future Direction Of Gis In Miningmentioning

confidence: 99%

Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management

2020

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“…For this purpose, simulation factors (daily working time, number of dispatched trucks, truck loading capacity, etc.) and temporal factors (truck-haulage operation time) are entered into the simulation algorithm [38]. Truck-haulage operation time consists of discrete events, such as ore loading, traveling, ore dumping, spotting, and waiting, and can be defined by the truck cycle time theory proposed by Subolesk [69].…”

Section: Design Of Dnn Modelmentioning

confidence: 99%

“…Information communication technologies (ICTs), such as wireless communications, sensor networks, global positioning system (GPS), and cloud computing, have been implemented at open-pit-mining sites to facilitate real-time monitoring of the operating status and haulage information of equipment, as well as collection of equipment-tracking data on web servers [30][31][32][33][34][35][36][37][38]. Further, several In this study, an open-pit limestone mine installed with an ICT-based mine-safety-management system was used as the investigation location.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Baek

Choi

2020

Applied Sciences

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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. truck-dispatch techniques have been proposed to simulate truck systems using real-time data collected from mining sites. For example, Tan et al. [39] identified discrete haulage-operation events by analyzing historical GPS truck-tracking data and implemented a truck-dispatch simulation algorithm using the Arena ® software. Chaowasakoo et al. [40] developed a real-time truck-dispatch system that plans truck-haulage strategies using truck-shovel activity-time data obtained from GPS.However, providing a comprehensive and effective truck-haulage systems simulation algorithm is difficult. First, open-pit mines are characterized by frequent changes in loading points during operations along with the use of multiple equipment-movement paths, unlike underground mines [1]. Additionally, several environmental factors, such as the weather, rock characteristics, and road conditions, affect haulage operations [12,41]. Thus, it is difficult to design a simulation algorithm for truck-haulage systems in large open-pit mines, where ore loading and haulage operations are simultaneously performed at multiple locations. Moreover, several types of simulation algorithms can be developed for a haulage plan that changes in real time, and it is difficult to alter these algorithms depending on variations in the haulage plan. Therefore, it is necessary to develop a simulation strategy that understands not only the characteristics of truck-haulage systems but also the sequence of discrete haulage operations. Additionally, the simulation technique must accurately predict ore production and equipment utilization using equipment-tracking data previously accumulated without use of prior-knowledge-based algorithms.Deep learning methods have recently attracted increased research interest because they facilitate easy analysis ...

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“…Therefore, the mine safety management system creates big data, which records the mining work on a web server. Further details on the study area mine safety management system and big data can be found in Baek and Choi [23].…”

Section: Study Areamentioning

confidence: 99%

“…To address these problems, technologies such as sensor networks, mobile devices, the Internet of things, and cloud computing have been introduced into mining sites, and researchers have been studying simulation techniques using big data collected on-site rather than algorithms based on prior knowledge [23]. Moreover, deep learning has been attracting attention as a technology that efficiently analyzes big data according to its purpose [24,25].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

Baek

Choi

2019

Applied Sciences

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A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN model structure for ore production prediction was set to five hidden layers and 40 hidden layer nodes. The test data exhibited a coefficient of determination of 0.99 and mean absolute percentage error (MAPE) of 2.80%. The optimal configuration for the crusher utilization prediction was set to four hidden layers and 40 hidden layer nodes, and the test data exhibited a coefficient of determination of 0.99 and MAPE of 2.49%. The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.

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Simulation of Truck Haulage Operations in an Underground Mine Using Big Data from an ICT-Based Mine Safety Management System

Cited by 17 publications

References 37 publications

Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management

Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

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