Analysing equipment allocation through queuing theory and Monte-Carlo simulations in surface mining operations

2021

In this study, we developed a system to collect and analyze log data related to truck travel times in underground mines using Bluetooth beacons and tablet computers. When a signal from beacons installed at a major underground mine is received by a truck-mounted tablet computer, the beacon information is collected and uploaded to a cloud server. A data processing program integrates the uploaded log data files into a single file, calculating the statistical values for each section of the transport route. The developed system was applied to a limestone underground mine located in Jeongseon, Korea, to diagnose and analyze the transport routes in the study area. As a result of this analysis, it was possible to select sections in which the truck transport time was stable and sections in which it was unstable. Consequently, the transport route could be classified into four types based on the distribution and fluctuations in the truck transport time data. Moreover, it was possible to analyze the causes of the stable and unstable sections through production logs and field staff interviews. The developed system could be used as a tool to improve transport operations by diagnosing and analyzing the truck transport routes of a mine.

Section: Implications For Simulating Truck Haulage Systems In Undergr...mentioning

confidence: 99%

Analysis and Diagnosis of Truck Transport Routes in Underground Mines Using Transport Time Data Collected through Bluetooth Beacons and Tablet Computers

Park

2021

“…Therefore, it is essential to design a truck-haulage system that not only maximizes mine productivity and equipment-management efficiency but also minimizes haulage cost [2]. Recently, several discrete-event simulation techniques [3][4][5][6][7][8][9][10][11] have been proposed for realizing effective truck-haulage systems designs. The allocation phase in the simulation of truck-haulage systems involves selecting the type, size, number, and payload of fleets suitable for use in haulage operations.…”

Section: Introductionmentioning

confidence: 99%

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

Baek

2020

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 ...

“…In the case of a mine truck haulage system, simulations may be used to predict productivity indices, such as the mine production and equipment utilization, according to system operating conditions, including the task time, amount of equipment, dispatch interval, and time consumed per unit task [9,10]. In this manner, it is possible to select optimal equipment combinations [11][12][13][14], establish equipment dispatch plans [10,[15][16][17][18], and identify optimal haulage routes [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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

Baek

2019

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.