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

Abstract

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.

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

“…In the equipment management category, a fault diagnosis [73,74] study that diagnosed equipment defects was performed. Haulage operations [75][76][77][78][79] and navigation [80] studies were conducted to optimize the transportation means, such as trucks and loaders, and to indicate the travelling mode of equipment, respectively. Predictive maintenance [81] study was performed to enhance the mine operation efficiency by predicting the equipment failure.…”

“…The evaluation indices depend on the purpose of the ML model. ML models used for classification (e.g., prediction of ore production and crusher utilization [76] and prediction of flyrock in open-pit blasting operations [57]) are generally evaluated in terms of the receiver operating characteristic/area under the curve (AUC) or confusion matrix. Models associated with regression (e.g., to estimate the cost of a mine, the cost is estimated using various variables such as the capacity of the mine and the distance from the railroad [43,44]) are generally evaluated in terms of the mean squared error (MSE), mean absolute error (MAE), or coefficient of determination (R) [2].…”

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

“…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.…”

Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System

“…In addition, mathematical optimization techniques can be applied to determine an optimal solution for the truck haulage operations. To date, several algorithms for truck haulage systems have been developed based on linear programming [5][6][7][8], genetic algorithms [9], queuing theory [10][11][12][13][14][15], fuzzy logic [16], and deep neural networks [17,18].…”