# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
###### 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 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. open-pit mine; deep learning; deep neural network (DNN); truck-haulage system; ore production +
Footnote †: journal: Medical  
truck-dispatch techniques have been proposed to simulate truck systems using real-time data collected from mining sites. For example, [PERSON] 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. [PERSON] 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 and efficient processing of large amounts of big data obtained from ICT systems [42]. Deep learning is an artificial intelligence (AI) technique wherein a computer analyzes data characteristics and designs prediction models without human interference or prior knowledge [43]. Deep learning methods extract high-level data features by using models that comprise a hierarchical connection of the input, hidden, and output layers [44; 45]. Nodes within each layer are connected to those in subsequent layers and provide predicted values as output using a feed-forward method. In this way, training of a deep learning model continues until prediction errors are minimized [46; 47].