Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mineral Processing and Grinding · Mining Techniques and Economics · Mining and Gasification Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Korea
Funding
National Research Foundation of Korea
References (83)
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