Prediction of Ore Production in a Limestone Underground Mine by Combining Machine Learning and Discrete Event Simulation Techniques
نویسندگان
چکیده
This study proposes a novel approach for enhancing the productivity of mining haulage systems by developing hybrid model that combines machine learning (ML) and discrete event simulation (DES) techniques to predict ore production. utilized time data collected from limestone underground mine using tablet computers Bluetooth beacons 15 weeks. The were used train an ML truck cycle time, support vector regression with particle swarm optimization (PSO–SVM) demonstrated best performance. PSO–SVM accurately predicted mean absolute error (MAE) 2.79 min, squared (MSE) 14.29 min2, root square (RMSE) 3.79 coefficient determination (R2) 0.68. output was linked DES production each truck, section, period. Verification its ability simulate system in area comparing logs results. study’s offers new method predicting determining optimal equipment combination workplace, thus systems.
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ژورنال
عنوان ژورنال: Minerals
سال: 2023
ISSN: ['2075-163X']
DOI: https://doi.org/10.3390/min13060830