4.5 Article

Application of Artificial Neural Network for the Prediction of Copper Ore Grade

Journal

MINERALS
Volume 13, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/min13050658

Keywords

ore prediction; artificial neural network; feature importance

Ask authors/readers for more resources

Accurate prediction of ore grade is crucial in various mining activities. Conventional methods often fail to capture the complexity of orebodies, resulting in incorrect estimations and costly decisions. In this study, an artificial neural network (ANN) model was proposed and evaluated using performance metrics such as MAE, MSE, RMSE, R, and R-2. The ANN model outperformed traditional machine learning methods with high accuracy. The feature importance analysis showed that lithology had the highest influence on grade prediction. This approach holds promise for ore grade estimation.
Precise prediction of ore grade is essential in feasibility studies, mine planning, open-pit and underground optimization, and ore grade control. Conventional methods, such as geometric and geostatistical methods, are the most popular techniques for mineral resource estimation but fail to capture the complexity of orebodies. Due to this limitation, grades are incorrectly estimated, leading to inaccurate mine plans and costly financial decisions. Here, we propose an ore grade prediction method using an artificial neural network (ANN). We collected 14,294 datasets from the Jaguar mine in Western Australia. The proposed model was developed by incorporating lithology, alteration, eastings, northwards, altitude, dip, and azimuth to predict the grade, and the performance evaluation metrics were measured based on the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), correlation coefficient, R, and coefficient of determination (R-2). The proposed ANN model outperformed classic machine learning methods with R-2, R, MAE, MSE, and RMSE of 0.584, 0.765, 0.0018, 0.0016, and 0.041, respectively. The Shapley technique was used to evaluate the feature importance of the input variables for the grade prediction. Lithology demonstrated the highest influence on ore prediction, whereas eastings had the least impact on output. The proposed approach is promising for ore model prediction.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available