In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%.

The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in open pit mining. While various parameters can affect rock fragmentation, this study considers ten among them (i.e., spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) to train and test the models. However, due to a weak correlation with rock fragmentation, drill diameter, average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features including the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. Machine learning models (random forest regression, support vector regression, and XG boost), as well as a deep learning model (neural network regression), are applied to develop a practical way that can optimize the prediction of fragmentation. This study employs performance measures such as R-squared, RMSE, MSE, MAPE, and MAE. The optimization of the model revealed promising results, indicating that the architecture 5-64-32-16-1 exhibits strong performance. Specifically, the model achieved mean squared error (MSE) values of 41.32 and 28.59 on the training and test datasets, respectively. The R² value for both training and test is 0.83. RFR is also performing well compared to SVR and XG boost with MSE values of 12.37 and 9.89 on training and testing data, respectively.in both sets, the R² value is 94%. Based on permutation importance and shapely plot values, it is observed that the powder factor has the highest impact, while the burden has the lowest impact on fragmentation.

Mineral grade estimation is a vital phase in mine planning and design, as well as in the mining project’s economic assessment. In mining, commonly accepted methods of ore grade estimation include geometrical approaches and geostatistical techniques such as kriging, which effectively capture the spatial grade variation within a deposit. The application of machine-learning (ML) techniques has been explored in the estimation of mineral resources, where complex correlations need to be captured. In this paper, the authors developed four machine-learning regression models, i.e., support vector regression (SVR), random forest regression (RFR), k-nearest neighbour (KNN) regression, and extreme gradient boost (XGBoost) regression, using a geological database to predict the grade in an Indian iron ore deposit. When compared with ordinary kriging (R² = 0.74; RMSE = 2.09), the RFR (R² = 0.74; RMSE = 2.06), XGBoost (R² = 0.73; RMSE = 2.12), and KNN (R² = 0.73; RMSE = 2.11) regression models produced similar results. The block model predictions generated using the RFR, XGBoost, and KNN models show comparable accuracy and spatial trends to those of ordinary kriging, whereas SVR was identified as less effective. When integrated with geological methods, these models demonstrate significant potential for enhancing and optimizing mine planning and design processes in similar iron ore deposits.

Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other wavelength selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy showed 91.9% or more when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities.

Heavy minerals, such as Rutile, Ilmenite and Zircon, and other essential trace elements are important in modern technology development. The integration of hyperspectral imaging and artificial intelligence presents a promising approach for the accurate identification of heavy minerals, especially Rare Earth Element (REE)–bearing phases such as Monazite. This study evaluates three AI classifiers, Support Vector Machine (SVM), Neural Networks (NNs) and Convolutional Neural Networks (CNNs), for their performance in classifying ten different minerals distributed across six grain size groups ranging from 125 μm to over 300 μm. The analysis focuses on how grain size affects spectral reflectance and classification accuracy. Among the tested models, SVM consistently outperformed NN and CNN, achieving the highest precision, recall and spectral similarity, particularly within the 150–300 μm grain size range. CNN showed the lowest performance and frequently misclassified spectrally similar minerals, such as Zircon and Rutile, likely due to its 1D architecture and limited spatial representation. Monazite, notable for its strong Nd³⁺ absorption features, was accurately identified across applicable grain sizes, highlighting its reliability for REE detection. Spectral Angle Mapper (SAM) analysis confirmed that SVM and NN maintained better spectral similarity than CNN. In general, the results highlight the significant influence of grain size, spectral similarity and dataset size on classification accuracy and the overall effectiveness of AI models in hyperspectral mineral analysis.