Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems

Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection

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.

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project

The aim of the study was to assess the predictive potential of mid-infrared (MIR) spectral response in the estimation of 60 soil properties. It is important to know the accuracy limitations in estimating various soil characteristics using various models in conditions of high spatial variability of the environment. To fully assess this potential, three types of algorithms were used in modeling, i.e., partial least squares (PLSR), one-dimensional convolutional neural network (1DCNN), and generalized regression neural network (GRNN). The research used data from 19 sub-Saharan African countries collected as part of the Africa Soil Information Service (AfSIS) Phase I project. The repositories provide 18,250 MIR reflectance recordings and nearly two thousand analytical data records from the determination of many soil properties by reference methods. The modeled subset of these properties included texture (three variables), bulk density, moisture content at soil water characteristic curves (SWCC, 4 variables), total and organic C and total N content (3 variables), total elemental content (32 variables), elemental content in bioavailable forms (12 variables), electrical conductivity, exchangeable acidity, exchangeable bases, pH, and phosphorus sorption index. It is not possible to indicate a universal optimal prediction model for all soil variables. The best prediction results are provided by all regression models for total and organic C, total Fe, total Al and bioavailable Al content, and pH. For bulk density, total N and total K content satisfactory results are provided by specific model type. Many other properties, i.e., texture, SWCC, total Ga, Rb, Na, Ca, Cu, Pb, Hg content, and bioavailable Ca and K content, can be predicted with accuracies sufficient for some less demanding tasks.

Analysis of Particle Size Distribution of Coke on Blast Furnace Belt Using Object Detection

Particle size distribution is an important parameter of metallurgical coke for use in blast furnaces. It is usually analyzed by traditional sieving methods, which cause delays and require maintenance. In this paper, a coke particle detection model was developed using a deep learning-based object detection algorithm (YOLOv3). The results were used to estimate the particle size distribution by a statistical method. Images of coke on the main conveyor belt of a blast furnace were acquired for model training and testing, and the particle size distribution determined by sieving was used for verification of the results. The experiment results show that the particle detection model is fast and has a high accuracy; the absolute error of the particle size distribution between the detection method and the sieving method was less than 5%. The detection method provides a new approach for fast analysis of particle size distributions from images and holds promise for a future online application in the plant.

Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition

A multitude of applications in engineering, ore processing, mineral exploration, and environmental science require grain recognition and the counting of minerals. Typically, this task is performed manually with the drawback of monopolizing both time and resources. Moreover, it requires highly trained personnel with a wealth of knowledge and equipment, such as scanning electron microscopes and optical microscopes. Advances in machine learning and deep learning make it possible to envision the automation of many complex tasks in various fields of science at an accuracy equal to human performance, thereby, avoiding placing human resources into tedious and repetitive tasks, improving time efficiency, and lowering costs. Here, we develop deep-learning algorithms to automate the recognition of minerals directly from the grains captured from optical microscopes. Building upon our previous work and applying state-of-the-art technology, we modify a superpixel segmentation method to prepare data for the deep-learning algorithms. We compare two residual network architectures (ResNet 1 and ResNet 2) for the classification and identification processes. We achieve a validation accuracy of 90.5% using the ResNet 2 architecture with 47 layers. Our approach produces an effective application of deep learning to automate mineral recognition and counting from grains while also achieving a better recognition rate than reported thus far in the literature for this process and other well-known, deep-learning-based models, including AlexNet, GoogleNet, and LeNet.

Introduction to the Special Issue “Advances in Computational Intelligence Applications in the Mining Industry”

This is an exciting time for the mining industry, as it is on the cusp of a change in efficiency as it gets better at leveraging data [...]