Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes

Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes

Accurate classification of sulfide minerals during combustion is essential for optimizing pyrometallurgical processes such as flash smelting, where efficient combustion impacts resource utilization, energy efficiency, and emission control. This study presents a deep learning-based approach for class...

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Bibliographic Details
Main Authors: Carlos Toro, Walter Díaz, Gonzalo Reyes, Miguel Peña, Nicolás Caselli, Carla Taramasco, Pablo Ormeño-Arriagada, Eduardo Balladares
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Big Data and Cognitive Computing
Subjects:
artificial neural networks
metallurgy
explainable AI
optical measurements
optical sensors
spectroscopy
Online Access:https://www.mdpi.com/2504-2289/9/5/130
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Summary:Accurate classification of sulfide minerals during combustion is essential for optimizing pyrometallurgical processes such as flash smelting, where efficient combustion impacts resource utilization, energy efficiency, and emission control. This study presents a deep learning-based approach for classifying visible and near-infrared (VIS-NIR) emission spectra from the combustion of high-grade sulfide minerals. A one-dimensional convolutional neural network (1D-CNN) was developed and trained on experimentally acquired spectral data, achieving a balanced accuracy score of 99.0% in a test set. The optimized deep learning model outperformed conventional machine learning methods, highlighting the effectiveness of deep learning for spectral analysis in high-temperature environments. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to enhance model interpretability and identify key spectral regions contributing to classification decisions. The results demonstrated that the model successfully distinguished spectral features associated with different mineral species, offering insights into combustion dynamics. These findings support the potential integration of deep learning for real-time spectral monitoring in industrial flash smelting operations, thereby enabling more precise process control and decision-making.
ISSN:2504-2289

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