Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes
No hay miniatura disponible
Fecha
0025
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Multidisciplinary Digital Publishing Institute (MDPI)
Nombre de Curso
Licencia CC
Licencia CC
Resumen
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. © 2025 by the authors.
Notas
INDEXACION SCOPUS
Palabras clave
artificial neural networks, combustion, explainable AI, machine learning, metallurgy, optical measurements, optical sensors, optical signal detection, pyrometallurgy, spectroscopy
Citación
DOI
10.3390/bdcc9050130