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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