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Mineralogical Analysis of Solid-Sample Flame Emission Spectra by Machine Learning
Adam R Bernicky1, Boyd Davis2, Milen Kadiyski3
1Department of Chemistry, Queen's University, 90 Bader Lane, Kingston, Ontario K7L 3N6, Canada.
Analytical Chemistry
|November 22, 2024
Summary
A novel artificial neural network (ANN) accurately analyzes copper ore samples using flame optical emission spectroscopy (OES). This advanced method precisely quantifies elemental content and identifies minerals, outperforming traditional models.
Area of Science:
- Analytical Chemistry
- Materials Science
- Artificial Intelligence
Background:
- Pyrometallurgical copper smelters analyze solid ore samples.
- Accurate elemental and mineralogical analysis is crucial for process optimization.
- Traditional spectroscopic analysis methods can be complex and time-consuming.
Purpose of the Study:
- To develop and validate an artificial neural network (ANN) for analyzing solid preconcentrated ore samples.
- To improve the accuracy and efficiency of elemental and mineral identification in copper ore analysis.
- To compare the performance of the ANN against traditional nonlinear partial least-squares (PLS) models.
Main Methods:
- Utilized a specialized flame optical emission spectroscopy (OES) system for sample analysis.
- Developed and optimized an artificial neural network (ANN) with 10 hidden layers and 40 nodes per layer.
- Categorized over 8500 complex spectra using the trained ANN.
Main Results:
- The ANN quantified elemental content with accuracy better than 1.5 mass%.
- The ANN identified prevalent minerals with accuracy better than 2.5 mass%.
- Flame temperature was determined with uncertainty < 3 K, and particle size within 2 μm.
- The ANN significantly outperformed a nonlinear partial least-squares fit model.
Conclusions:
- The developed ANN provides a superior method for the quantitative analysis of copper ore samples.
- This AI-driven approach enhances the precision of elemental and mineralogical characterization in pyrometallurgy.
- The study demonstrates the potential of advanced machine learning techniques in industrial chemical analysis.
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