Mineralogical Analysis of Solid-Sample Flame Emission Spectra by Machine Learning

Solid preconcentrated ore samples used in pyrometallurgical copper smelters are analyzed by flame emission spectroscopy using a specialized flame optical emission spectroscopy (OES), system. Over 8500 complex spectra are categorized using an artificial neural network (ANN) that was optimized to have...

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Veröffentlicht in:	Analytical chemistry (Washington) 2024-12, Vol.96 (49), p.19330-19338
Hauptverfasser:	Bernicky, Adam R., Davis, Boyd, Kadiyski, Milen, Loock, Hans-Peter
Format:	Artikel
Sprache:	eng
Schlagworte:	analytical chemistry Artificial neural networks Copper ores Emission analysis Emission spectra Emission spectroscopy Flame temperature least squares Machine learning Neural networks Optical emission spectroscopy particle size Smelters Spectroscopic analysis Spectroscopy temperature uncertainty
Online-Zugang:	Volltext
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