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 10 hidden layers with 40 nodes per layer. The ANN was able to quantify the elemental content of all samples to within better than 1.5 mass% and was able to identify the prevalent minerals to within better than 2.5 mass%. The flame temperature was obtained with an uncertainty of σ < 3 K and the particle size to within 2 μm. The results are found to be superior to those obtained to a nonlinear partial least-squares fit model, which is equivalent to an ANN having no hidden layers.