Data Fusion of Laser-Induced Breakdown Spectroscopy and Spectral Reflectance Techniques for Estimating the Mineralogical Composition of Copper Concentrates
Fecha
2021Autor
Sbarbaro-Hoffer, Daniel Gerónimo
Yáñez Solorza, Jorge Carlos
UNIVERSIDAD DE CONCEPCION
Institución
Resumen
The pyrometallurgical copper industry faces some challenges in terms of the instrumentation
for its processes. In this work, Laser-Induced Breakdown Spectroscopy (LIBS) data will
be studied and combined with Diffuse Reflectance Spectroscopy (DRS) data and also with Hyperspectral
Imaging (HSI) data to characterize the elemental and mineralogical composition in
copper concentrates. This knowledge can be used to develop a sensor that replaces the current
procedure used, which is risky, slow, and generates toxic waste and gaseous emissions.
LIBS spectra are used for elemental characterization of samples, whereas DRS spectra can
be used for molecular or mineral determination. HSI sensors provide a wider range of data for
the sample material. The information from these sources can be fused to obtain a more reliable
characterization. These spectroscopic techniques are high dimensional in terms of features or
wavelengths. In order to process these datasets, it is essential to reduce their dimensionality,
which can be done by using variable selection techniques. In LIBS, the expert selection is
frequently used since there are peaks that are known to be associated with certain elemental
species. For DRS and HSI data, it is less direct how to choose some wavelengths. Thus
some automatic variable selection algorithms can be applied for this task. In this work, two
variable selection methods are proposed for LIBS data. Both methods combine the use of expert
knowledge to select the best wavelengths.
Before fusing LIBS and HSI datasets, DRS is fused with LIBS data using a small dataset.
LIBS and HSI data are finally fused using low-level and mid-level data fusion techniques.
For each regression analysis, artificial neural networks (ANN) were used, which have gained
attention for regression studies due to the flexibility in dealing with large amounts of nonlinear
correlated data. The results show that by using mid-level data fusion, it is possible to outperform
the performance of the individual sources, with root mean squared errors of prediction reductions
ranging from 4% to 70% in the case of LIBS-DRS data fusion, and from 1% to 74% in the case
of LIBS-HSI data fusion.