RESUMO

The development of real-time monitoring sensors for pyro-metallurgical processes is an analytical challenge, mainly due to adverse environmental conditions, high spectral interferences and multiphase (molten and gas) reactions. This work demonstrates the suitability of stand-off LIBS (ST-LIBS) for real time monitoring of the desulfurization of blister copper which is carried out in molten phase. Here sulfur is removed by the formation of SO2 by supplying oxygen in molten phase. Using ST-LIBS the relative emission intensities of Cu(I) at 351.06 nm, O at 777.34 nm and S at 921.29 nm in both molten and gaseous phase were considered simultaneously during the process. This was possible only by the use high energy laser pulse over up to 270 mJ per pulse. In the case of copper, the selection of emission lines was assessed considering non-linear behavior, which is caused by self-absorption. For the first time, real time determination of sulfur in ppm range is reported by ST-LIBS using low sensitive lines from the NIR region. These results were validated with differential optical absorption spectroscopy (DOAS) as gold standard method. The analytical information obtained by LIBS can precisely determine the critical end-point of the desulfurization where the removal of sulfur is finished, and copper started to oxidize.

Assuntos

Vesícula , Cobre , Humanos , Lasers , Análise Espectral , Enxofre

RESUMO

Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for the analysis of rocks and mineral samples. Artificial neural networks (ANNs) have been used to estimate the concentration of minerals in samples from LIBS spectra. These spectra are very high dimensional data, and it is known that only specific wavelengths have information on the atomic and molecular features of the sample under investigation. This work presents a systematic methodology based on the Akaike information criterion (AIC) for selecting the wavelengths of LIBS spectra as well as the ANN model complexity, by combining prior knowledge and variable selection algorithms. Several variable selection algorithms are compared within the proposed methodology, namely KBest, a least absolute shrinkage and selection operator (LASSO) regularization, principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS). As an illustrative example, the estimation of copper, iron and arsenic concentrations in pelletized mineral samples is performed. A dataset of LIBS emission spectra with 12 287 wavelengths in the range of 185-1049 nm obtained from 131 samples of copper concentrates is used for regression analysis. An ANN is then trained considering the selected reduced wavelength data. The results are satisfactory using LASSO and CARS algorithms along with prior knowledge, showing that the proposed methodology is very effective for selecting wavelengths and model complexity in quantitative analyses based on ANNs and LIBS.