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Open AccessArticle

Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend

1
Metallurgical Engineering Department, Universidad de Concepción, Concepción CCP 4070386, Chile
2
Dirección de Investigación, Universidad Tecnológica de Chile INACAP, Huechuraba, Avenida el Condor 720, Ciudad Empresarial, Huechuraba 8580000, Chile
3
Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Lientur 1457, Concepción 4080871, Chile
*
Author to whom correspondence should be addressed.
Metals 2019, 9(9), 1017; https://doi.org/10.3390/met9091017
Received: 29 August 2019 / Revised: 15 September 2019 / Accepted: 16 September 2019 / Published: 19 September 2019
(This article belongs to the Special Issue Advances in Pyrometallurgy)
The pyrometallurgical processes for primary copper production have only off-line and time-demanding analytical techniques to characterize the in and out streams of the smelting and converting steps. Since these processes are highly exothermic, relevant process information could potentially be obtained from the visible and near-infrared radiation emitted to the environment. In this work, we apply spectral sensing and multivariate data analysis methodologies to identify and classify copper and iron sulfide minerals present in the blend from spectra measured during their combustion in a laboratory drop-tube setup, in which chemical reactions that take place in flash smelting furnaces can be reproduced. Controlled combustion experiments were conducted with two industrial concentrates and with high-grade mineral species as well, with a focus on pyrite and chalcopyrite. Exploratory analysis by means of Principal Component Analysis (PCA) applied on the spectral data depicted high correlation features among species with similar elemental compositions. Classification algorithms were tested on the spectral data, and a classification accuracy of 95.3% with a support vector machine (SVM) algorithm with a Gaussian kernel was achieved. The results obtained by the described procedures are shown to be very promising as a first step in the development of a predictive and analytical tool in search of fitting the current need for real-time control of pyrometallurgical processes. View Full-Text
Keywords: copper concentrate; pyrometallurgy; flash smelting; combustion; classification; spectroscopy; PCA; SIMCA; PLS-DA; k-NN; support vector machines copper concentrate; pyrometallurgy; flash smelting; combustion; classification; spectroscopy; PCA; SIMCA; PLS-DA; k-NN; support vector machines
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Díaz, W.; Toro, C.; Balladares, E.; Parra, V.; Coelho, P.; Reyes, G.; Parra, R. Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend. Metals 2019, 9, 1017.

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