Toward the Modeling of the Oxidation of Small Metal and Metalloid Molecules

et al. 2018

Sensors

In this paper, we report on spectral features emitted by a reaction shaft occurring in flash smelting of copper concentrates containing sulfide copper minerals such as chalcopyrite (CuFeS2), bornite (Cu5FeS4) and pyrite (FeS2). Different combustion conditions are addressed, such as sulfur-copper ratio and oxygen excess. Temperature and spectral emissivity features are estimated for each case by using the two wavelength method and radiometric models. The most relevant results have shown an increasing intensity behavior for higher sulfur-copper ratios and oxygen contents, where emissivity is almost constant along the visible spectrum range for all cases, which validates the gray body assumption. CuO and FeO emission line features along the visible spectrum appear to be a sensing alternative for describing the combustion reactions.

Section: Copper Concentrates Spectral Measurementssupporting

confidence: 92%

Flash Smelting Copper Concentrates Spectral Emission Measurements

Arias

Torres

et al. 2018

Sensors

“…Furthermore, some authors reported FeO emission in meteoric ablation measurements and a terrestrial night airglow spectrum [8,9] as well as in experiments with induced emission on steel surfaces [10]. On the other hand, other authors reported spectral features at 605 nm and 615 nm as copper oxide (CuO) [11,12], and recently, Knapp et al [13] proposed a model of this diatomic molecule. We were unable to find reports related with Fe 3 O 4 spectral emissions in the VIS-NIR spectral regions for combustion processes.…”

Section: Introductionmentioning

confidence: 97%

On the Detection of Spectral Emissions of Iron Oxides in Combustion Experiments of Pyrite Concentrates

Torres

Parra

et al. 2020

Sensors

In this paper, we report on the spectral detection of wustite, Fe(II) oxide (FeO), and magnetite, Fe(II, III) oxide (Fe3O4), molecular emissions during the combustion of pyrite (FeS2), in a laboratory-scale furnace operating at high temperatures. These species are typically generated by reactions occurring during the combustion (oxidation) of this iron sulfide mineral. Two detection schemes are addressed: the first consisting of measurements with a built-in developed spectrometer with a high sensitivity and a high spectral resolution. The second one consisting of spectra measured with a low spectral resolution and a low sensitivity commercial spectrometer, but enhanced and analyzed with post signal processing and multivariate data analysis such as principal component analysis (PCA) and a multivariate curve resolution—the alternating least squares method (MCR-ALS). A non-linear model is also proposed to reconstruct spectral signals measured during pyrite combustion. Different combustion conditions were studied to evaluate the capacity of the detection schemes to follow the spectral emissions of iron oxides. The results show a direct correlation between FeO and Fe3O4 spectral features intensity, and non-linear relations with key combustion variables such as flame temperature, and the combusted sulfide mineral particle size.

“…These peaks are also observed in the loadings obtained with PCA using only pyrite spectra. For chalcopyrite spectra, loadings depict peaks at 606 and 616 nm (Figure 6b), and they are associated with the presence of copper oxides [32]. Note that loading vectors only give an idea of the wavelengths' contribution to spectral data variance and that in any case, their amplitude can be interpreted as a relative concentration in the original spectra; however, they can be seen as a first approach to elucidate the structure of spectral patterns hidden in the data because of their weak emissions.…”

Section: Resultsmentioning

confidence: 99%

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

Díaz

Balladares

et al. 2019

Metals

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.