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.

In this paper a low-cost, practical pixel-based flame spectrum and temperature estimation system based on flame color images is proposed. A spectral resolution of $\sim 0.4\mathrm{nm}$ is achieved with an optical system formed by a color camera, a linear model, a flame’s spectral training data, and a spectral reconstruction procedure. As a proof of concept, the estimated spectra are compared to local measurements performed with a commercial spectrometer. In order to estimate the absolute flame-temperature maps, two radiometric images at different wavelengths are reconstructed and the two-color pyrometry method is applied. Experiments show errors of about 2.0% over the estimated temperature, making this system a practical tool for flame sensing in combustion-process monitoring.

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.

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.

The project focused on the investigation of new sensing techniques in the copper production industry, specifically in flash smelting of copper concentrate process. In this paper, we report a direct relationship between the visible and near infrared emission spectra in the combustion of copper concentrates, by changing some operating conditions, such as the sulfur-copper and the oxygen ratios provided to the reaction zone. Spectral processing techniques are applied to the measured spectra. The first one aims to separate both, continuous radiation mainly associated to incandescent particles and heating walls, with discontinuous emissions associated to some emitting atoms and molecules. This goal was carried out using airPLS baseline estimation algorithm. The second processing technique aims to find the continuous emission only associated to the combustion of copper concentrate particles, eliminating the background spectra associated with the smelter walls. This goal was carried out by directly measuring walls emission at operating temperature and in absence of flame. The most relevant results show that the estimation of the total radiation associated with each measured spectra is an intrinsic parameter of the process that can provide useful information to the operator that supervises the industrial process. It allows estimate quantitatively the sulfurcopper ratio in order to online monitor the mineral characteristics of the copper concentrate that is entering the process. On the other hand, the approach of a first prototype of the emissivity model for the copper concentrate particles, which validated with measurements, becomes a promising tool that will allow increasing the development of optoelectronic applications around this industry process.

In this research, the spectral detection of copper oxide is reported from different combustion tests of chalcopyrite particles and copper concentrates. Combustion experiments were performed in a bench reactor. In all the tests, the radiation emitted from the sulfide particle reactions was captured in the VIS–NIR range. The obtained spectral data were processed by using the airPLS (adaptive iteratively reweighted penalized least squares) algorithm to remove their baseline, and principal component analysis (PCA) and the multivariate curve resolution method alternate least squares (MCR-ALS) methods were applied to identify the emission lines or spectral bands of copper oxides. The extracted spectral pattern is directly correlated with the emission profile reported in the literature, evidencing the potential of using spectral analysis techniques on copper sulfide combustion spectra.