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.

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 ∼ 0.4 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.

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In this paper, a novel automated algorithm to estimate and remove the continuous baseline from measured flame spectra is proposed. The algorithm estimates the continuous background based on previous information obtained from a learning database of continuous flame spectra. Then, the discontinuous flame emission is calculated by subtracting the estimated continuous baseline from the measured spectrum. The key issue subtending the learning database is that the continuous flame emissions are predominant in the sooty regions, in absence of discontinuous radiation. The proposed algorithm was tested using natural gas and bio-oil flames spectra at different combustion conditions, and the goodness-of-fit coefficient (GFC) quality metric was used to quantify the performance in the estimation process. Additionally, the commonly used first derivative method (FDM) for baseline removing was applied to the same testing spectra in order to compare and to evaluate the proposed technique. The achieved results show that the proposed method is a very attractive tool for designing advanced combustion monitoring strategies of discontinuous emissions.

A nonintrusive low-cost sensor based on silicon photodiode detectors has been designed to analyze the formation and behavior of excited CH(*) and C(2)(*) radicals in the combustion process by sensing the spectral emission of hydrocarbon flames. The sensor was validated by performing two sets of experiments for both nonconfined and confined flames. For a nonconfined oil flame, the sensor responses for the axial intensity were highly correlated with the measurements obtained with a radiometer. For confined gas flames the ratio between the signal corresponding to C(2)(*) and CH(*) was successfully correlated with the CO pollutant emissions and the combustion efficiency. These results give additional insight on how to prevent an incomplete combustion using spectral information. The fast response, the nonintrusive character, and the instantaneous measurement of the needed spectral information makes the proposed optical sensor a key element in the development of advanced control strategies for combustion processes.