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.

A nonintrusive low-cost sensor based on silicon photodiode detectors has been designed to analyze the formation and behavior of excited ${\mathrm{CH}}^{*}$ and ${{\mathrm{C}}_2}^{*}$ 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 ${{\mathrm{C}}_2}^{*}$ and ${\mathrm{CH}}^{*}$ 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.

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.

Despite the widespread and extended use of sensor systems in different industries, there is an important gap to reach equivalent conditions in pyrometallurgical processes for primary production. In the specific case of copper pyrometallurgy, the situation is particularly challenging to incorporate the Industry 4.0 concept for the optimization of their operations. Currently, only two instruments can be identified at the commercial level: the Noranda pyrometer and the Online Production Control (OPC) system. The iron-making and steelmaking industries, however, present an advanced level of control based on monitoring and sensing networks throughout the entire process. This reality has served as a basis for developing a series of solutions based on radiometric sensors for copper pyrometallurgy. We present two types of sensing concept. The first one is applied to smelting and converting reactors based on the measurements of the radiation of the oxidation of different copper and iron sulfides. The second one considers hyperspectral imaging of molten phases flow during operations. The idea of this proposal is to transfer some commercial sensing technologies already in use in the steelmaking industry. In this article, the fundamentals of the sensor design, proofs of concept, and the initial industrial validations are reviewed. Finally, a discussion on the contribution of this knowledge and development opportunities within the framework of Industry 4.0 are addressed.