Flame spectra-temperature estimation based on a color imaging camera and a spectral reconstruction technique

Abstract

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.

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

“…As reported in the literature [2,[4][5][6], spectroscopy and optical techniques are suitable, nondestructive and contactless sensing alternatives to characterize combustion processes. In particular, some authors have reported the FeO spectral emission pattern in the visible (VIS) spectral range as reported by West and Broida in their work [7].…”

Hyperspectral Imaging Retrieval Using MODIS Satellite Sensors Applied to Volcanic Ash Clouds Monitoring

“…Linear basis vectors were calculated by using principal components analysis (PCA). PCA was chosen because it has been previously verified to provide the most accurate spectral matching results, using a few of both the optical channels and principal components, compared to the non-negative matrix factorization (NNMF) and independent component analysis (ICA) reduction methods [46,51]. Thus, PCA was computed over the training spectral data L. Figure 3a depicts the first to fourth principal components, as a function of wavelength λ, where the fourth component and the subsequent basis vectors only contain negligible information.…”

“…where ρ is a k-by-one vector containing the optical sensor pixel response, E is an N-by-one column vector representing the incident spectrum, and Ω is an N-by-k matrix containing the spectral sensitivity vectors of the optical channels (T is the transpose function). In practice, spectral sensitivity vectors in Ω features any optical sensor in a mathematical way and represents the transmittance of the filters of the optical sensor weighted by its quantum efficiency [46,47]. We will consider that the additive noise e could be neglected and the spectral sensitivity vectors in Ω are known.…”

“…We will consider that the additive noise e could be neglected and the spectral sensitivity vectors in Ω are known. To obtain the spectral retrieval model, let us assume that a set of target spectra of high spectral resolution N have been previously measured and stored in a referential matrix L. This matrix is composed of p uncorrelated samples (sorted in rows) and measured with a high spectral resolution spectrometer; then, the dimension of L is p-by-N. Next, we assume that any spectra in the referential dataset L could be approximated by a linear model as follows [46,48]:…”

“…To surpass this limitation, the second approach incorporates the optical sensor spectral sensitivity Ω. In this context, the Maloney-Wandell method provides a proper estimate of G. This method of spectral retrieval was firstly proposed for spectral characterization of human vision [43,50], and in the last few years, its applications have been extended [44,[46][47][48]. Thus, combining Equation 3and Equation 2, the linear relation = Ω T V ρ can be found.…”