Removing baseline flame’s spectrum by using advanced recovering spectrum techniques

Arias, Luis; Sbárbaro, Daniel; Torres, Sergio N.

doi:10.1364/ao.51.006111

Cited by 13 publications

(12 citation statements)

References 9 publications

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“…(a) (b) Some algorithms have been proposed to automatically separate the continuous and discontinuous spectral information from the measured flame emission spectra [18]. In this paper, continuous spectral radiation intensities in a wavelength range from 500 nm to 900 nm were extracted from the measured spectral radiation intensities of the MSW flame, and they were then fitted by a fourth-order polynomial.…”

Section: Measurement Principlementioning

confidence: 99%

“…Figure 4 shows a comparison of the original continuous spectral radiation intensities (dotted line) extracted from Figure 3b and the corresponding fitting curves (solid line). In order to evaluate the fitting performance, the goodness-of-fit coefficient (GFC) [18] quality metrics between the fitting I c and the original I c spectral radiation intensities in the absence of discontinuous emission lines were calculated as:…”

Section: Measurement Principlementioning

confidence: 99%

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In Situ Measurement of Alkali Metals in an MSW Incinerator Using a Spontaneous Emission Spectrum

et al. 2017

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This paper presents experimental investigations of the in situ diagnosis of the alkali metals in the municipal solid waste (MSW) flame of an industrial grade incinerator using flame emission spectroscopy. The spectral radiation intensities of the MSW flame were obtained using a spectrometer. A linear polynomial fitting method is proposed to uncouple the continuous spectrum and the characteristic line. Based on spectra processing and a non-gray emissivity model, the flame temperature, emissivity, and intensities of the emission of alkali metals were calculated by means of measuring the spectral radiation intensities of the MSW flame. Experimental results indicate that the MSW flame contains alkali metals, including Na, K, and even Rb, and it demonstrates non-gray characteristics in a wavelength range from 500 nm to 900 nm. Peak intensities of the emission of the alkali metals were found to increase when the primary air was high, and the measured temperature varied in the same way as the primary air. The temperature and peak intensities of the lines of emission of the alkali metals may be used to adjust the primary airflow and to manage the feeding of the MSW to control the alkali metals in the MSW flame. It was found that the peak intensity of the K emission line had a linear relationship with the peak intensity of the Na emission line; this correlation may be attributed to their similar physicochemical characteristics in the MSW. The variation trend of the emissivity of the MSW flame and the oxygen content in the flue gas were almost opposite because the increased oxygen content suppressed soot formation and decreased soot emissivity. These results prove that the flame emission spectroscopy technique is feasible for monitoring combustion in the MSW incinerator in situ.

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Section: Measurement Principlementioning

confidence: 99%

Section: Measurement Principlementioning

confidence: 99%

In Situ Measurement of Alkali Metals in an MSW Incinerator Using a Spontaneous Emission Spectrum

et al. 2017

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“…For the flame emission spectrum, which contains both discontinuous and continuous spectra, it is inappropriate to apply the above measurement principle. Indeed, flame emission spectra can be expressed as the sum of the continuous and discontinuous spectral emissions, as shown in Equation (6) [15].…”

Section: Measurement Principlementioning

confidence: 99%

“…Usually, a continuous spectrum represents low-frequency components and the discontinuous emission is characterized by high-frequency components [14]. The continuous spectrum of a coal-fired flame that is usually applied to calculate temperature provides important information for soot formation [15]. The existence of discontinuous emission spectra may influence the temperature calculation's accuracy.…”

Section: Introductionmentioning

confidence: 99%

Spectrometer-Based Line-of-Sight Temperature Measurements during Alkali-Pulverized Coal Combustion in a Power Station Boiler

Yan

et al. 2017

Energies

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Abstract:A portable spectrometer system that simultaneously measures the temperature, emissivity, and radiation intensity of an alkali metal was used in a 1000 MW coal-fired power plant boiler furnace. A calibrated fiber-optic spectrometer system was applied to obtain the radiation intensity of the flame. A simple method based on polynomial fitting was used to separate the continuous baseline from the measured flame spectra that contained both continuous and discontinuous bands. Nine synthetic spectra that included the baseline, noise, and three simulated discontinuous bands based on a Gaussian function were created to test the accuracy of the separation method. The accuracy of the estimated continuous baseline was evaluated by the goodness-of-fit coefficient quality metric. The results indicated good spectral matching for the selected profiles. The soot emissivity model by Hottel and Broughton was employed to calculate temperature and emissivity. The influence of discontinuous emission spectra on the temperature and emissivity calculations was evaluated. The results showed that the maximum difference of the measurement points of the calculated temperature was only 6 K and that the relative difference in emissivity among the measurement points was less than 5%. In addition, a comparison between the actual intensity of the alkali metal and the calculated temperature indicated that the change in the radiation intensity of the alkali metal followed the trend of the calculated temperature. This study serves as a preliminary investigation for measuring gas-phase alkali metal concentrations in a furnace.

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“…However, conventional algorithms for baseline removal use local spectral information or user predefined structure, 4,5 but typically they do not consider explicit information about previous knowledge of the process. A novel approach for automatic baseline removal was recently proposed, 10,11 where continuous spectral features are estimated from a linear model using entire and sampled basis vectors. These vectors are calculated by performing principal component analysis (PCA) over a learning matrix containing a priori knowledge of only continuous spectral characteristics.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of a Principal Component Analysis-Based and an Artificial Neural Network-Based Method for Baseline Removal

et al. 2016

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This work presents a non-parametric method based on a principal component analysis (PCA) and a parametric one based on artificial neural networks (ANN) to remove continuous baseline features from spectra. The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix. The parametric method, however, uses an ANN to filter out the baseline. Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing 100 synthetic composed spectra at different signal-to-baseline ratio (SBR), signal-to-noise ratio (SNR), and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used. Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity.

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Removing baseline flame’s spectrum by using advanced recovering spectrum techniques

Cited by 13 publications

References 9 publications

In Situ Measurement of Alkali Metals in an MSW Incinerator Using a Spontaneous Emission Spectrum

In Situ Measurement of Alkali Metals in an MSW Incinerator Using a Spontaneous Emission Spectrum

Spectrometer-Based Line-of-Sight Temperature Measurements during Alkali-Pulverized Coal Combustion in a Power Station Boiler

Comparative Analysis of a Principal Component Analysis-Based and an Artificial Neural Network-Based Method for Baseline Removal

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