Experimental verification of gas spectra calculated for high temperatures using the HITRAN/HITEMP database

Fleckl, Thomas; Jäger, Helmut; Obernberger, Ingwald

doi:10.1088/0022-3727/35/23/315

Cited by 33 publications

(27 citation statements)

References 8 publications

(11 reference statements)

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“…The differences confirm the correctness of the spectral model. A similar approach using HITRAN and ILS was also confirmed experimentally at temperatures of up to 1100 °C [31].…”

Section: Modeling Of Gas Spectramentioning

confidence: 53%

A Local Model and Calibration Set Ensemble Strategy for Open-Path FTIR Gas Measurement with Varying Temperature

Cięszczyk¹

2013

Metrology and Measurement Systems

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Open-Path Fourier Transform Infrared OP-FTIR spectrometers are commonly used for the measurement of atmospheric pollutants and of gases in industrial processes. Spectral interpretation for the determination of gas concentrations is based on the HITRAN database line-by-line modeling method. This article describes algorithms used to model gas spectra and to determine gas concentration under variable temperatures. Integration of individual rotational lines has been used to reduce the impact of spectrometer functions on the comparison of both measured and synthetic modeled spectra. Carbon monoxide was used as an example. A new algorithm for gas concentration retrieval consisting of two ensemble methods is proposed. The first method uses an ensemble of local models based on linear and non-linear PLS (partial least square) regression algorithms, while the second is an ensemble of a calibration set built for different temperatures. It is possible to combine these methods to decrease the number of regression models in the first ensemble. These individual models are appropriate for specific measurement conditions specified by the ensemble of the calibration set. Model selection is based on comparison of gas spectra with values determined from each local model.

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“…The differences confirm the correctness of the spectral model. A similar approach using HITRAN and ILS was also confirmed experimentally at temperatures of up to 1100 °C [31].…”

Section: Modeling Of Gas Spectramentioning

confidence: 53%

A Local Model and Calibration Set Ensemble Strategy for Open-Path FTIR Gas Measurement with Varying Temperature

Cięszczyk¹

2013

Metrology and Measurement Systems

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“…The same is true about the ability to produce these conditions in the laboratory. Such measurements require special equipment [47] and can be used to confirm the accuracy of synthetic spectra modelling [48]. The CO concentration was determined using measured spectrum [49] for the PLS 370-470 K model.…”

Section: 3mentioning

confidence: 99%

Influence of temperature on synthetic data-based calibration models for low resolution open-path FTIR spectroscopy

Cięszczyk¹

2014

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. This article presents the problem of determining the gas concentration under conditions of changing temperatures. The solution to this problem is based on spectral measurements using chemometric models. Such problems arise when measuring gases during various industrial processes, mainly the pyrolysis process. Chemometric models are built using synthetic calibration data. Line-by-line and statistical models were applied for spectra modelling. The effect of temperature on extracting the gas concentration from spectra of various resolutions was determined. Next, the global model was built and tested using calibration data for specific temperature ranges. The properties of the linear and nonlinear partial least-squares (PLS) models that were applied to the considered issues were demonstrated. CO and CH4 were used as example gases.

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“…Using the equation of radiative energy transfer, gas spectra can be calculated for specific content and temperature along the observation path [16]. The correctness of modelling methods using databases of spectral parameters of gases was confirmed in experimental conditions [17,18]. Simulation allows to select arbitrary physical conditions of the gas layer (gas plume).…”

Section: Introductionmentioning

confidence: 98%

Inversion of Remote Sensing Data Using Multiple Ratios of Spectral Radiation Intensities and Neural Networks

Cięszczyk

2017

Acta Phys. Pol. A

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The article presents a method for determining the content and temperature on the basis of spectra from remote measurements. The technique uses measurements of a high resolution radiation spectrum that allows the visibility of the individual rotational lines of gases such as CO2, used here in the range of 2470-2495 cm −1 . At the same time a new algorithm is applied of pre-processing the spectrum, involving the use of multiple ratios of intensity at several wavenumbers as input to an inverse model based on neural networks. Due to it, the dimensionality of the input can significantly be reduced. Additionally, the data interpreted do not have to be measured in units of spectral radiance. Thus only the calibration of the sensitivity of the spectrometer at various wavelengths is required. The neural models were constructed on the basis of data from the simulation. The proposed method works with a uniform layer of radiating gas for determining the temperature and CO2 content. For a non-uniform layer it is possible to determine the line-of-sight temperature profile and average gas content. The method can be extended to different spectral ranges and to other gases present in substantial quantities in the exhaust gases of various processes.

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Experimental verification of gas spectra calculated for high temperatures using the HITRAN/HITEMP database

Cited by 33 publications

References 8 publications

A Local Model and Calibration Set Ensemble Strategy for Open-Path FTIR Gas Measurement with Varying Temperature

A Local Model and Calibration Set Ensemble Strategy for Open-Path FTIR Gas Measurement with Varying Temperature

Influence of temperature on synthetic data-based calibration models for low resolution open-path FTIR spectroscopy

Inversion of Remote Sensing Data Using Multiple Ratios of Spectral Radiation Intensities and Neural Networks

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