Automated Algorithm for Removing Continuous Flame Spectrum Based On Sampled Linear Bases
In this paper, an automated algorithm to estimate and remove the continuous baseline from measured spectra containing both continuous and discontinuous bands is proposed. The algorithm uses previous information contained in a Continuous Database Spectra (CDBS) to obtain a linear basis, with minimum number of sampled vectors, capable of representing a continuous baseline. The proposed algorithm was tested by using a CDBS of flame spectra where Principal Components Analysis and Non-negative Matrix Factorization were used to obtain linear bases. Thus, the radical emissions of natural gas, oil and bio-oil flames spectra at different combustion conditions were obtained. In order to validate the performance in the baseline estimation process, the Goodness-of-fit Coefficient and the Root Mean-squared Error quality metrics were evaluated between the estimated and the real spectra in absence of discontinuous emission. The achieved results make the proposed method a key element in the development of automatic monitoring processes strategies involving discontinuous spectral bands.
[1] C. Martins, J. Carvalho, and M. Ferreira, "CH and C2 radicals characterization
in natural gas turbulent diffusion flames," Journal of the Brazilian
Society of Mechanical Sciences and Engineering, vol. 27, no. 2, pp. 110-
118, 2005.
[2] N. Docquier, S. Belhalfaoui, F. Lacas, N. Darabiha, and C. Rolon, "Experimental
and numerical study of chemiluminescence in methane/air
high-pressure flames for active control applications," Proceeding of the
combustion institute, vol. 28, pp. 1765-1774, 2000.
[3] G. Zizak, "Flame emission spectroscopy: Fundamentals and applications,"
Instituto per la Tecnologia dei Materiali e dei Processi Energitici,
Tech. Rep., 2000, iCS training course on laser diagnostics of combustion
processes, NILES, University of Cairo, Egypt.
[4] L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, "On the
spectral band measurements for combustion monitoring," Combustion
and Flame, vol. 158, pp. 423-433, 2011.
[5] G. Schulze, A. Jirasek, M. Yu, A. Lim, R. Turner, and M. Blades,
"Investigation of selected baseline removal techniques as candidates for
automated implementation," Appl. Spectrosc., vol. 59, no. 5, pp. 545-
574, 2005.
[6] A. G. Gaydon and H. G. Wolfhard, The spectroscopy of flames, 1st ed.
Chapman and Hall LTD., 1957.
[7] P. Ngendakumana, B. Zuo, and E. Winandy, "A spectroscopic study of
flames for a pollutant formation regulation in a real oil boiler," Proc. 2th
Int-l Conf. on Tech. and Combustion for a Clean Environment, 1993.
[8] Y. Kojima, Y. Ikeda, and T. Nakajima, "Basic aspect of oh(a), ch(a) and
c2(d) chemiluminiscence in the reaction zone of laminar methane-air
premixed flames," Combustion and Flame, vol. 140, pp. 34-45, 2004.
[9] Y. Hardalupas, M. Orain, C. Panoutsos, A. Taylor, J. Olofsson,
H. Seyfried, M. Richter, J. Hult, M. Alden, F. Hermann, and J. Klingmann,
"Chemiluminescence sensor for local equivalence ratio of reacting
mixtures of fuel and air (flameseek)," Applied Thermal Engineering,
vol. 24, pp. 1619-1632, 2004.
[10] L. Shao and P. Griffiths, "Automatic baseline correction by wavelet transform
for quantitative open-path fourier transform infrared spectroscopy,"
Environ. Sci. Technol., vol. 41, no. 20, pp. 7054-7059, 2007.
[11] M. Lopez, J. Hern'andez, E. Valero, and J. Romero, "Selecting algorithms,
sensors, and linear bases for optimum spectral recovery of
skylight," J. Opt. Soc. Am., vol. 24, no. 4, pp. 942-956, 2007.
[12] P. Courrieu, "Fast computation of moore-penrose inverse matrices,"
Neural Information Processing, vol. 8, no. 2, pp. 25-29, 2005.
[13] F. Imai, M. Rosen, and R. Berns, "Comparative study of metrics for
spectral match quality," CGIV First European Conference on Colour
Graphics, Imaging, and Vision, pp. 492-496, 2002.
[14] M. Lopez, J. Hern'andez, E. Valero, and J. Nieves, "Colorimetric and
spectral combined metric for the optimization of multispectral systems,"
Proceeding of the 10th Congress of the International Colour Association
(AIC05), pp. 1685-1688, 2005.
[15] J. L. Nieves, E. M. Valero, J. Hernandez, and J. Romero, "Recovering
fluorescent spectra with an rgb digital camera and color filters using
different matrix factorizations," Appl. Opt., vol. 46, no. 19, pp. 4144-
4154, 2007.
[1] C. Martins, J. Carvalho, and M. Ferreira, "CH and C2 radicals characterization
in natural gas turbulent diffusion flames," Journal of the Brazilian
Society of Mechanical Sciences and Engineering, vol. 27, no. 2, pp. 110-
118, 2005.
[2] N. Docquier, S. Belhalfaoui, F. Lacas, N. Darabiha, and C. Rolon, "Experimental
and numerical study of chemiluminescence in methane/air
high-pressure flames for active control applications," Proceeding of the
combustion institute, vol. 28, pp. 1765-1774, 2000.
[3] G. Zizak, "Flame emission spectroscopy: Fundamentals and applications,"
Instituto per la Tecnologia dei Materiali e dei Processi Energitici,
Tech. Rep., 2000, iCS training course on laser diagnostics of combustion
processes, NILES, University of Cairo, Egypt.
[4] L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, "On the
spectral band measurements for combustion monitoring," Combustion
and Flame, vol. 158, pp. 423-433, 2011.
[5] G. Schulze, A. Jirasek, M. Yu, A. Lim, R. Turner, and M. Blades,
"Investigation of selected baseline removal techniques as candidates for
automated implementation," Appl. Spectrosc., vol. 59, no. 5, pp. 545-
574, 2005.
[6] A. G. Gaydon and H. G. Wolfhard, The spectroscopy of flames, 1st ed.
Chapman and Hall LTD., 1957.
[7] P. Ngendakumana, B. Zuo, and E. Winandy, "A spectroscopic study of
flames for a pollutant formation regulation in a real oil boiler," Proc. 2th
Int-l Conf. on Tech. and Combustion for a Clean Environment, 1993.
[8] Y. Kojima, Y. Ikeda, and T. Nakajima, "Basic aspect of oh(a), ch(a) and
c2(d) chemiluminiscence in the reaction zone of laminar methane-air
premixed flames," Combustion and Flame, vol. 140, pp. 34-45, 2004.
[9] Y. Hardalupas, M. Orain, C. Panoutsos, A. Taylor, J. Olofsson,
H. Seyfried, M. Richter, J. Hult, M. Alden, F. Hermann, and J. Klingmann,
"Chemiluminescence sensor for local equivalence ratio of reacting
mixtures of fuel and air (flameseek)," Applied Thermal Engineering,
vol. 24, pp. 1619-1632, 2004.
[10] L. Shao and P. Griffiths, "Automatic baseline correction by wavelet transform
for quantitative open-path fourier transform infrared spectroscopy,"
Environ. Sci. Technol., vol. 41, no. 20, pp. 7054-7059, 2007.
[11] M. Lopez, J. Hern'andez, E. Valero, and J. Romero, "Selecting algorithms,
sensors, and linear bases for optimum spectral recovery of
skylight," J. Opt. Soc. Am., vol. 24, no. 4, pp. 942-956, 2007.
[12] P. Courrieu, "Fast computation of moore-penrose inverse matrices,"
Neural Information Processing, vol. 8, no. 2, pp. 25-29, 2005.
[13] F. Imai, M. Rosen, and R. Berns, "Comparative study of metrics for
spectral match quality," CGIV First European Conference on Colour
Graphics, Imaging, and Vision, pp. 492-496, 2002.
[14] M. Lopez, J. Hern'andez, E. Valero, and J. Nieves, "Colorimetric and
spectral combined metric for the optimization of multispectral systems,"
Proceeding of the 10th Congress of the International Colour Association
(AIC05), pp. 1685-1688, 2005.
[15] J. L. Nieves, E. M. Valero, J. Hernandez, and J. Romero, "Recovering
fluorescent spectra with an rgb digital camera and color filters using
different matrix factorizations," Appl. Opt., vol. 46, no. 19, pp. 4144-
4154, 2007.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:55655", author = "Luis Arias and Jorge E. Pezoa and Daniel Sbárbaro", title = "Automated Algorithm for Removing Continuous Flame Spectrum Based On Sampled Linear Bases", abstract = "In this paper, an automated algorithm to estimate and remove the continuous baseline from measured spectra containing both continuous and discontinuous bands is proposed. The algorithm uses previous information contained in a Continuous Database Spectra (CDBS) to obtain a linear basis, with minimum number of sampled vectors, capable of representing a continuous baseline. The proposed algorithm was tested by using a CDBS of flame spectra where Principal Components Analysis and Non-negative Matrix Factorization were used to obtain linear bases. Thus, the radical emissions of natural gas, oil and bio-oil flames spectra at different combustion conditions were obtained. In order to validate the performance in the baseline estimation process, the Goodness-of-fit Coefficient and the Root Mean-squared Error quality metrics were evaluated between the estimated and the real spectra in absence of discontinuous emission. The achieved results make the proposed method a key element in the development of automatic monitoring processes strategies involving discontinuous spectral bands.
", keywords = "Flame spectra, removing baseline, recovering spectrum.", volume = "6", number = "3", pages = "614-8", }
