Abstract

In this paper, a novel automated algorithm to estimate and remove the continuous baseline from measured flame spectra is proposed. The algorithm estimates the continuous background based on previous information obtained from a learning database of continuous flame spectra. Then, the discontinuous flame emission is calculated by subtracting the estimated continuous baseline from the measured spectrum. The key issue subtending the learning database is that the continuous flame emissions are predominant in the sooty regions, in absence of discontinuous radiation. The proposed algorithm was tested using natural gas and bio-oil flames spectra at different combustion conditions, and the goodness-of-fit coefficient (GFC) quality metric was used to quantify the performance in the estimation process. Additionally, the commonly used first derivative method (FDM) for baseline removing was applied to the same testing spectra in order to compare and to evaluate the proposed technique. The achieved results show that the proposed method is a very attractive tool for designing advanced combustion monitoring strategies of discontinuous emissions.

© 2012 Optical Society of America

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References

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  1. A. G. Gaydon and H. G. Wolfhard, The Spectroscopy of Flames, 1st ed. (Chapman and Hall, 1957).
  2. C. Romero, X. Li, S. Keyvan, and R. Rossow, “Spectrometer-based combustion monitoring for flame stoichiometry and temperature control,” Appl. Thermal Eng. 25, 659–676(2005).
    [CrossRef]
  3. N. Docquier and S. Candel, “Combustion control and sensors: a review,” Prog. Energy Combust. Sci. 28, 107–150 (2002).
    [CrossRef]
  4. L. Meher, D. V. Sagar, and S. Naik, “Technical aspects of biodiesel production by transesterification—a review,” Renewable Sustainable Energy Rev. 10, 248–268 (2006).
  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. 59, 545–574 (2005).
    [CrossRef]
  6. L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, “On the spectral band measurements for combustion monitoring,” Combust. Flame 158, 423–433 (2011).
    [CrossRef]
  7. M. Lopez, J. Hernandez, E. Valero, and J. Romero, “Selecting algorithms, sensors, and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. 24, 942–956 (2007).
    [CrossRef]
  8. L. T. Maloney and B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. 3, 29–33 (1986).
    [CrossRef]
  9. L. Arias and S. Torres, “On the flame spectrum recovery by using a low-spectral resolution sensor,” in Proceedings of the Iberoamerican Congress on Pattern Recognition CIARP (Springer-Verlag, 2011), pp. 256–263.
  10. J. Nieves, E. Valero, J. Hernandez, and J. Romero, “Recovering fluorescent spectra with an RGB digital camera and color filters using different matrix factorizations,” Appl. Opt. 46, 4144–4154 (2007).
    [CrossRef]
  11. M. Lopez, J. Hernandez, and J. Romero, “Developing an optimum computer-designed multispectral system comprising a monochrome CCD camera and a liquid-crystal tunable filter,” Appl. Opt. 47, 4381–4390 (2008).
    [CrossRef]

2011

L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, “On the spectral band measurements for combustion monitoring,” Combust. Flame 158, 423–433 (2011).
[CrossRef]

2008

2007

J. Nieves, E. Valero, J. Hernandez, and J. Romero, “Recovering fluorescent spectra with an RGB digital camera and color filters using different matrix factorizations,” Appl. Opt. 46, 4144–4154 (2007).
[CrossRef]

M. Lopez, J. Hernandez, E. Valero, and J. Romero, “Selecting algorithms, sensors, and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. 24, 942–956 (2007).
[CrossRef]

2006

L. Meher, D. V. Sagar, and S. Naik, “Technical aspects of biodiesel production by transesterification—a review,” Renewable Sustainable Energy Rev. 10, 248–268 (2006).

2005

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. 59, 545–574 (2005).
[CrossRef]

C. Romero, X. Li, S. Keyvan, and R. Rossow, “Spectrometer-based combustion monitoring for flame stoichiometry and temperature control,” Appl. Thermal Eng. 25, 659–676(2005).
[CrossRef]

2002

N. Docquier and S. Candel, “Combustion control and sensors: a review,” Prog. Energy Combust. Sci. 28, 107–150 (2002).
[CrossRef]

1986

L. T. Maloney and B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. 3, 29–33 (1986).
[CrossRef]

Arias, L.

L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, “On the spectral band measurements for combustion monitoring,” Combust. Flame 158, 423–433 (2011).
[CrossRef]

L. Arias and S. Torres, “On the flame spectrum recovery by using a low-spectral resolution sensor,” in Proceedings of the Iberoamerican Congress on Pattern Recognition CIARP (Springer-Verlag, 2011), pp. 256–263.

Blades, M.

Candel, S.

N. Docquier and S. Candel, “Combustion control and sensors: a review,” Prog. Energy Combust. Sci. 28, 107–150 (2002).
[CrossRef]

Docquier, N.

N. Docquier and S. Candel, “Combustion control and sensors: a review,” Prog. Energy Combust. Sci. 28, 107–150 (2002).
[CrossRef]

Gaydon, A. G.

A. G. Gaydon and H. G. Wolfhard, The Spectroscopy of Flames, 1st ed. (Chapman and Hall, 1957).

Hernandez, J.

Jirasek, A.

Keyvan, S.

C. Romero, X. Li, S. Keyvan, and R. Rossow, “Spectrometer-based combustion monitoring for flame stoichiometry and temperature control,” Appl. Thermal Eng. 25, 659–676(2005).
[CrossRef]

Li, X.

C. Romero, X. Li, S. Keyvan, and R. Rossow, “Spectrometer-based combustion monitoring for flame stoichiometry and temperature control,” Appl. Thermal Eng. 25, 659–676(2005).
[CrossRef]

Lim, A.

Lopez, M.

M. Lopez, J. Hernandez, and J. Romero, “Developing an optimum computer-designed multispectral system comprising a monochrome CCD camera and a liquid-crystal tunable filter,” Appl. Opt. 47, 4381–4390 (2008).
[CrossRef]

M. Lopez, J. Hernandez, E. Valero, and J. Romero, “Selecting algorithms, sensors, and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. 24, 942–956 (2007).
[CrossRef]

Maloney, L. T.

L. T. Maloney and B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. 3, 29–33 (1986).
[CrossRef]

Meher, L.

L. Meher, D. V. Sagar, and S. Naik, “Technical aspects of biodiesel production by transesterification—a review,” Renewable Sustainable Energy Rev. 10, 248–268 (2006).

Naik, S.

L. Meher, D. V. Sagar, and S. Naik, “Technical aspects of biodiesel production by transesterification—a review,” Renewable Sustainable Energy Rev. 10, 248–268 (2006).

Ngendakumana, P.

L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, “On the spectral band measurements for combustion monitoring,” Combust. Flame 158, 423–433 (2011).
[CrossRef]

Nieves, J.

Romero, C.

C. Romero, X. Li, S. Keyvan, and R. Rossow, “Spectrometer-based combustion monitoring for flame stoichiometry and temperature control,” Appl. Thermal Eng. 25, 659–676(2005).
[CrossRef]

Romero, J.

Rossow, R.

C. Romero, X. Li, S. Keyvan, and R. Rossow, “Spectrometer-based combustion monitoring for flame stoichiometry and temperature control,” Appl. Thermal Eng. 25, 659–676(2005).
[CrossRef]

Sagar, D. V.

L. Meher, D. V. Sagar, and S. Naik, “Technical aspects of biodiesel production by transesterification—a review,” Renewable Sustainable Energy Rev. 10, 248–268 (2006).

Sbarbaro, D.

L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, “On the spectral band measurements for combustion monitoring,” Combust. Flame 158, 423–433 (2011).
[CrossRef]

Schulze, G.

Torres, S.

L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, “On the spectral band measurements for combustion monitoring,” Combust. Flame 158, 423–433 (2011).
[CrossRef]

L. Arias and S. Torres, “On the flame spectrum recovery by using a low-spectral resolution sensor,” in Proceedings of the Iberoamerican Congress on Pattern Recognition CIARP (Springer-Verlag, 2011), pp. 256–263.

Turner, R.

Valero, E.

M. Lopez, J. Hernandez, E. Valero, and J. Romero, “Selecting algorithms, sensors, and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. 24, 942–956 (2007).
[CrossRef]

J. Nieves, E. Valero, J. Hernandez, and J. Romero, “Recovering fluorescent spectra with an RGB digital camera and color filters using different matrix factorizations,” Appl. Opt. 46, 4144–4154 (2007).
[CrossRef]

Wandell, B. A.

L. T. Maloney and B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. 3, 29–33 (1986).
[CrossRef]

Wolfhard, H. G.

A. G. Gaydon and H. G. Wolfhard, The Spectroscopy of Flames, 1st ed. (Chapman and Hall, 1957).

Yu, M.

Appl. Opt.

Appl. Spectrosc.

Appl. Thermal Eng.

C. Romero, X. Li, S. Keyvan, and R. Rossow, “Spectrometer-based combustion monitoring for flame stoichiometry and temperature control,” Appl. Thermal Eng. 25, 659–676(2005).
[CrossRef]

Combust. Flame

L. Arias, S. Torres, D. Sbarbaro, and P. Ngendakumana, “On the spectral band measurements for combustion monitoring,” Combust. Flame 158, 423–433 (2011).
[CrossRef]

J. Opt. Soc. Am.

M. Lopez, J. Hernandez, E. Valero, and J. Romero, “Selecting algorithms, sensors, and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. 24, 942–956 (2007).
[CrossRef]

L. T. Maloney and B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. 3, 29–33 (1986).
[CrossRef]

Prog. Energy Combust. Sci.

N. Docquier and S. Candel, “Combustion control and sensors: a review,” Prog. Energy Combust. Sci. 28, 107–150 (2002).
[CrossRef]

Renewable Sustainable Energy Rev.

L. Meher, D. V. Sagar, and S. Naik, “Technical aspects of biodiesel production by transesterification—a review,” Renewable Sustainable Energy Rev. 10, 248–268 (2006).

Other

A. G. Gaydon and H. G. Wolfhard, The Spectroscopy of Flames, 1st ed. (Chapman and Hall, 1957).

L. Arias and S. Torres, “On the flame spectrum recovery by using a low-spectral resolution sensor,” in Proceedings of the Iberoamerican Congress on Pattern Recognition CIARP (Springer-Verlag, 2011), pp. 256–263.

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Figures (5)

Fig. 1.
Fig. 1.

Typical flame’s spectrum (normalized) where discontinuous emissions are added to continuous baseline.

Fig. 2.
Fig. 2.

(a) 97 continuous spectra used to compose the learning matrix (LM). (b) The spectral sensitivity of the USB2000 spectrometer. (c) The first three principal components (PC) extracted from the LM. (d) The histogram of the principal components.

Fig. 3.
Fig. 3.

(a) Variance of the principal components as a function of the PCs index, and (b) cumulative contribution of the principal components, calculated as the cumulative variance contained in the first PCs with respect to the total variance of the PCs, as a function of the PCs index.

Fig. 4.
Fig. 4.

Baseline estimation (dotted lines) using FDM at different critical slope.

Fig. 5.
Fig. 5.

Original flame spectra (solid lines) and the recovered continuous spectra (dashed lines) of (a) the natural gas flame and (b) the bio-oil flames for different air/fuel conditions. The respective estimated discontinuous emission of (c) the natural gas flame spectra and (d) the bio-oil flame spectra.

Tables (1)

Tables Icon

Table 1. GFC Values Calculated with the Proposed and the FDM Removing Techniques

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

Ifem(λ)=Ifcont(λ)+Ifdisc(λ),
Ifcont(λ)=VN×n·ϵn×1,
rk×1=Sk×Nt·Ifcont(λ)N×1,
rk×1=Sk×Nt·VN×n·ϵn×1,
rk×1=Λk×n·ϵn×1.
I^fcont(λ)=VN×n·Λn×k+·rk×1.
I^fdisc(λ)=Ifem(λ)ΓN×n·rk×1,
ΓN×n=VN×n·Λn×k+.
GFC=|jIfcont(λj)I^fcont(λj)|[j[Ifcont(λj)]2]1/2[j[I^fcont(λj)]2]1/2,

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