Abstract
Application of pulsed photoacoustic spectroscopy for in-situ measurements of trace gases with changeable spatial and temporal distribution requires high sensitivity, selectivity, and easy handling devices. In order to improve PAS characteristics in trace gases measurements, we have applied computational intelligence. Computational intelligence as a combination of learning, adaptation, and evolution may increase efficiency and precision of measurements and provides possibility of system–environment interactions. Two metaheuristic techniques are applied to improve accuracy in simultaneous determination of photoacoustic signal parameters: particle swarm optimization and artificial bee colony optimization. Swarm intelligences are applied to simultaneously determination of unknown parameters of photoacoustic signal: radius of the laser beam spatial profile \({r}_{L}\) and vibrational-to-translational relaxation time \({\tau }_{V-T}\). Experimental PA signals are generated in the SF6 + Ar mixture in multiphoton regime. Results produced by particle swarm optimization and artificial bee colony are discussed. Values of common parameters: population size and number of iterations in both algorithms are chosen to be the same. Advantages, such as easy implementation, high precision, and the possibility of finding solutions in a wide range of parameters make swarm intelligence algorithms efficient and perspective tool for in-situ measurements.
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References
Beck, K.M., Gordon, R.J.: Theory and application of time-resolved optoacoustics in gases. J. Chem. Phys. 89, 5560–5567 (1988)
Beck, K.M., Ringwelski, A., Gordon, R.J.: Time-resolved optoacoustic measurements of vibrational relaxation rates. Chem. Phys. Lett. 121, 529–534 (1985)
Djordjevic, КL., Galovic, S.P., Jordovic-Pavlovic, M.I., Nesic, M.V., Popovic, M.N., Cojbasic, Z.M., Markushev, D.D.: Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure. Opt. Quantum Electron. 52(5), 1–9 (2020)
Engelbrecht, A.P.: Computational Intelligence, 2nd edn. Wiley, Hoboken (2007)
Hodgkinson, J., Tatam, R.P.: Optical gas sensing: a review. Meas. Sci. Technol. 24, 012004 (2013)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN'95—International Conference on Neural Networks Year: 1995 Volume: 4 Conference Paper Publisher. IEEE (1995)
Lazarević, A., Ćojbašić, Ž, Lazarević, D.: Computationally intelligent modelling of the plasma cutting process. Int. J. Comput. Integr. Manuf. 33(3), 252–264 (2020). https://doi.org/10.1080/0951192X.2020.1736635
Li, J., Chen, W., Yu, B.: Recent progress on infrared photoacoustic spectroscopy techniques. Appl. Spectrosc. Rev. 46, 440–471 (2011)
Lukić, M., Ćojbasić, Ž, Rabasović, M., Markushev, D., Todorović, D.: Neural networks-based real-time determination of the laser beam spatial profile and vibrational-to-translational relaxation time within pulsed photoacoustics. Int. J. Thermophys. 34(8–9), 1795–1802 (2013a)
Lukić, M., Ćojbasić, Ž, Rabasović, M., Markushev, D., Todorović, D.: Genetic algorithms application for the photoacoustic signal temporal shape analysis and energy density spatial distribution calculation. Int. J. Thermophys. 34(8–9), 1466–1472 (2013b)
Lukić, M., Ćojbašić, Ž, Rabasović, M.D., Markushev, D.D.: Computationally intelligent pulsed photoacoustics. Meas. Sci. Technol. 25(12), 125203 (2014)
Malan, K., Engelbrecht, A.: Algorithm comparisons and the significance of population size. In: Proceedings of the IEEE Congress on Evolutionary Computation (2008)
Markushev, D.D., Jovanović-Kurepa, J., Terzić, M.: Excitation dynamics during the multiphoton absorption in SF6+buffer-gas mixtures. J. Quantum Spectrosc. Radiat. Transf. 76, 85–99 (2003)
Meyer, P.L., Sigrist, M.W.: Atmospheric pollution monitoring using CO2-laser photoacoustic spectroscopy and other techniques. Rev. Sci. Instrum. 61(7), 1779–1807 (1990)
Parsopoulos, К., Vrahatis, M.: Particle swarm optimization and intelligence: advances and applications. IGI Global, Joanina, Greece (2010)
Rabasović, M.D., Nikolić, J.D., Markushev, D.D.: Pulsed photoacoustic system calibration for highly excited molecules: II. Influence of the laser beam profile and the excitation energy decay. Meas. Sci. Technol. 17, 2938–2944 (2006a)
Rabasović, M.D., Markushev, D.D., Jovanović-Kurepa, J.: Pulsed photoacoustic system calibration for highly excited molecules. Meas. Sci. Technol. 17, 1826–1837 (2006b)
Rabasović, M.D., Nikolić, J.D., Markushev, D.D.: Simultaneous determination of the spatial profile of the laser beam and vibrational-to-translational relaxation time by pulsed photoacoustics. Appl. Phys. B 88, 309–315 (2007)
Repond, P., Sigrist, M.W.: Photoacoustic spectroscopy on trace gases with continuously tunable CO2 laser. Appl. Opt. 35(21), 4065–4085 (1996)
Russo, S.D., Sampaolo, A., Patimisco, P., Menduni, G., Giglio, M., Hoelzl, C., Passaro, V.M.N., Wu, H., Dong, L., Spagnolo, V.: Quartz-enhanced photoacoustic spectroscopy exploiting low-frequency tuning forks as a tool to measure the vibrational relaxation rate in gas species. Photoacoustics 21, 100227 (2021)
Sigrist, M.W.: Trace gas monitoring by laser-photoacoustic spectroscopy. Infrared Phys. Technol. 36(I), 415–425 (1995)
Sigrist, M.W.: Trace gas monitoring by laser photoacoustic spectroscopy and related techniques (plenary). Rev. Sci. Instrum. 74(1), 486–490 (2003)
Talbi, E.G.: Metaheuristics: from design to implementation. Wiley, New York (2009)
Tomberg, T., Vainio, M., Hieta, T., Halonen, L.: Sub-parts-per-trillion level sensitivity in trace gas detection by cantilever-enhanced photo-acoustic spectroscopy. Sci. Rep. 8, 1848 (2018)
Xiong, L., Bai, W., Chen, F., Zhao, X., Yu, F., Diebold, G.J.: Photoacoustic trace detection of gases at the parts-per-quadrillion level with a moving optical grating. Proc. Natl. Acad. Sci. USA 114(28), 7246–7249 (2017)
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This article is part of the Topical Collection on Photonics: Current Challenges and Emerging Applications, Guest edited by Jelena Radovanovic, Dragan Indjin, Maja Nesic, Nikola Vukovic and Milena Milosevic.
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Lukić, M., Ćojbašić, Ž. & Markushev, D.D. Trace gases analysis in pulsed photoacoustics based on swarm intelligence optimization. Opt Quant Electron 54, 674 (2022). https://doi.org/10.1007/s11082-022-04059-y
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DOI: https://doi.org/10.1007/s11082-022-04059-y