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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 7 — Mar. 1, 2012
  • pp: B108–B114

Construction of a predictive model for concentration of nickel and vanadium in vacuum residues of crude oils using artificial neural networks and LIBS

José L. Tarazona, Jáder Guerrero, Rafael Cabanzo, and E. Mejía-Ospino  »View Author Affiliations


Applied Optics, Vol. 51, Issue 7, pp. B108-B114 (2012)
http://dx.doi.org/10.1364/AO.51.00B108


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Abstract

A predictive model to determine the concentration of nickel and vanadium in vacuum residues of Colombian crude oils using laser-induced breakdown spectroscopy (LIBS) and artificial neural networks (ANNs) with nodes distributed in multiple layers (multilayer perceptron) is presented. ANN inputs are intensity values in the vicinity of the emission lines 300.248, 301.200 and 305.081 nm of the Ni(I), and 309.310, 310.229, and 311.070 nm of the V(II). The effects of varying number of nodes and the initial weights and biases in the ANNs were systematically explored. Average relative error of calibration/prediction (REC/REP) and average relative standard deviation (RSD) metrics were used to evaluate the performance of the ANN in the prediction of concentrations of two elements studied here.

© 2012 Optical Society of America

OCIS Codes
(020.0020) Atomic and molecular physics : Atomic and molecular physics
(300.0300) Spectroscopy : Spectroscopy

History
Original Manuscript: September 26, 2011
Revised Manuscript: December 1, 2011
Manuscript Accepted: December 14, 2011
Published: February 24, 2012

Citation
José L. Tarazona, Jáder Guerrero, Rafael Cabanzo, and E. Mejía-Ospino, "Construction of a predictive model for concentration of nickel and vanadium in vacuum residues of crude oils using artificial neural networks and LIBS," Appl. Opt. 51, B108-B114 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-7-B108


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References

  1. T. P. Sorokina, L. A. Buluchevskaya, O. V. Potapenko, and V. P. Doronin, “Conversion of nickel and vanadium porphyrins under catalytic cracking conditions,” Pet. Chem. 50, 51–55 (2010). [CrossRef]
  2. E. R. Cabrera, J. F. Franco, F. Mondragón, and J. J. Fernández, “Conversión de fondos de vacío de petróleo a semicoque,” Revista Energética 37, 39–51 (2007).
  3. J. L. Fabec and M. L. Ruschak, “Determination of nickel, vanadium and sulfur in crudes and heavy crude fractions by inductively coupled argon plasma/atomic emission spectrometry and flame atomic absorption spectrometry,” Anal. Chem. 57, 1853–1863 (1985). [CrossRef]
  4. H. M. Al-Swaidan, “The determination of lead, nickel and vanadium in Saudi Arabian crude oil by sequential injection analysis/inductively-coupled plasma mass spectrometry,” Talanta 43, 1313–1319 (1996). [CrossRef]
  5. C. Duyck, N. Miekeley, C. L. P. Silveira, and P. Szatmari, “Trace element determination in crude oil and its fractions by inductively coupled plasma mass spectrometry using ultrasonic nebulization of toluene solutions,” Spectrochim. Acta B 57, 1979–1990 (2002). [CrossRef]
  6. K. Iwasaki and K. Tanaka, “Preconcentration and X-ray fluorescence determination of vanadium, nickel and iron in residual fuel oils and in particulate material from oil-fired sources,” Anal. Chim. Acta 136, 293–299 (1982). [CrossRef]
  7. E. R. Denoyer and L. A. Siegel, “Determination of sulfur, nickel and vanadium in fuel and residual oils by X-ray fluorescence spectrometry,” Anal. Chim. Acta 192, 361–366 (1987). [CrossRef]
  8. M. Turunen, S. Peraniemi, M. Ahlgren, and H. Westerholm, “Determination of trace elements in heavy oil samples by graphite furnace and cold vapour atomic absorption spectrometry after acid digestion,” Anal. Chim. Acta 311, 85–91 (1995). [CrossRef]
  9. M. Bettinelli and P. Tittarelli, “Evaluation and validation of instrumental procedures for the determination of nickel and vanadium in fuel oils,” J. Anal. At. Spectrom. 9, 805–812 (1994). [CrossRef]
  10. I. Lang, G. Sebor, V. Sychra, D. Kolihova, and O. Weisser, “The determination of metals in petroleum samples by atomic absorption spectrometry: Part II. Determination of nickel,” Anal. Chim. Acta 84, 299–305 (1976). [CrossRef]
  11. O. Osibanjo, S. E. Kakulu, and S. O. Ajayi, “Analytical application of inorganic salt standards and mixed-solvent systems to trace-metal determination in petroleum crudes by atomic-absorption spectrophotometry,” Analyst 109, 127–129 (1984). [CrossRef]
  12. N. S. Kaki, M. M. Barbooti, S. S. Baha-Uddin, and E. B. Hassan, “Determination of Trace Metals and Their Distribution in Heavy Crude Oil Distillates (350 °C+) by Atomic Absorption Spectrophotometry,” Appl. Spectrosc. 43, 1257–1259 (1989). [CrossRef]
  13. O. Platteau and M. Carrillo, “Determination of metallic elements in crude oil-water emulsions by flame AAS,” Fuel 74, 761–767 (1995). [CrossRef]
  14. F. J. Fortes, T. Ctvrtnícková, M. P. Mateo, L. M. Cabalín, G. Nicolas, and J. J. Laserna, “Spectrochemical study for the in situ detection of oil spill residues using laser-induced breakdown spectroscopy,” Anal. Chim. Acta 683, 52–57 (2010). [CrossRef]
  15. T. Hussain and M. A. Gondal, “Monitoring and assessment of toxic metals in Gulf war oil spill contaminated soil using laser-induced breakdown spectroscopy,” Environ. Monit. Assess. 136, 391–399 (2007). [CrossRef]
  16. M. A. Gondal, T. Hussain, Z. H. Yamani, and M. A. Baig, “Detection of heavy metals in Arabian crude oil residue using laser induced breakdown spectroscopy,” Talanta 69, 1072–1078 (2006). [CrossRef]
  17. M. A. Gondal, M. N. Siddiqui, and M. M. Nasr, “Detection of trace metals in asphaltenes using an advanced laser-induced breakdown spectroscopy (LIBS) technique,” Energy Fuels 24, 1099–1105 (2010). [CrossRef]
  18. A. Ciucci, M. Corsi, V. Palleschi, S. Rastelli, A. Salvetti, and E. Tognoni, “New procedure for quantitative elemental analysis by laser induced plasma spectroscopy,” Appl. Spectrosc. 53, 960–964 (1999). [CrossRef]
  19. E. Tognoni, G. Cristoforetti, S. Legnaioli, and V. Palleschi, “Calibration-free laser-induced breakdown spectroscopy: State of the art,” Spectrochim. Acta B 65, 1–14 (2010). [CrossRef]
  20. E. Tognoni, G. Cristoforetti, S. Legnaioli, V. Palleschi, A. Salvetti, M. Mueller, U. Panne, and I. Gornushkin, “A numerical study of expected accuracy and precision in calibration-free laser-induced breakdown spectroscopy in the assumption of ideal analytical plasma,” Spectrochim. Acta B 62, 1287–1302 (2007). [CrossRef]
  21. E. Schenk and J. Almirall, “Elemental analysis of cotton by laser-induced breakdown spectroscopy,” Appl. Opt. 49, C153–C160 (2010). [CrossRef]
  22. P. Inakollu, T. Philip, A. K. Rai, F.-Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta B 64, 99–104 (2009). [CrossRef]
  23. E. C. Ferreira, D. M. B. P. Milori, E. J. Ferreira, R. M. Da Silva, and L. Martin-Neto, “Artificial neural network for Cu quantitative determination in soil using a portable laser induced breakdown spectroscopy system,” Spectrochim. Acta B 63, 1216–1220 (2008). [CrossRef]
  24. V. Motto-Ros, A. S. Koujelev, G. R. Osinski, and A. E. Dudelzak, “Quantitative multi-elemental laser-induced breakdown spectroscopy using artificial neural networks,” J. Eur. Opt. Soc. Rapid Pub. 3, 08011 (2008).
  25. A. Koujelev, V. Motto-Ros, D. Gratton, and A. Dudelzak, “Laser-induced breakdown spectroscopy as geological tool for field planetary analogue research,” Can. Aeronaut. Space J. 55, 97–106 (2009). [CrossRef]
  26. A. Koujelev, M. Sabsabi, V. Motto-Ros, S. Laville, and S. L. Lui, “Laser-induced breakdown spectroscopy with artificial neural network processing for material identification,” Planet. Space Sci. 58, 682–690 (2010). [CrossRef]
  27. S. Y. Oh, F.-Y. Yueh, and J. P. Singh, “Quantitative analysis of tin alloy combined with artificial neural network prediction,” Appl. Opt. 49, C36–C41 (2010). [CrossRef]
  28. I. Prasanthi, P. Thomas, K. R. Awadhesh, F.-Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta B 64, 99–104 (2009). [CrossRef]
  29. J. B. Sirven, B. Bousquet, L. Canioni, L. Sarger, S. Tellier, M. Potin-Gautier, and I. Le Hecho, “Qualitative and quantitative investigation of chromium-polluted soils by laser-induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256–262 (2006). [CrossRef]
  30. A. Ramil, A. J. López, and A. Yañez, “Application of artificial neural networks for the rapid classification of archaeological ceramics by means of laser induced breakdown spectroscopy (LIBS),” Appl. Phys. A 92, 197–202 (2008). [CrossRef]
  31. M. Boueri, V. Motto-Ros, W. Q. Lei, Q. L. Ma, L. J. Zhen, H. P. Zeng, and J. Yu, “Identification of polymer materials using laser-induced breakdown spectroscopy combined with artificial neural network,” Appl. Spectrosc. 65, 307–314 (2011). [CrossRef]
  32. M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt Algorithm,” IEEE Trans. Neural Netw. 5, 989–993 (1994). [CrossRef]
  33. M. Otto, Chemometrics, 2nd ed. (Wiley-VCH, 2007).

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