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