Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/53007
Título: Chemical process simulation using evolutionary algorithms: application to the analysis of impedance parameters of electrochemical systems
Autores/as: Gonzalez, F.
Greiner, D. 
Aznarez, J. J. 
Mena, V. 
Souto, R. M.
Santana, J. J. 
Palabras clave: Nonlinear Least-Squares
Genetic Algorithms
Differential Evolution
Optimization
Fecha de publicación: 2015
Editor/a: 0001-9704
Publicación seriada: Afinidad 
Resumen: Electrochemical Impedance Spectroscopy (EIS) is a powerful tool in the characterization of organic coated metal systems because the method can give both qualitative and quantitative information regarding their behavior. Impedance data are fitted to a relevant electrical equivalent circuit in order to evaluate parameters directly related to the resistance and the durability of coated metal systems. The parametric analysis of the measured data is usually performed using non-linear regression algorithms, though they present the major disadvantage that correct fitting requires introduction of initial values for the parameters adequate to produce a quick and good convergence of the fitting process. An alternate method to regression algorithms for the analysis of measured impedance data in terms of equivalent circuit parameters is provided by evolutionary algorithms, more especially the differential evolution algorithms. The applicability of this method was tested by comparison with the results produced using a commercial fitting software (namely, ZSimpWin). In all the cases, better fitting results were obtained using the differential evolution algorithm.
URI: http://hdl.handle.net/10553/53007
ISSN: 0001-9704
Fuente: Afinidad[ISSN 0001-9704],v. 72 (572), p. 278-283
Colección:Artículos
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