Chemical process simulation using evolutionary algorithms: application to the analysis of impedance parameters of electrochemical systems

Abstract

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