|M.Sc Student||Tesler Alexander|
|Subject||Analysis of Impedance Spectroscopy Data Using Genetic|
|Department||Department of Chemical Engineering||Supervisors||Professor Daniel Lewin|
|Professor Yoed Tsur|
|Full Thesis text|
The need to study and characterize interactions of solid-solid, solid-liquid interfaces and physical and electrical properties of materials results in the wide application of electrochemical methods in modern chemistry and material science. The key attribute is the complex electric impedance - measured as a function of frequency of small sinusoidal potential perturbations, and referred to as ImpedanceSpectroscopy (IS). While the collection of impedance data is relatively simple, their accurate analysis and interpretation, expressed as a predictive model, is not an easy task. To estimate the model parameters, it is necessary to solve the inverse problem (Fredholm integral equation with complex kernel function), proceeding from discrete measured points to a continuous model. Unfortunately, the problem is ambiguous and ill-posed and cannot be solved directly because of the presence of noise in the measured signal. In fact, the data can be fitted to an infinite number of models. The goal of the research is to quantify the noise of measured IS signals and to find the most compact models, adopted from Baltianski and Tsur, that fit the data well enough using evolutionary programming methods. Two complementary methods have been applied: Genetic Algorithm (GA) and Genetic Programming (GP). The former method facilitates robust parameter estimation for arbitrary nonlinear models, while the latter uses the adaptive GP approach of Grosman and Lewin to create relatively non-complex models through the penalization of unnecessarily complex models. As demonstrated, this approach enables the most appropriate reduced-order models to be generated to match Impedance Spectroscopy data.