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Alex Tesler will be giving his MSc research presentation to the
Chemical Engineering Department on 5th July at 12:30 in Auditorium
6. The
seminar is entitled: "Analysis of Impedance Data using Genetic
Programming Methods."
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 resistivity – impedance –
measured as a function of frequency of small sinusoidal potential
perturbations, and referred to as Impedance Spectroscopy (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 of the first kind), 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 is can be fitted to an infinite number of models.
The goal of the research is to quantify the noise of measured IS signals
(using the Kramers-Kronig Transform) and to find the most compact models 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 IS data.
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