Parameter estimation of complex chemical kinetics with covariance matrix adaptation evolution strategy

This paper presents a method for parameter estimation of complex chemical kinetics by an evolution strategy which uses a scheme called covariance matrix adaptation. The advantage of this scheme is that a completely derandomized self-adaptation of mutation distribution can be achieved. The used algorithm utilizes even cumulation to improve the performance. The method was tested on experimental data of ethene pyrolysis and the parameters of two kinetic models depicting the phenomenon were estimated. The models comprised of 37 and 687 reversible reactions respectively. The method successfully estimated the kinetic parameters for both models.