Simulation and optimization of a non-linear dynamic process using mathematica

Abstract

Evolutionary algorithms (EAs) have proven to be a powerful and robust optimizing technique even for complex optimization problems. The main aim of this work is to show that such a powerful simulating and optimizing of a non-linear dynamic process. In this paper, the complex reaction sequence used to study various reaction kinetics by optimization the rate constants. Two algorithms from the field of artificial intelligent—Differential evolution (DE), Self-organizing migrating algorithm (SOMA) are used in this investigation. Two optimization techniques were developed using Mathematica for accurately determining the rate constants of the reaction at certain temperature from the experimental data. The results show that EAs are used successfully in the process optimization.