Optimizing Arrhenius parameters for multi-step reactions via metaheuristic algorithms

Abstract

In combustion simulation, the Arrhenius equation is a key tool for modeling multi-step reactions such as propane and methane reactions. It describes a relationship between the reaction rate, temperature, the pre-exponential factor, and activation energy. Applying these parameters outside their validated temperature and pressure ranges, or for unverified reactions, can result in important errors. The present study optimizes the coefficients of the Arrhenius model for multi-step combustion reactions, by utilizing experimental data and advanced optimization techniques. Our methodology incorporates metaheuristics techniques such as least squares minimization, particle swarm optimization, ant colony optimization, the slime mold algorithm, and the whale optimization algorithm. The results indicate that the optimized coefficients significantly improve the predictions while reducing computational time and associated costs. Furthermore, this paper presents a comprehensive comparative analysis of the various optimization techniques utilized and clarifies the advantages and limitations of each technique in the context of Arrhenius equation optimization.