Kinetic Modeling and Parameters Identification Base d on Metaheuristic Optimization Techniques for Extractio n Process of Marrubium vulgare L. Essential Oil

Abstract

Recently, increasing attention has been directed to the isolation of natural active components from various medicinal plants. In the pr esent research, the extraction of essential oil from horehound ( M. vulgare L.) is presented. Effects of mass ratio and particle size on the process performance were studi ed and kinetics were determined. The chemical composition of the volatiles present in M. vulgare L. was evaluated for the sample extracted in the optimum conditions (mass ra tio, 3 kg m -3 and particle size,0.1< d <0.63 mm) by using GC–MS. Eugenol (21.5%), β -Caryophyllene (11.5%) and β - bisabolene (10.3 %) were the major constituents fou nd. Experimental data were fitted into three mathematical models having one and two t ime constants, in order to describe the extraction behaviour. The obtained coefficients of correlation show that the predicted and experimental data were in good agreement (0.995 4< R <0.9982). In all cases the model constants have been found to change with mass ratio and particle size. The study was also an opportunity to improve the performance of two ev olutionary algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO ), for identification of kinetic parameters with a satisfactory accuracy. The presen ted approach can be helpful for modeling and optimization of further extraction pro cesses. Keywords: Genetic algorithm, Grinding effect, Parameter iden tification, Particle swarm optimization Mass ratio effect.