Application of genetic algorithm on model-based optimisation of supercritical carbon dioxide extraction: An overview

Abstract

Green supercritical carbon dioxide extraction technology has gained enormous interest especially in the application of extraction of natural products. The use of carbon dioxide as solvent in the extraction process is very advantageous as carbon dioxide is cheap, non-Toxic, high selectivity, and can easily be separated. Supercritical carbon dioxide extraction is considered as a complex process as more factors affect the outcome of the process as compared to conventional extraction methods. This technology requires higher cost to setup and maintain the equipment. For these reasons, the whole process needs to be fully understood to ensure that the process is well optimised. Mathematical modelling is a way to explain the relationship between process variables and the outcome of the process. The optimisation of supercritical carbon dioxide extraction needs to be highly integrated to be feasible, which means that a complex mathematical model is involved. In overcoming this problem, genetic algorithm was applied in several studies. Genetic algorithm is one of the optimisation methods that is able to optimise a complex and large scale of problems, with high accuracy and practicality. This paper intends to give an overview on the application of genetic algorithm in the optimisation of mathematical modelling of supercritical carbon dioxide extraction process.