Integration of genetic algorithm and mesoscopic modeling for the optimization of membrane separation processes

Abstract

This article is dedicated to the development of an innovative approach to optimizing membrane separation processes. The paper introduces the integration of a genetic algorithm (GA) and mesoscopic modeling to enhance the efficiency and accuracy of process parameter optimization. The GA is employed for evolutionary search of optimal parameters, such as pressure, temperature, and membrane material characteristics. The use of evolutionary principles allows for efficient exploration of parameter space, identifying optimal solutions. Mesoscopic modeling serves as a tool for detailed analysis and visualization of membrane separation processes. It involves modeling the interaction of molecules with the membrane surface, enabling a more accurate consideration of the physicochemical aspects of the process. The integration of the GA and mesoscopic modeling creates a unique tool for membrane separation process optimization. The developed approach contributes not only to improving component separation efficiency but also to minimizing energy consumption. The method presented in the article has been successfully tested on model membrane process systems and demonstrated significant improvements compared to traditional optimization methods. The research results confirm the potential of the proposed approach for application in membrane technology industries, opening new perspectives in the field of separation process optimization.