A new method for multicomponent mixture separation cascade optimization using artificial bee colony algorithm. (June 2020)

• Record Type: Journal Article
• Authors: Ezazi, Farzaneh; Mallah, Mohammad Hassan; Sabet, Javad Karimi; Norouzi, Ali
• Is Part Of: Progress in nuclear energy. Volume 124(2020)
• Publication Date: 2020-06
• Subjects: Matched-X cascade -- Objective function -- Total interstage flow -- Artificial bee colony algorithm -- Particle swarm optimization; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 333.7924
• DOI: 10.1016/j.pnucene.2020.103371
• Languages: English
• ISSNs: 0149-1970

Abstract

Abstract: This research deals with a cascade design method using artificial bee colony algorithm to find multicomponent mixture separation cascade optimal parameters. Therefore, the real coded artificial bee colony was developed and the waste and product concentrations of a target component and the total interstage flow of the cascade were selected as the objective function main items. To evaluate the proposed method, a comparative study between the simulated and optimum matched-X cascades and optimum cascade has been performed. The method leads to an optimum matched-X cascade with lower total interstage flow than the common matched-X cascade, and it can successfully find the optimum cascade with better characteristics than the common and optimum matched-X cascades. Also, it was compared with the pre-published results of particle swarm optimization, in the case of variable separation factors. The results revealed that the method significantly outperformed the particle swarm optimization and usage of the algorithm guarantees finding admissible solution. Highlights: This research deals with a cascade design method using artificial bee colony algorithm. The real coded artificial bee colony was developed. To evaluate the proposed method, a comparative study has been performed. The method leads to an optimum matched-X cascade. The results revealed that the method significantly outperformed the particle swarm optimization.