Particle Swarm Optimization and Application to Liquid–Liquid Equilibrium

Authored by: Anand Bharti , Debashis Kundu , Dharamashi Rabari , Tamal Banerjee

Phase Equilibria in Ionic Liquid Facilitated Liquid–Liquid Extractions

Print publication date:  March  2017
Online publication date:  March  2017

Print ISBN: 9781498769488
eBook ISBN: 9781315367163
Adobe ISBN:

10.1201/9781315367163-6

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Abstract

Low volatile phosphonium ILs have proved to be better solvents as compared to volatile organic solvents from our liquid–liquid equilibrium (LLE) experiments in Chapter 2. However, the experiments were carried out on the laboratory scale. The separation has not been implemented on an industrial scale to date. For transforming laboratory data for an industrial application, a process optimization study is necessary. In the past, many popular stochastic algorithms such as Genetic Algorithm (GA) (Goldberg, 1989), Simulated Annealing (SA) (Kirkpatrick, Gelatt, & Vecchi, 1983), particle swarm optimization (PSO) (Eberhart & Kennedy, 1995a, 1995b), Ant colony optimization (ACO) (Colorni, Dorigo, & Maniezzo, 1991; Dorigo, 1992), Differential Evolution (DE) (Storn & Price, 1997) and Self-Organising Migrating Algorithm (SOMA) (Zelinka, 2004) have been investigated for optimization in science and engineering.

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