Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.cherd.2012.04.011
Title: Evaluation of Covariance Matrix Adaptation Evolution Strategy, Shuffled Complex Evolution and Firefly Algorithms for phase stability, phase equilibrium and chemical equilibrium problems
Authors: Fateen, S.E.K.
Bonilla-Petriciolet, A.
Rangaiah, G.P. 
Keywords: Chemical equilibrium calculations
Covariant Matrix Adaptation Evolution Strategy
Phase equilibrium calculations
Phase stability analysis
Shuffled Complex Evolution
Issue Date: Dec-2012
Source: Fateen, S.E.K., Bonilla-Petriciolet, A., Rangaiah, G.P. (2012-12). Evaluation of Covariance Matrix Adaptation Evolution Strategy, Shuffled Complex Evolution and Firefly Algorithms for phase stability, phase equilibrium and chemical equilibrium problems. Chemical Engineering Research and Design 90 (12) : 2051-2071. ScholarBank@NUS Repository. https://doi.org/10.1016/j.cherd.2012.04.011
Abstract: Phase equilibrium calculations and phase stability analysis of reactive and non-reactive systems play a significant role in the simulation, design and optimization of reaction and separation processes in chemical engineering. These challenging problems, which are often multivariable and non-convex, require global optimization methods for solving them. Stochastic global optimization algorithms have shown promise in providing reliable and efficient solutions for these thermodynamic problems. In this study, we evaluate three alternative global optimization algorithms for phase and chemical equilibrium calculations, namely, Covariant Matrix Adaptation-Evolution Strategy (CMA-ES), Shuffled Complex Evolution (SCE) and Firefly Algorithm (FA). The performance of these three stochastic algorithms was tested and compared to identify their relative strengths for phase equilibrium and phase stability problems. The phase equilibrium problems include both multi-component systems with and without chemical reactions. FA was found to be the most reliable among the three techniques, whereas CMA-ES can find the global minimum reliably and accurately even with a smaller number of iterations. © 2012 The Institution of Chemical Engineers.
Source Title: Chemical Engineering Research and Design
URI: http://scholarbank.nus.edu.sg/handle/10635/63872
ISSN: 02638762
DOI: 10.1016/j.cherd.2012.04.011
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