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 | Citation: | 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 |
Appears in Collections: | Staff Publications |
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.