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Fateen S.E.K.,Cairo University | Bonilla-Petriciolet A.,Aguascalientes Institute of Technology | Rangaiah G.P.,National University of Singapore
Chemical Engineering Research and Design | Year: 2012

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


Bonilla-Petriciolet A.,Aguascalientes Institute of Technology | Segovia-Hernandez J.G.,University of Guanajuato
Fluid Phase Equilibria | Year: 2010

Particle swarm optimization is a novel evolutionary stochastic global optimization method that has gained popularity in the chemical engineering community. This optimization strategy has been successfully used for several applications including thermodynamic calculations. To the best of our knowledge, the performance of PSO in phase stability and equilibrium calculations for both multicomponent reactive and non-reactive mixtures has not yet been reported. This study introduces the application of particle swarm optimization and several of its variants for solving phase stability and equilibrium problems in multicomponent systems with or without chemical equilibrium. The reliability and efficiency of a number of particle swarm optimization algorithms are tested and compared using multicomponent systems with vapor-liquid and liquid-liquid equilibrium. Our results indicate that the classical particle swarm optimization with constant cognitive and social parameters is a reliable method and offers the best performance for global minimization of the tangent plane distance function and the Gibbs energy function in both reactive and non-reactive systems. © 2009 Elsevier B.V. All rights reserved. Source


Bhargava V.,Indian Institute of Technology Kharagpur | Fateen S.E.K.,Cairo University | Bonilla-Petriciolet A.,Aguascalientes Institute of Technology
Fluid Phase Equilibria | Year: 2013

In this study, Cuckoo Search is introduced for performing phase equilibrium and stability calculations for the first time. Cuckoo Search is a population-based method that mimics the reproduction strategy of cuckoos. This meta-heuristics have been successfully used for solving some engineering design and optimization problems with promising results. However, this emerging optimization method has not been applied in chemical engineering problems including thermodynamic calculations. This study reports the application of Cuckoo Search and its modified version for phase equilibrium and stability calculations in both reactive and non-reactive systems. Performance of this nature-inspired optimization method has been analyzed using several phase stability, phase equilibrium and reactive phase equilibrium problems. Results show that Cuckoo Search offers a reliable performance for solving these thermodynamic calculations and is better than other meta-heuristics previously applied in phase equilibrium modeling. © 2012 Elsevier B.V. Source


Elnabawy A.O.,Cairo University | Fateen S.-E.K.,Cairo University | Bonilla-Petriciolet A.,Aguascalientes Institute of Technology
Industrial and Engineering Chemistry Research | Year: 2014

Stochastic global optimization algorithms have shown promise in providing reliable and efficient solutions for phase stability and phase equilibrium problems in reactive and nonreactive systems. A special class of stochastic methods is Swarm Intelligence, in which search agents are allowed to interact with each other and with their environment and benefit from their peers in their collective pursuit for the global minimum, resulting in an intelligent behavior unknown to the individual agents. Of special interest are swarm intelligence methods with less tunable algorithm parameters, which allow for easy and user-friendly implementation. In particular, this study introduces the Charged System Search, a novel swarm intelligence method, as a global optimization tool to the Chemical Engineering literature via implementing it, for the first time, in solving phase stability and equilibrium problems. Two Charged System Search variants have been employed, namely, the Magnetic Charged System Search and the hybrid version with Particle Swarm Optimization. This hybrid method is coupled with chaotic maps to overcome the local optimum entrapment and to aid its exploration capability. Results indicate that these two variants generally outperformed the Charged System Search, especially the hybrid chaotic algorithm. Results of this study were also compared to those reported for other swarm intelligence methods applied in phase equilibrium calculations. In summary, this study introduces novel swarm intelligence methods for performing phase stability and equilibrium calculations in both reactive and nonreactive systems. © 2014 American Chemical Society. Source


Fateen S.-E.K.,Cairo University | Bonilla-Petriciolet A.,Aguascalientes Institute of Technology
Industrial and Engineering Chemistry Research | Year: 2014

This study introduces a strategy to improve the effectiveness of Cuckoo Search (CS) algorithm for the unconstrained Gibbs free energy minimization in phase equilibrium calculations of nonreactive systems. Specifically, the gradient information of the unconstrained Gibbs free energy function, which is readily available, is used to enhance the balance between diversification and intensification stages of the CS algorithm for phase-split calculations in multicomponent systems. The results showed that it is feasible to improve the numerical performance of the CS algorithm using the gradient information of the Gibbs free energy function; this improved method provides better results for phase equilibrium calculations in nonreactive systems with insignificant additional computational effort. This gradient-based Cuckoo Search (GBCS) algorithm outperformed the conventional CS algorithm, in terms of its reliability and efficiency in solving phase equilibrium problems, especially for multicomponent systems. © 2014 American Chemical Society. Source

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