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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.


Bonilla-Petriciolet A.,Aguascalientes Institute of Technology | Rangaiah G.P.,National University of Singapore | Segovia-Hernandez J.G.,University of Guanajuato
Fluid Phase Equilibria | Year: 2011

Phase equilibrium modeling plays an important role in design, optimization and control of separation processes. The global optimization problem involved in phase equilibrium calculations is very challenging due to the high non-linearity of thermodynamic models especially for multi-component systems subject to chemical reactions. To date, a few attempts have been made in the application of stochastic methods for reactive phase equilibrium calculations compared to those reported for non-reactive systems. In particular, the population-based stochastic methods are known for their good exploration abilities and, when optimal balance between the exploration and exploitation is found, they can be reliable and efficient global optimizers. Genetic algorithms (GAs) and differential evolution with tabu list (DETL) have been very successful for performing phase equilibrium calculations in non-reactive systems. However, there are no previous studies on the performance of both these strategies to solve the Gibbs free energy minimization problem for systems subject to chemical equilibrium. In this study, the constrained and unconstrained Gibbs free energy minimization in reactive systems have been analyzed and used to assess the performance of GA and DETL. Specifically, the numerical performance of these stochastic methods have been tested using both conventional and transformed composition variables as the decision vector for free energy minimization in reactive systems, and their relative strengths are discussed. The results of these strategies are compared with those obtained using SA, which has shown competitive performance in reactive phase equilibrium calculations. To the best of our knowledge, there are no studies in the literature on the comparison of reactive phase equilibrium using both the formulations with stochastic global optimization methods. Our results show that the effectiveness of the stochastic methods tested depends on the stopping criterion, the type of decision variables, and the use of local optimization for intensification stage. Overall, unconstrained Gibbs free energy minimization involving transformed composition variables requires more computational time compared to constrained minimization, and DETL has better performance for both constrained and unconstrained Gibbs free energy minimization in reactive systems. © 2010 Elsevier B.V.


Hernandez-Montoya V.,Aguascalientes Institute of Technology | Ramirez-Montoya L.A.,Aguascalientes Institute of Technology | Bonilla-Petriciolet A.,Aguascalientes Institute of Technology | Montes-Moran M.A.,CSIC - National Coal Institute
Biochemical Engineering Journal | Year: 2012

Carbons loaded with specific chemical moieties were prepared from pecan nut shells employing a natural modifier agent obtained from egg shell, which is rich in calcium, for the selective adsorption of fluoride from water. A L 4 orthogonal array of the Taguchi method was used to optimize the synthesis conditions for obtaining these selective carbons. The samples obtained were characterized and the elemental composition, textural parameters and morphology were determined. Fluoride adsorption experiments were performed in synthetic and real groundwater samples. Results showed that carbons obtained from pecan nut shells modified with a calcium solution extracted from egg shells (CMPNS) were more effective for fluoride removal than those using the nut shell precursor as such. The calcium chemical species on the carbon surfaces were more important in the fluoride adsorption process than the carbon textural parameters. In addition, hydrogencarbonate was found to be the main competitor for the active sites of CMPNS during the fluoride removal process. © 2012 Elsevier B.V..


Sotelo-Pichardo C.,Aguascalientes Institute of Technology | Ponce-Ortega J.M.,Universidad Michoacana de San Nicolás de Hidalgo | El-Halwagi M.M.,Texas A&M University | Frausto-Hernandez S.,Aguascalientes Institute of Technology
Journal of Cleaner Production | Year: 2011

This paper presents a new general mathematical programming model for the optimal retrofit of material conservation networks considering recycle, reuse and regeneration schemes (notice that most of the previously reported methods only have considered the synthesis case, and nowadays it is very important to have strategies for the retrofit of water networks because several existing water network are functioned under suboptimal conditions because these were synthesized using inefficient approaches or because the process and environmental constraints have changed). The model considers the reconfiguration of existing networks to satisfy stricter process and environmental constraints considering the repiping for the network, the reuse of the existing treatment units, the modification for the capacity and performance of the existing units and the installation of new treatment units to reduce the overall operating cost through the reduction of the use of fresh sources. The objective function accounts for the minimization of the total annual cost associated to the retrofit process. This retrofit process involves simultaneously economic (because the reduction of the fresh sources costs) and environmental (because the reduction of the waste streams discharged to the environment and with a better quality) improvements. The applicability of the proposed model is proved through a set of example problems addressed, where no numerical complications were observed. In addition, the proposed approach is general and it can be applied to any specific case with the information required. © 2011 Elsevier Ltd. All rights reserved.


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.


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.


Bonilla-Petriciolet A.,Aguascalientes Institute of Technology
Fluid Phase Equilibria | Year: 2012

The modeling of vapor-liquid equilibrium data using local composition models is an interesting and challenging global optimization problem in the context of chemical engineering and applied thermodynamics. Until now, several deterministic and stochastic global optimization strategies have been used for modeling vapor-liquid equilibrium (VLE) data. Stochastic optimization methods may offer several advantages for solving global optimization problems and, until now, some meta-heuristics have been tested for modeling phase equilibrium data. However, these optimization strategies usually show a robust performance but, in some challenging problems, they may fail to locate the global optimum. In particular, Harmony Search (HS) is a direct-search method with attractive characteristics for its use in phase equilibrium modeling and calculations. However, to the best of our knowledge, this stochastic optimization strategy has not been used to perform this type of thermodynamic calculations. This study introduces the HS method for solving the non-linear parameter estimation problem involved in the modeling of VLE data. Specifically, the performance of this meta-heuristic has been tested and analyzed using several sets of binary VLE data with local composition models and both the classical approach of the least squares regression and the error-in-variable formulation. Results of this study are used to identify the capabilities and limitations of HS for VLE data modeling. In summary, HS is a promising meta-heuristic for processing these phase equilibrium data using the classical least square formulation and may offer a better performance than those obtained using current stochastic methods such as Genetic Algorithm or Particle Swarm Optimization. However, the reliability of the traditional HS is poor for VLE parameter estimation using the error-in-variable formulation. Finally, this paper discusses and analyzes alternatives to improve the performance of HS in VLE data modeling especially for the error-in-variable approach. Results indicate that the HS variants called the Improved Harmony Search and the Global-Best Harmony Search offer a better performance for solving EIV parameter estimation problems. © 2012 Elsevier B.V.


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.


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.


Fateen S.E.K.,Cairo University | Bonilla-Petriciolet A.,Aguascalientes Institute of Technology
Fluid Phase Equilibria | Year: 2014

Stochastic global optimization methods have been successfully used to perform phase stability calculations. However, these methods may show some drawbacks in challenging phase stability problems. In this study, we made use of the gradient of the tangent plane distance function to improve the performance of Cuckoo Search (CS) algorithm, which is a promising nature-inspired stochastic global optimization method, for the calculation of phase stability analysis. The new modified algorithm, Gradient-Based Cuckoo Search (GBCS), was evaluated for solving several challenging phase stability problems. Its performance at different numerical effort levels and the effect of stopping criterion have been analyzed. GBCS was found to perform better than the original CS algorithm. In comparison with other stochastic optimization methods using an improvement objective function-based stopping criterion, GBCS proved to be the most reliable without any reduction in efficiency. © 2014 Elsevier B.V.

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