Application of particle swarm optimization to model the phase equilibrium of complex mixtures

Lazzús, Juan A.; Ponce, Alejandro A. Pérez; Chilla, L. O. Palma

doi:10.1016/j.fluid.2012.01.011

Cited by 16 publications

(17 citation statements)

References 14 publications

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“…7 shows the comparison of the performance of Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) [13] and HS method for parameter estimation using LS formulation. It is convenient to note that previous studies have reported a robust performance of DE [13], PSO [14,[32][33][34], SA [11,13,32] and GA [12,35] for solving global optimization problems in the modeling of phase equilibrium including parameter estimation problems. To directly examine and compare the performance of HS with those obtained for other stochastic methods using LS approach, we keep their numerical efforts the same via NFE and analyze the results obtained in terms of GSR.…”

Section: Performance Of Traditional Hs Methodsmentioning

confidence: 99%

On the capabilities and limitations of harmony search for parameter estimation in vapor–liquid equilibrium modeling

Bonilla-Petriciolet

2012

Fluid Phase Equilibria

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Section: Performance Of Traditional Hs Methodsmentioning

confidence: 99%

On the capabilities and limitations of harmony search for parameter estimation in vapor–liquid equilibrium modeling

Bonilla-Petriciolet

2012

Fluid Phase Equilibria

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“…Lazzus J. A. et al 57 utilized PSO to predict the phase equilibrium data of supercritical carbon dioxide and show that the PSO provides a good method to optimize the parameters with high accuracy. Ahmadi M. A. et al 58 applied unied PSO to train the feed forward ANN, the results demonstrate the effectiveness of the proposed model.…”

Section: Hybrid Anns Based On Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Solubility prediction of gases in polymers based on an artificial neural network: a review

Chen

et al. 2017

RSC Adv.

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As an important physical chemistry property, solubility is still a popular research topic. Its theoretical calculation method has developed rapidly. In particular, the artificial neural network (ANN) has attracted the attention of researchers because of its unique nonlinear processing ability. This review provides a brief explanation of the ANN approaches that are most commonly applied to predict gas solubility in polymers, and states the implementation principle, progress, and performance analysis of hybrid ANNs based on the intelligence algorithm. The prospect of solubility prediction based on current research trends is then proposed. This review attempts to analyze the solubility calculation method and provides an insight into and reference for the application of the artificial intelligence method in chemistry and material fields, and can expand in the future because of the increasing number of solubility prediction approaches being introduced.

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“…Therefore, to improve the performance of the RBF NN, an optimization of the RBF NN parameters is necessary. Researchers have discovered that many evolutionary algorithms, such as the genetic algorithm (GA), simulated annealing algorithm (SA), ant colony algorithm, and particle swarm optimization (PSO) algorithm, can be used for this optimization. The PSO algorithm is a global and advanced algorithm with a strong ability to search global optimum values.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self‐adaptive particle swarm optimization and a clustering method

Huang

Liu

et al. 2013

J of Applied Polymer Sci

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A novel model based on a radial basis function neural network (RBF NN), chaos theory, self-adaptive particle swarm optimization (PSO), and a clustering method is proposed to predict the gas solubility in polymers; this model is hereafter called CSPSO-C RBF NN. To develop the CSPSO-C RBF NN, the conventional PSO was modified with chaos theory and a selfadaptive inertia weight factor to overcome its premature convergence problem. The classical k-means clustering method was used to tune the hidden centers and radial basis function spreads, and the modified PSO algorithm was used to optimize the RBF NN connection weights. Then, the CSPSO-C RBF NN was used to investigate the solubility of N 2 in polystyrene (PS) and CO 2 in PS, polypropylene, poly(butylene succinate), and poly(butylene succinate-co-adipate). The results obtained in this study indicate that the CSPSO-C RBF NN was an effective method for predicting the gas solubility in polymers. In addition, compared with conventional RBF NN and PSO neural network, the CSPSO-C RBF NN showed better performance. The values of the average relative deviation, squared correlation coefficient, and standard deviation were 0.1282, 0.9970, and 0.0115, respectively. The statistical data demonstrated that the CSPSO-C RBF NN had excellent prediction capabilities with a high accuracy and a good correlation between the predicted values and the experimental data.

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Application of particle swarm optimization to model the phase equilibrium of complex mixtures

Cited by 16 publications

References 14 publications

On the capabilities and limitations of harmony search for parameter estimation in vapor–liquid equilibrium modeling

On the capabilities and limitations of harmony search for parameter estimation in vapor–liquid equilibrium modeling

Solubility prediction of gases in polymers based on an artificial neural network: a review

Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self‐adaptive particle swarm optimization and a clustering method

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