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Hybrid Particle Swarm-Ant Colony Algorithm to Describe the Phase Equilibrium of Systems Containing Supercritical Fluids with Ionic Liquids

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Communications in Computational Physics 2013年第6期14卷 107-125页

作者： juan a.lazzus Departamento de Fisica Universidad de La SerenaCasilla 554La SerenaChile

Based on biologically inspired algorithms,a thermodynamic model to describe the vapor-liquid equilibrium of binary complex mixtures containing supercritical fluids and ionic liquids,is *** Peng-Robinson equation of state with the Wong-Sandler mixing rules are used to evaluate the fugacity coefficient on the ***,a hybrid particle swarm-ant colony optimization was used to minimize the difference between calculated and experimental bubble pressure,and calculate the binary interaction parameters for the excess Gibbs free energy of all systems *** are carried out in nine systems with imidazolium-based ionic *** results show that the bubble pressures were correlated with low deviations between experimental and calculated *** deviations show that the proposed hybrid algorithm is the preferable method to describe the phase equilibrium of these complex mixtures,and can be used for other similar systems.

关键词： Particle swarm optimization ant colony optimization vapor-liquid equilibrium ionic liquids supercritical fluids Peng-Robinson equation of state

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Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution ＋ Neural Network ＋ Particle Swarm Optimization

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Chinese Journal of Chemical Engineering 2010年第5期18卷 817-823页

作者： juan A. lazzus Department of Physics University of La Serena Casilla 554 La Serena Chile

The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).

关键词： flash point group contribution method artificial neural networks particle swarm optimization property estimation

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Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds

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Chinese Journal of Chemical Physics 2009年第1期22卷 19-26页

作者： juan A. lazzus Departamento de Fisica Universidad de La Serena Casilla 554 La Serena Chile

The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship （QSPR） parameters in quantum chemistry. Eleven descriptors that reflect the intermolecular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab. Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellent alternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.

关键词： Melting point Quantitative structure-property relationship Artificial neural network Quantum chemistry

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