Thermodiffusion is one of the mechanisms in transport phenomena in which molecules are transported in a multicomponent mixture driven by temperature gradients. Thermodiffusion in associating mixtures presents a larger degree of complexity than non-associating mixtures, since the direction of flow in associating mixtures may change with variations in composition and temperature. In this study a new activation energy model is proposed for predicting the ratio of evaporation energy to activation energy. The new model has been implemented for prediction of thermodiffusion for acetone-water, ethanol-water and isopropanol-water mixtures. In particular, a sign change in the thermodiffusion factor for associating mixtures has been predicted, which is a major step forward in modeling of thermodiffusion for associating mixtures. In addition, a new model for the prediction of thermodiffusion coefficients for linear chain hydrocarbon binary mixtures is proposed using the theory of irreversible thermodynamics and a kinetics approach. The model predicts the net amount of heat transported based on an available volume for each molecule. This model has been found to be the most reliable and represents a significant improvement over the earlier models. Also a new approach to predicting the Soret coefficient in binary mixtures of linear chain and aromatic hydrocarbons using the thermodynamics of irreversible processes is presented. This approach is based on a free volume theory which explains the diffusivity in diffusion-limited systems. The proposed model combined with the Shukla and Firoozabadi model has been applied to predict the Soret coefficient for binary mixtures of toluene and n-hexane, and benzene and n-heptane. Comparisons of theoretical results with experimental data show a good agreement. The proposed model has also been applied to estimate thermodiffusion coefficients of binary mixtures of n-butane & carbon dioxide and n-dodecane & carbon dioxide at different temperature. The results have also been incorporated into CFD software FLUENT for 3-dimensional simulations of thermodiffusion and convection in porous media. The predictions show the thermodiffuison phenomenon is dominant at low permeabilities (0.0001 to 0.01), but as the permeability increases convection plays an important role in establishing a concentration distribution. Finally, the activation energy in Eyring’s viscosity theory is examined for associating mixtures. Several methods are used to estimate the activation energy of pure components and then extended to mixtures of linear hydrocarbon chains. The activation energy model based on alternative forms of Eyring’s viscosity theory is implemented to estimate the thermodiffusion coefficient for hydrocarbon binary mixtures. Comparisons of theoretical results with the available thermodiffusion coefficient data have shown a good performance of the activation energy model.

Thermodiffusion is one of the mechanisms in transport phenomena in which molecules are transported in a multicomponent mixture driven by temperature gradients. Thermodiffusion in associating mixtures presents a larger degree of complexity than non-associating mixtures, since the direction of flow in associating mixtures may change with variations in composition and temperature. In this study a new activation energy model is proposed for predicting the ratio of evaporation energy to activation energy. The new model has been implemented for prediction of thermodiffusion for acetone-water, ethanol-water and isopropanol-water mixtures. In particular, a sign change in the thermodiffusion factor for associating mixtures has been predicted, which is a major step forward in modeling of thermodiffusion for associating mixtures. In addition, a new model for the prediction of thermodiffusion coefficients for linear chain hydrocarbon binary mixtures is proposed using the theory of irreversible thermodynamics and a kinetics approach. The model predicts the net amount of heat transported based on an available volume for each molecule. This model has been found to be the most reliable and represents a significant improvement over the earlier models. Also a new approach to predicting the Soret coefficient in binary mixtures of linear chain and aromatic hydrocarbons using the thermodynamics of irreversible processes is presented. This approach is based on a free volume theory which explains the diffusivity in diffusion-limited systems. The proposed model combined with the Shukla and Firoozabadi model has been applied to predict the Soret coefficient for binary mixtures of toluene and n-hexane, and benzene and n-heptane. Comparisons of theoretical results with experimental data show a good agreement. The proposed model has also been applied to estimate thermodiffusion coefficients of binary mixtures of n-butane & carbon dioxide and n-dodecane & carbon dioxide at different temperature. The results have also been incorporated into CFD software FLUENT for 3-dimensional simulations of thermodiffusion and convection in porous media. The predictions show the thermodiffuison phenomenon is dominant at low permeabilities (0.0001 to 0.01), but as the permeability increases convection plays an important role in establishing a concentration distribution. Finally, the activation energy in Eyring’s viscosity theory is examined for associating mixtures. Several methods are used to estimate the activation energy of pure components and then extended to mixtures of linear hydrocarbon chains. The activation energy model based on alternative forms of Eyring’s viscosity theory is implemented to estimate the thermodiffusion coefficient for hydrocarbon binary mixtures. Comparisons of theoretical results with the available thermodiffusion coefficient data have shown a good performance of the activation energy model.

[pt] A utilização de modelos de inteligência computacional para tarefas de previsão Multi-Step de séries temporais tem apresentado resultados que permitem considerar estes modelos como alternativas viáveis para este tipo de problema. Baseados nos requerimentos computacionais e a melhora de desempenho, recentemente novas áreas de pesquisa têm sido apresentadas na comunidade científica. Este é o caso do Reservoir Computing, que apresenta novos campos de estudo para redes neurais do tipo recorrentes, as quais, no passado, não foram muito utilizados devido à complexidade de treinamento e ao alto custo computacional. Nesta nova área são apresentados modelos como Liquid State Machine e Echo State Networks, que proporcionam um novo entendimento no conceito de processamento dinâmico para redes recorrentes e propõem métodos de treinamento com baixo custo computacional. Neste trabalho determinou-se como foco de pesquisa a otimização de parâmetros globais para o projeto das Echo State Networks. Embora as Echo State Networks sejam objeto de estudo de pesquisadores reconhecidos, ainda apresentam comportamentos desconhecidos, em parte pela sua natureza dinâmica, mas também, pela falta de estudos que aprofundem o entendimento no comportamento dos estados gerados. Utilizando como fundamento o modelo Separation Ratio Graph para análise do desempenho, é proposto um novo modelo, denominado ESN-GA-SRG, que usa como base redes ESN com otimização de parâmetros globais utilizando GA e seleção de topologias para Reservoir por meio de análise de estados empregando SRG. O desempenho deste novo modelo é avaliado na previsão das 11 séries que compõem a versão reduzida do NN3 Forecasting Competition e em 36 séries da competição M3, selecionadas segundo características de periodicidade na amostragem, assimetria, sazonalidade e estacionaridade. O desempenho do modelo ESN-GA-SRG na previsão dessas séries temporais foi superior na maioria dos casos, com significância estatística, quando comparado com outros modelos da literatura. / [en] The use of computational intelligence models for Multi-Step time series prediction tasks has presented results that allow us to consider these models as viable alternatives for this type of problem. Based on computational requirements and performance improvement, new areas of research have recently been presented in the scientific community. This is the case of Reservoir Computing, which presents new fields of study for recurrent-type neural networks, which in the past were not widely used because of training complexity and high computational cost. In this new area are presented models such as Liquid State Machine and Echo State Networks, which provide a new understanding of the concept of dynamic processing for recurring networks and propose methods of training with low computational cost. In this work, we determined the optimization of global parameters for the Echo State Networks project. Although Echo State Networks are the object of study by recognized researchers, they still present unknown behavior, partly due to their dynamic nature, but also due to the lack of studies that deepen behavior understanding of the generated states. Based on the Separation Ratio Graph model for performance analysis, a new model, called ESN-GA-SRG, is proposed, which uses ESN networks with global parameter optimization using GA and selection of topologies for Reservoir through analysis of States employing SRG. The performance of this new model is evaluated to forecast the 11 series that made up the reduced version of the NN3 Forecasting Competition and for 36 series of the M3 competition, selected according to characteristics of periodicity in sampling, asymmetry, seasonality and stationary. The performance of the ESN-GA-SRG model in predicting these time series was superior in most cases, with statistical significance when compared with other models in the literature.