Made available in DSpace on 2014-10-09T12:31:30Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T13:59:33Z (GMT). No. of bitstreams: 1 01417.pdf: 15560137 bytes, checksum: 857fe87504571bc27557a261974b6971 (MD5) / Tese (Doutoramento) / IPEN/T / Universitaet Karlsruhe - Fakultaet fuer Maschinenbau

No abstract available. / Dissertation (MSc (Metallurgy))--University of Pretoria, 2006. / Materials Science and Metallurgical Engineering / unrestricted

Orientador: Mario de Jesus Mendes / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica / Made available in DSpace on 2018-08-07T19:04:30Z (GMT). No. of bitstreams: 1 Schmitz_JonesErni_D.pdf: 1427141 bytes, checksum: 5c994ce371331fd4c3ac0f3e3861f49b (MD5) Previous issue date: 2006 / Resumo: A simulação de processos é um componente fundamental de uma grande variedade de atividades de Engenharia de Processos, tais como a Otimização Online, o controle em Tempo Real, a Identificação, etc. O cálculo de Equilíbrio de Fases é uma atividade fundamental em qualquer simulação de processos de separação. O elevado tempo computacional deste cálculo provocado pela sua natureza iterativa pode criar incompatibilidades entre a atividade de simulação e as aplicações em tempo real que ela integra. O objetivo deste trabalho foi desenvolver um método alternativo simples, mas suficientemente preciso, para realizar os cálculos de equilíbrio de fases na simulação de processos de separação de sistemas complexos. Entende-se por tal, sistemas que apresentam problemas de Equilíbrio Líquido-Líquido e de Equilíbrio Líquido-Líquido-Vapor, como é o caso dos que possuem um Azeótropo Heterogêneo. Pelas suas propriedades, as Redes Neurais Artificiais surgem naturalmente como candidatas alternativas para esta tarefa. Como objeto de aplicação foram escolhidos dois sistemas que apresentam um azeótropo heterogêneo, o sistema binário acetato de etila - água e o sistema ternário etanol - acetato de etila - água. Para gerar os dados usados no treinamento das redes foi implementado um método convencional de cálculo de equilíbrio de fases, adequado à complexidade dos sistemas escolhidos, o método de Pham & Doherty. Para a resolução do problema da estabilidade de fases, a primeira etapa do cálculo do equilíbrio de fases, foram testados dois tipos de redes neurais artificiais (RNAs), as Redes Neurais Artificiais Probabilísticas (RNAPs) e os Perceptrons. Com os perceptrons foram encontradas dificuldades para atingir a precisão desejada, sendo necessário recorrer a perceptrons com várias camadas escondidas. Já as RNAPs apresentaram uma excelente precisão, embora a sua simulação seja mais lenta. Perceptrons simples de uma só camada escondida foram usados com êxito na solução da segunda etapa do cálculo de equilíbrio de fases, o problema da divisão de fases. Combinando as redes desenvolvidas para cada uma das etapas foi criada uma ferramenta que permite resolver qualquer problema de equilíbrio de fases para os sistemas estudados. A precisão dos resultados fornecidos pelas redes neurais é comparável à dos apresentados pelos métodos tradicionais, mas os cálculos do equilíbrio de fases feitos usando redes neurais foram mais rápidos. Pode-se concluir que as redes neurais artificiais constituem uma alternativa válida aos métodos tradicionais do cálculo do equilíbrio de fases baseados em equações de estado para sistemas complexos como os avaliados / Abstract: Process simulation is a basic component of different Process Engineering activities such as On-line Optimization, Model Predictive Control, Identification, etc. The calculation of Phase Equilibrium appears as a fundamental task in any simulation of a separation process. However, the high computational time due to the iterative nature of this calculation makes it oft unsuitable for use with real time process analysis and synthesis strategies. The objective of this work is to develop a simple but accurate method to perform the phase equilibrium calculations required to the study of the behavior of complex systems. As such we mind those systems who present liquid-liquid and vapor-liquid-liquid phase equilibrium problems, such as systems with a heterogeneous azeotrope do. Given their inherent ability to learn and recognize non-linear and highly complex relationships, artificial neural networks (ANNs) appear to be well suited for such a task. Two chemical systems, the binary ethyl acetate ¿ water and the ternary ethanol ¿ ethyl acetate ¿ water were chosen; both systems present a miscibility gap and a heterogeneous azeotrope. The data sets used to train the ANNs were computed using the method of Pham & Doherty. Two kinds of neural networks were tried to solve the phase stability problem, namely the probabilistic neural networks (PNNs) and the perceptrons. In order to attain an acceptable precision perceptrons had to be trained with several hidden layers. Even though, PNNs got slightly better results than the perceptrons. Simple perceptrons were able to deliver the required precision when trained to predict the compositions of phases in equilibrium. Coupling the ANNs trained for phase stability with those trained for phase division a tool was obtained that can solve any phase equilibrium problem for the two chosen systems. Predictions made with the use of neural networks were faster than those made using the traditional methods, and delivered comparable precision / Doutorado / Sistemas de Processos Quimicos e Informatica / Doutor em Engenharia Química

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21103号 / 工博第4467号 / 新制||工||1694(附属図書館) / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 田中 功, 教授 中村 裕之, 准教授 弓削 是貴 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Yes / The growth of cocrystalline phases continues to expand as a key area of crystal engineering research. Understanding the phase behavior of the material and controlling the crystalline form of the material from a solution-based route can be aided by the construction of a ternary phase diagram for the system. A range of methods exist for this process which display a variety of costs and time to achieve the final diagram. The application of quantitative NMR (qNMR) to this problem offers a fast analysis method to directly determine the solution composition of all species (coformers and solvent) and is demonstrated to successfully allow the construction of ternary diagrams with and without a cocrystal phase being formed for systems with high and low solubility.

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The thermodynamic and kinetic phenomena involved in the formation of sub-micron polyimide particles from polyimide/solvent/nonsolvent ternary systems were studied. A quantitative equilibrium approach was considered in the B-bonding studies of these ternary systems. The effect of H-bonding on the physical properties and phase behavior of the ternary systems was investigated. The critical requirements for the control of polymer particle size and size distribution during particle formation by precipitation from solution are discussed. It was found that the equilibrium constants, enthalpies and entropies of H-bonding in the ternary systems containing water can be obtained by analysis of the ^lH_NMR data. Using the calculated equilibrium constants, the determination of the concentration of H-bonds between N-methylpyrrolidinone (NHP) and water is possible. We found a very good correlation between the B-bond concentrations and the deviations of the specific volume and viscosity from ideal mixing in the NHP/water mixtures. / Ph. D.

No description available.