During the course of this thesis the UNIFAC method (group-based method) was regressed to individual Px(T) binary datasets, and the results are compared to the regression results using the Wilson, NRTL, and UNIQUAC equations (component-based models). It is shown that these component-based methods best represent the experimental data when the comparisons are restricted to those systems defined by only two UNIFAC maingroups. For those systems requiring three or more maingroups, however, the regressions using the UNIFAC method (i.e. the group-based approach) are shown to provide the best reproducible results. Evaluations are also presented on the ability of the UNIFAC and mod. UNIFAC (Do.) methods to reproduce experimental activity coefficients at infinite dilution for single and co-solvent systems. For the case of single solvent-systems the newly developed MRR combinatorial expression (Moller, 2010) is evaluated as a direct combinatorial replacement for both methods, although it was originally developed only for estimating activity coefficients at infinite dilution in alkane-solvents. Overall, it is shown that the best results are obtained using the mod. UNIFAC (Do.) method, and that poor results are obtained when trying to use the MRR combinatorial as a direct combinatorial replacement in either method (for systems other than alkane-solvents). Given the favourable results obtained using the mod. UNIFAC (Do.) method, the model was used to generate pseudo data points at multiple temperatures for regression using the NRTL equation, where parameters quadratic in temperature were fitted. It is shown that one may introduce unnecessary errors when translating these predictions into the model parameters of the NRTL equation. In order to eliminate these potential “losses in translation,” a new liquid activity coefficient model/methodology is being proposed. Instead of using group contribution methods as second-choice data generators, it is proposed that these predictive methods be employed in a more direct fashion in process simulations. Instead of regressing experimental data using component-based methods such as NRTL and Wilson, the error in the predicted results are regressed by layering one of these methods on top of a group contribution method like mod. UNIFAC (Do.). This is the fundamental idea behind the proposed hybrid methodology/models. Results are presented for two hybrid models, where the NRTL and Wilson equations are used to correct for the predictions made using the mod. UNIFAC (Do.) method. These methods are being called NRTL-FAC(Do.) and Wilson-FAC(Do.) respectively. In most cases, it is shown that the overall regression results using these new models are as good as or better than the individual models making them up. All experimental data used in this dissertation was obtained from the Dortmund Data Bank (DDBST Software and Separation Technology GmbH, 2009), and all predictions made using the UNIFAC and mod. UNIFAC (Do.) methods were calculated using the Consortium parameters (The UNIFAC Consortium, 2008). / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2011.

Submitted by Livia Mello (liviacmello@yahoo.com.br) on 2016-10-14T12:23:25Z No. of bitstreams: 1 DissNOG.pdf: 2677969 bytes, checksum: 0a4b7cc57095da998c310a3abfe6a92c (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:45:34Z (GMT) No. of bitstreams: 1 DissNOG.pdf: 2677969 bytes, checksum: 0a4b7cc57095da998c310a3abfe6a92c (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:45:40Z (GMT) No. of bitstreams: 1 DissNOG.pdf: 2677969 bytes, checksum: 0a4b7cc57095da998c310a3abfe6a92c (MD5) / Made available in DSpace on 2016-11-08T18:45:48Z (GMT). No. of bitstreams: 1 DissNOG.pdf: 2677969 bytes, checksum: 0a4b7cc57095da998c310a3abfe6a92c (MD5) Previous issue date: 2016-08-01 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / This dissertation is intended to be used by high school teachers and contains a set of concepts that leads to the understanding of entropy and its applications in the classroom. It begins with the definition of thermodynamics, through the study of the first and second law, showing the relevance of choice of the thermodynamic system, the nature of its boundaries, the variables of the thermodynamic transformations, ending with Carnot cycle and the reversibility and irreversibility concepts. The ideas of microstates and macrostates is introduced with the use of models easy to be used in the classroom, enabling the students to obtain quantitative results for disorder and therefore for entropy. The increase of entropy is associated with irreversible transformations and with energy degradation leading to effects on life and on the environment. The purpose of this dissertation is to provide tools for teachers, so that the Carnot cycle and the entropy concept can be used more significantly, with a broader view allowing teachers and high school students to have a better understanding of natural phenomena appreciating to learn new concepts. / Essa dissertação é voltada para os professores de ensino médio e contém um conjunto de conceitos que leva ao entendimento de entropia e sua aplicação em sala de aula. Tem início com a definição da termodinâmica, passando pelo estudo da primeira e da segunda lei da termodinâmica, mostrando a relevância da clara escolha do sistema termodinâmico de interesse, especificando a natureza das fronteiras do sistema, das variáveis envolvidas nas transformações, terminando com ciclo de Carnot e com os conceitos de reversibilidade e irreversibilidade. As ideias de microestados e macroestados são introduzidas, com uso de modelos de fácil utilização em sala de aula, possibilitando uma formulação quantitativa do conceito de desordem e de entropia. Conclui-se associando o aumento de entropia nas transformações irreversíveis com a degradação energética, que influi no desenvolvimento da vida e no meio ambiente, demonstrando o caráter probabilístico da entropia. O intuito dessa dissertação é fornecer ferramentas para que o ensino do ciclo de Carnot possa ser feito de maneira mais significativa, mais participativa, levando o estudante a gostar de aprender novos conceitos.