Injection moulding process optimization for microstructured parts production

Ao longo das últimas décadas, a micromoldação (u-moldação) por injeção de termoplásticos ganhou um lugar de destaque no mercado de equipamentos eletrónicos e de uma ampla gama de componentes mecânicos. No entanto, quando o tamanho do componente diminui, os pressupostos geralmente aceites na moldação por injeção convencional deixam de ser válidos para descrever o comportamento reológico e termomecânico do polímero na microimpressão. Por isso, a compreensão do comportamento dinâmico do polímero à escala micro bem como da sua caraterização, análise e previsão das propriedades mecânicas exige uma investigação mais alargada. O objetivo principal deste programa doutoral passa por uma melhor compreensão do fenómeno físico intrínseco ao processo da μ-moldação por injeção. Para cumprir com o objetivo estabelecido, foi efetuado um estudo paramétrico do processo de μ-moldação por injeção, cujos resultados foram comparados com os resultados obtidos por simulação numérica. A caracterização dinâmica mecânica das μ-peças foi efetuada com o objetivo de recolher os dados necessários para a previsão do desempenho mecânico das mesmas, a longo prazo. Finalmente, depois da calibração do modelo matemático do polímero, foram realizadas análises estruturais com o intuito de prever o desempenho mecânico das μ-peças no longo prazo. Verificou-se que o desempenho mecânico das μ-peças pode ser significativamente afetado pelas tensões residuais de origem mecânica e térmica. Estas últimas, resultantes do processo de fabrico e das condições de processamento, por isso, devem ser consideradas na previsão do desempenho mecânico e do tempo de serviço das u-moldações.

NMR in the characterization of heavy residual procedural streams

The main objective of this work was to monitor a set of physical-chemical properties of heavy oil procedural streams through nuclear magnetic resonance spectroscopy, in order to propose an analysis procedure and online data processing for process control. Different statistical methods which allow to relate the results obtained by nuclear magnetic resonance spectroscopy with the results obtained by the conventional standard methods during the characterization of the different streams, have been implemented in order to develop models for predicting these same properties. The real-time knowledge of these physical-chemical properties of petroleum fractions is very important for enhancing refinery operations, ensuring technically, economically and environmentally proper refinery operations. The first part of this work involved the determination of many physical-chemical properties, at Matosinhos refinery, by following some standard methods important to evaluate and characterize light vacuum gas oil, heavy vacuum gas oil and fuel oil fractions. Kinematic viscosity, density, sulfur content, flash point, carbon residue, P-value and atmospheric and vacuum distillations were the properties analysed. Besides the analysis by using the standard methods, the same samples were analysed by nuclear magnetic resonance spectroscopy. The second part of this work was related to the application of multivariate statistical methods, which correlate the physical-chemical properties with the quantitative information acquired by nuclear magnetic resonance spectroscopy. Several methods were applied, including principal component analysis, principal component regression, partial least squares and artificial neural networks. Principal component analysis was used to reduce the number of predictive variables and to transform them into new variables, the principal components. These principal components were used as inputs of the principal component regression and artificial neural networks models. For the partial least squares model, the original data was used as input. Taking into account the performance of the develop models, by analysing selected statistical performance indexes, it was possible to conclude that principal component regression lead to worse performances. When applying the partial least squares and artificial neural networks models better results were achieved. However, it was with the artificial neural networks model that better predictions were obtained for almost of the properties analysed. With reference to the results obtained, it was possible to conclude that nuclear magnetic resonance spectroscopy combined with multivariate statistical methods can be used to predict physical-chemical properties of petroleum fractions. It has been shown that this technique can be considered a potential alternative to the conventional standard methods having obtained very promising results.