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Contém referências bibliográficas
Bibliografia : p. 588
Relatório do projeto final de licenciatura em Design de Moda e Têxtil
Contém referências bibliográficas
The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008), fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions) were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression) and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines) were tested. Classification and regression trees (CART) was the most accurate model for the prediction of pulp ISO brightness (R = 0.85). The other parameters could be predicted with fair results (R = 0.64–0.75) by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and
pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.
A tool to predict the tensile properties of cork was applied in order to be used for material and application selection. The mechanical behaviour of cork under tensile stress was determined in the tangential and axial direction. Cork planks of two commercial quality classes were used and samples were taken at three radial positions in the planks.For the construction of the predictive model, nine properties were measured: mechanical properties (Young’s modulus, fracture stress and fracture strain) and the physical properties (porosity, number of pores, density, approximation of the pores to elliptical and circular shape and distance to the nearest pore). The aim of this research work was to predict the mechanical properties from the physical properties using neural networks.Initially, the problem was approached as a regression problem, but the poor correlation coefficients obtained made the authors define a classification problem. The criterion used for the classification problem was the test error rate, training the neural network with a variety of neurons in the hidden layer until the minimum error was achieved. The influence of each individual variable was also studied in order to evaluate their importance for the prediction of the mechanical properties.The results show that the Young’s modulus and fracture stress can be predicted with an error rate in test of 10.6 and 10.2 %, respectively, being the measure of the approximation of the pores to elliptical shape avoidable. Regarding the fracture strain, its prediction from physical properties implies an excessive error.
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In this study, the accuracy of mathematical techniques such as multiple linear regression, clustering, decision trees (CART) and neural networks was evaluated to predict Young’s modulus, compressive stress at 30% strain and instantaneous recovery velocity of cork. Physical properties, namely test direction, density, porosity and pore number, as well as test direction were used as input. The better model was achieved when a classification problem was performed. Only compressive stress at 30% strain can be predicted with neural networks with an error rate of about 20%. The prediction of Young’s modulus and instantaneous recovery velocity led to unacceptably high error rates due to the heterogeneity of the material.
The aim of this work is develop a tool based on neural networks to predict the botanical origin of honeys using physical and chemical parameters. The managed database consists of 49 honey samples of 2 different classes: monofloral (almond, holm oak, sweet chestnut, eucalyptus, orange, rosemary, lavender, strawberry trees, thyme, heather, sunflower) and multifloral. The moisture content, electrical conductivity, water activity, ashes content, pH, free acidity, colorimetric coordinates in CIELAB space (L(∗), a(∗), b(∗)) and total phenols content of the honey samples were evaluated. Those properties were considered as input variables of the predictive model. The neural network is optimised through several tests with different numbers of neurons in the hidden layer and also with different input variables. The reduced error rates (5%) allow us to conclude that the botanical origin of honey can be reliably and quickly known from the colorimetric information and the electrical conductivity of honey.
