Traditional algarvian distillats and liqueurs historic scientific aspects

All the evidence indicates that distillation and liqueurs preparation began in Monchique mountain because this place was pointed as a possible capital of the oldest population of Algarve and an important Arabic village (Barreto, 1972: 19). It was possible to find lots of vestiges like the alembic produced by Arabic population near the X century (Telo, 1988: 77). Traditionally the Algarvian people produce the Arbutus unedo L., fig, carob, grape distillates. At the same time they produce liqueur-using maceration of parts of plants or fruits in some kinds of distillates. Most of the work about Algarvian distillates started by studying the basic compounds of Arbutus unedo spirits by gas chromatography (GC) and mass spectrometry (MS) as well as other physical-chemical properties. In a second phase aged distillates were studied by their phenolic compounds evolution using high resolution liquid chromatography (HPLC). Volatile compounds of traditional liqueurs were identified by head space micro extraction solid phase (HE-SPME) and also analysed by gas chromatography mass spectrometry (GC-MS) and when possible confirmed with standards. Total phenols were determined by Folin-Ciocalteur method. Flavenoids were studied by high performance liquid chromatography (HPLC). Sensorial analysis was also done in every drink studies. The results showed that the arbutus distillate doesn’t present a high level of methanol according to the current legislation. The excesses of acidity or ethyl acetate present normal values when the fermentation is well done (Galego, et al. 1995: 341; Galego, et al. 1995: 685). During the aging process, the colour of spirits tend to become darker, the colour changes occurred more rapidly in the arbutus spirits located in cellars with higher temperatures (Galego, et al. 2001: 432). In the sensory evaluation of samples aged during 12 months into 50 L medium toasting level oak wood barrels, panellists considered that samples of arbutus spirit had too much wood flavour and they were not able to detect the characteristic aroma of arbutus fruit (Galego, et al., 2001: 183). Differences in liqueurs were observed using HS-SPME-GC, HS-SPME-GC-MS or HPLC analysis and this observation was confirmed by a sensorial panel (Galego, et al. 2003: 60).

A ilha de Moçambique durante a Idade Moderna

Dissertação de Mestrado, História da Arte Portuguesa, Faculdade de Ciências Humanans e Sociais, Universidade do Algarve, 2007

Intelligent predictive control for thermal comfort and energy savings in public buildings

This talk addresses the problem of controlling a heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identifed by means of a multi-objective genetic algorithm [1]; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, with a special emphasis on a fast and accurate computation of the PMV indices [2]. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer [3] and winter [4] conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.

Predictive control of HVAC systems in buildings

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015