Thermal performance in a conventional fireplace and in an inset appliance

The objective of this study is to compare the performance of the equipment currently employed in the domestic heating with firewood, the conventional fireplace and the inset appliance with close firedoors. For such, it was followed the European Standard EN13229:2001/A2:2004. Efficiency and heat output is determined and the major heat losses that penalize the appliance performance are identified and calculated. Tests in laboratory were developed in two inset appliances, and tests in situ in one conventional fireplace. One of the appliances uses only staging as primary air, and the other only grate air In inset appliances, with a heat output near 10 kW, the average efficiency varies between 67% and 73%, while in a conventional fireplace that value lies at 30%. In all these devices the major losses take place as sensible heat in the flue gases, 23% to 30 % in inset appliances and above 50% in a conventional fireplace. The second most important heat loss happens by chemical losses in the flue gases. It takes values near 17% in a conventional fireplace and may be disregarded in an inset appliance.

Bio-removal of toxic metals by metal resistant anaerobic bacteria: molecular characterization and performance studies

Tese de dout., Ciências Biotecnológicas (Biotecnologia Ambiental), Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2010

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%.