Contributions à la paleontology du Miocène moyen continental du basin du tage

The lignite-clays of Póvoa de Santarém dated as Upper «Vindobonian» (mammalian zone MN6), fielded abundant remains of animals and plants (spores, pollens, seeds, etc.). The forms identified are indicative of several environments. Plants, either aquatic or belonging to swampy areas are predominant (Nuphar sp., Sparganium sp., Stratiotes kaltennordheimensis, cf. Ranunculus sp.). There are also remains of plants characteristiques of a humid rather than a swampy soil such as Polypodiaceae, Myrica ceriferiformis, Toddalia maii, Spirematospermum wetzeleri. The genera Vitis and Ephedra, although rare, point fowards the existence of drier regions in the neighbourhood. The presence of polens such as Picea indicate the presence at some distance less warm upland forest areas.

Contributions à la paleontology du Miocène moyen continental du basin du Tage; (V) les végètaux de Póvoa de Santarém (note préliminaire)

The lignite-clays of Póvoa de Santarém dated as Upper «Vindobonian» (mammalian zone MN6), fielded abundant remains of animals and plants (spores, pollens, seeds, etc.). The forms identified are indicative of several environments. Plants, either aquatic or belonging to swampy areas are predominant (Nuphar sp., Sparganium sp., Stratiotes kaltennordheimensis, cf. Ranunculus sp.). There are also remains of plants characteristiques of a humid rather than a swampy soil such as Polypodiaceae, Myrica ceriferiformis, Toddalia maii, Spirematospermum wetzeleri. The genera Vitis and Ephedra, although rare, point fowards the existence of drier regions in the neighbourhood. The presence of polens such as Picea indicate the presence at some distance less warm upland forest areas.

Responses of higher plants to boron deficiency

Dissertation presented to obtain the degree of Doctorate in Biochemistry by Instituto de Tecnologia Química e Biológica of Universidade Nova de Lisboa

Modelling of the kinetics of municipal solid waste composting in full-scale mechanical-biological treatment plants

A thesis submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy in Sanitary Engineering in the Faculty of Sciences and Technology of the New University of Lisbon

Risk-based decision support system for life cycle management of industrials plants

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

Evaluation of neuroprotective potential of plyphenols derived from Portuguese native plants: Juniperus sp. and Rubus sp.

Dissertation presented to obtain the Ph.D degree in Biochemistry

Regulation of the rice gene OsRMC under salt stress: identification and functional characterization of novel transcription factors

Dissertation presented to obtain the Ph.D degree in Biology

Study on the valorization routes of ashes from thermoelectric power plants working under mono-and co-combustion regimes

Thesis submitted to obtain the Doctoral degree in Energy and Bioenergy

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.