Structural and functional characterization of ModA and TupA proteins belonging to the molybdate and tungstate transport system from desulfovibrio alaskensis G20

Fundação para a Ciência e Tecnologia - EXPL/BBB-BEP/0274/2012

Matching CO2 large point sources and potential geological storage sites in mainland Portugal

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia do Ambiente, Perfil Gestão e Sistemas Ambientais

PV inverters for module level applications

Dissertação para obtenção do Grau de Mestre em Energias Renováveis – Conversão Eléctrica e Utilização Sustentáveis

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Noble energy oil & Gas

Equity research report

Bioremediation and CO2 scavenging using molybdenum-containing enzymes

Carbon dioxide valorization, will not only help to relieve the greenhouse effect but might also allow us to transform it in value-added chemicals that will help overcoming the energy crisis. To accomplish this goal, more research that focus on sequestering CO2 and endeavors through a carbon-neutral or carbon-negative strategy is needed in order to handle with the dwindling fossil fuel supplies and their environmental impact. Formate dehydrogenases are a promising means of turning CO2 into a biofuel that will allow for a reduction of greenhouse gas emissions and for a significant change to the economic paramount. The main objective of this work was to assess whether a NAD+-independent molybdenum-containing formate dehydrogenase is able to catalyze the reduction of CO2 to formate. To achieve this, a molybdenum-containing formate dehydrogenase was isolated from the sulfate reducing bacteria Desulfovibrio desulfuricans ATCC 27774. Growth conditions were found that allowed for a greater cellular mass recovery and formate dehydrogenase expression. After growth trials, kinetic assays for formate oxidation and CO2 reduction were performed and kinetic parameters determined. For the formate oxidation reaction, a KM of 49 μM and a turnover constant of 146 s-1 were determined. These kinetic parameters are in agreement with those determined by Mota, et al. (2011). Finally, we found that this molybdenum-containing enzyme was able to catalyze the reduction of CO2 to formate with a turnover constant of 4.6 s-1 and a KM of 13 μM. For the first time a NAD+-independent molybdenum-containing formate dehydrogenase was found to catalyze CO2 reduction, allowing its use as a biocatalyst in energetically efficient CO2 fixation processes that can be directed towards bioremediation or as an alternative and renewable energy source. Characterizing these enzymes may lead to the development of more efficient synthetic catalysts, make them readily available and more suited for practical applications.

Clearing the cloudy crystal balls: Hybrid modelling for energy and climate change mitigation scenarios – A case study for Portugal

Dissertação para obtenção do Grau de Doutor em Ambiente

An agent-based evolutionary approach for manufacturing system layout design

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Design of future distribution grids

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

A random access MAC protocol for MPR satellite networks

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

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.