Identification of atmospheric pollutants control mechanisms during co-combustion of coal and non-toxic wastes

Doctoral dissertation for Ph.D. degree in Sustainable Chemistry

Fossil fuel dependence and interfuel substitution in the electricity sector of Western European countries between 1990-2011

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

An intertemporal pricing model for CO2 allowances: The impact of the clean development mechanism

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Avaliação do ciclo de vida do sistema de produção soja-girassol no cerrado Brasileiro.

RESUMO: O girassol é uma importante cultura na região de Parecis, no Cerrado brasileiro. Em 2014, a região respondeu pela produção de 232.700 t de grãos, 45% da produção nacional. A produção de girassol provém principalmente de um sistema que tem a soja como cultura principal. A associação entre soja e girassol pode reduzir impactos ambientais devido ao uso compartilhado de recursos. Este estudo desenvolveu uma Avaliação de Ciclo de Vida (ACV) ?do berço ao túmulo? do sistema de produção soja-girassol usado na região de Parecis e comparou seu perfil ambiental ao das monoculturas de soja e girassol. Impactos relacionados ao uso do solo (emissões da mudança de uso da terra e calagem) por cada cultura foram alocados em função do tempo de ocupação do solo. O sistema soja-girassol teve impactos ambientais menores em todas as categorias de impacto quando comparado à monocultura de soja e girassol, com o mesmo rendimento. Reduções importantes foram observadas em ?Mudança do Clima?, ?Acidificação Terrestre? e ?Formação de Material Particulado?. ABSTRACT: Sunflower is an important crop in Parecis region of the Brazilian Cerrado. In 2014 the region accounted for the production of 232,700 tons of sunflower grain, 45% of national production. Sunflower production comes mostly from a system that has soybean as the main crop. The association of soybean and sunflower can reduce environmental impacts due to shared use of resources. This study performed a ?cradle to gate? Life Cycle Assessment (LCA) of the soybean-sunflower production system used in Parecis region and compared its environmental profile to that of the monoculture of these two crops. Impacts related to the use of soil (land use change emissions and liming) by each crop were evaluated according to time of soil occupation criterion. Soybean-sunflower system had lower environmental impacts on every impact category comparing to soybean and sunflower monoculture with the same yield. Important reduction were observed on ?Climate change?, ?Terrestrial acidification? and ?Particulate matter formation? categories.

The influence of use additives on nitrous oxide emission during swine slurry composting.

use of additives (Mg/P and nitrification inhibitor dicyandiamide - DCD), on nitrous oxide emission during swine slurry composting. The experiment was run in duplicate; the gas was monitored for 30 days in different treatments (control, DCD, Mg/P and DCD + Mg/P). Nitrous oxide emissions rate (mg of N2O-N.day-1) and the accumulated emissions were calculated to compare the treatments. Results has shown that emissions of N-N2O were reduced by approximately 70, 46 and 96% through the additions of DCD, MgCl2.6H2O + H3PO4 and both additives, respectively, compared to the control. Keywords Composting; swine slurry; additives; nitrous

Potential for ORC application in the Portuguese manufacturing industry

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas Ambientais

Carbon prices. Dynamic analysis of European and Californian markets

Dissertação para obtenção do Grau de Doutor em Alterações Climáticas e Políticas de Desenvolvimento Sustentável

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

Ageing on commercial automotive catalysts, a structure reactivity study

Increasingly stringed regulations for diesel engine emissions have a significant impact on the required efficiency of DOC. Lowered DOC oxidation efficiency due to thermal aging effects influences the efficiency of the exhaust aftertreatment systems downstream of the DOC. In this work carried out in the Jean Le Rond d’Alembert Institute the effect of hydrothermal aging on the reactivity and structure of a commercial DOC was investigated. The characterization of the catalytic performance was carried out on a synthetic gas bench using carrots catalyst under conditions close to the realistic conditions i.e. using a synthetic gas mixture, representative of the exhaust gases from diesel engines. Different structural characterization techniques were performed: textural and morphological proprieties were analyzed by BET and TEM, the characterization of the presented crystallographic phases was performed by DRX and the determination of the number of reducible species was possible by TPR. TEM results shown, an increase of the metal particle size with the aging caused by the agglomeration of metal particles, revealing the presence of metal sintering. DRX results also suggest the presence of support sintering. Furthermore, DRX and BET results unexpectedly reveal that the most drastic aging conditions used actually activated the catalyst surface. As expected, the aging affected negatively the catalyst performance on the oxidation of methane and CO, however an improvement of the NO oxidation performance with the aging was observed. Nevertheless, for the aging conditions used, catalytic activity results show that the influence of aging in DOC performance was not significant, and therefore, more drastic aging conditions must be used.

Provide instructions and resources for assessment and training in earth building: the Pirate project

This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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.

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.

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.

Efficiency optimization of the transportation supply chain activity: the maritime option for Volkswagen Autoeuropa

Field Lab of Entrepreneurial Innovative Ventures

Automated quantitative analysis of MCC-IMS spectra

Ion Mobility Spectrometry coupled with Multi Capillary Columns (MCC -IMS) is a fast analytical technique working at atmospheric pressure with high sensitivity and selectivity making it suitable for the analysis of complex biological matrices. MCC-IMS analysis generates its information through a 3D spectrum with peaks, corresponding to each of the substances detected, providing quantitative and qualitative information. Sometimes peaks of different substances overlap, making the quantification of substances present in the biological matrices a difficult process. In the present work we use peaks of isoprene and acetone as a model for this problem. These two volatile organic compounds (VOCs) that when detected by MCC-IMS produce two overlapping peaks. In this work it’s proposed an algorithm to identify and quantify these two peaks. This algorithm uses image processing techniques to treat the spectra and to detect the position of the peaks, and then fits the data to a custom model in order to separate the peaks. Once the peaks are separated it calculates the contribution of each peak to the data.