Constituição e transcendência: os casos dos regimes comunitários do entre-guerras

Tede de Doutoramento, na especialidade de Ciências Políticas apresentada à FDUNL

Determination of the most suitable oil pipeline route using GIS least cost path analysis

The Keystone XL has a big role for transforming Canadian oil to the USA. The function of the pipeline is decreasing the dependency of the American oil industry on other countries and it will help to limit external debt. The proposed pipeline seeks the most suitable route which cannot damage agricultural and natural water recourses such as the Ogallala Aquifer. Using the Geographic Information System (GIS) techniques, the suggested path in this study got extremely high correct results that will help in the future to use the least cost analysis for similar studies. The route analysis contains different weighted overlay surfaces, each, was influenced by various criteria (slope, geology, population and land use). The resulted least cost path routes for each weighted overlay surface were compared with the original proposed pipeline and each displayed surface was more effective than the proposed Keystone XL pipeline.

Strategic account management: the path to gain customer loyalty

The account management field works closely with the sales team, serving as the customer’s primary point of contact. This project’s purpose was to understand if shifting the account management from brand centric to customer centric, would be the best fit for a Portuguese Pharmaceutical company. This customer centric approach - Strategic Account Management (SAM) - was studied, understanding the implicated trade-offs to the company. The workforce was probed about the project and their comments were analyzed. The conclusion points to an implementation of SAM and proposes the adaptations to follow in order to smooth the change.

Modeling and optimization of extracellular polysaccharides production by Enterobacter A47

Polysaccharides are gaining increasing attention as potential environmental friendly and sustainable building blocks in many fields of the (bio)chemical industry. The microbial production of polysaccharides is envisioned as a promising path, since higher biomass growth rates are possible and therefore higher productivities may be achieved compared to vegetable or animal polysaccharides sources. This Ph.D. thesis focuses on the modeling and optimization of a particular microbial polysaccharide, namely the production of extracellular polysaccharides (EPS) by the bacterial strain Enterobacter A47. Enterobacter A47 was found to be a metabolically versatile organism in terms of its adaptability to complex media, notably capable of achieving high growth rates in media containing glycerol byproduct from the biodiesel industry. However, the industrial implementation of this production process is still hampered due to a largely unoptimized process. Kinetic rates from the bioreactor operation are heavily dependent on operational parameters such as temperature, pH, stirring and aeration rate. The increase of culture broth viscosity is a common feature of this culture and has a major impact on the overall performance. This fact complicates the mathematical modeling of the process, limiting the possibility to understand, control and optimize productivity. In order to tackle this difficulty, data-driven mathematical methodologies such as Artificial Neural Networks can be employed to incorporate additional process data to complement the known mathematical description of the fermentation kinetics. In this Ph.D. thesis, we have adopted such an hybrid modeling framework that enabled the incorporation of temperature, pH and viscosity effects on the fermentation kinetics in order to improve the dynamical modeling and optimization of the process. A model-based optimization method was implemented that enabled to design bioreactor optimal control strategies in the sense of EPS productivity maximization. It is also critical to understand EPS synthesis at the level of the bacterial metabolism, since the production of EPS is a tightly regulated process. Methods of pathway analysis provide a means to unravel the fundamental pathways and their controls in bioprocesses. In the present Ph.D. thesis, a novel methodology called Principal Elementary Mode Analysis (PEMA) was developed and implemented that enabled to identify which cellular fluxes are activated under different conditions of temperature and pH. It is shown that differences in these two parameters affect the chemical composition of EPS, hence they are critical for the regulation of the product synthesis. In future studies, the knowledge provided by PEMA could foster the development of metabolically meaningful control strategies that target the EPS sugar content and oder product quality parameters.