A UML-based approach for modeling industrial control applications

The main purpose of the poster is to present how the Unified Modeling Language (UML) can be used for diagnosing and optimizing real industrial production systems. By using a car radios production line as a case study, the poster shows the modeling process that can be followed during the analysis phase of complex control applications. In order to guarantee the continuity mapping of the models, the authors propose some guidelines to transform the use cases diagrams into a single object diagram, which is the main diagram for the next phases of the development.

Desenvolvimento de membranas poliméricas baseadas em Poli(fluoreto de vinilideno) para a filtração de materiais biológicos

Dissertação de mestrado em Biofísica e Bionanossistemas

Valorization of agro-industrial wastes towards the production of rhamnolipids

In this work, oil mill wastewater (OMW), a residue generated during olive oil extraction, was evaluated as an inducer of rhamnolipid production. Using a medium containing as sole ingredients corn steep liquor (10%, v/v), sugarcane molasses (10%, w/v) and OMW (25%, v/v), Pseudomonas aeruginosa #112 produced 4.5 and 5.1 g of rhamnolipid per liter in flasks and reactor, respectively, with critical micelle concentrations as low as 13 mg/l. Furthermore, in the medium supplemented with OMW, a higher proportion of more hydrophobic rhamnolipid congeners was observed comparing with the same medium without OMW. OMW is a hazardous waste which disposal represents a serious environmental problem; therefore, its valorization as a substrate for the production of added-value compounds such as rhamnolipids is of great interest. This is the first report of rhamnolipid production using a mixture of these three agro-industrial by-products, which can be useful for the sustainable production of rhamnolipids.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

In-line rheo-optical microstructural characterization of complex polymer systems

Tese de Doutoramento em Ciência e Engenharia de Polímeros e Compósitos.

Contribuição para o melhoramento da gestão operacional de uma linha de fabrico de garrafas de gás

Dissertação de mestrado integrado em Engenharia e Gestão Industrial