Desenvolvimento de algoritmos de agrupamento e modelação de manchas. Aplicação em imagens de retinografia

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

Contribuição da produção em regime especial na garantia do abastecimento de energia elétrica e o impacto no planeamento da rede

Esta dissertação apresenta um estudo sobre a garantia de fornecimento de energia elétrica por parte dos produtores em regime especial com tecnologia cogeração e o impacto que estes traduzem na fase de planeamento da rede. Este trabalho foi realizado na Energias de Portugal - Distribuição (EDP-D) na direção de planeamento da rede (DPL). Para este estudo foi utilizado o caso de uma subestação com dezoito produtores em regime especial agregados à sua rede, em que dezasseis desses produtores são cogeração. A proposta de estudo para o caso concreto, passa pela análise das condições de funcionamento da subestação e apurar se a mesma necessita de alguma reformulação, tendo em vista as cargas a satisfazer atuais e possível incremento de carga futura. Considerando que a subestação está inserida num ambiente industrial e atendendo que existem diversos produtores de energia elétrica nas imediações da subestação. Para a resolução da garantia do fornecimento de energia por parte da cogeração, estudou-se a possibilidade de prever a energia produzida por estes produtores, através dos seguintes modelos de previsão: árvore de regressão, árvore de regressão com aplicação bagging e uma rede neuronal (unidirecional). Com a implementação destes modelos pretende-se estimar qual a potência que se pode esperar na garantia de abastecimento da carga, prevenindo maior solicitação de potência por parte da subestação. A metodologia utilizada baseia-se em simulações computacionais.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Retrieval of kinetic rates in reactions with semi batch liquid phase using ill-posed inverse problem theory

A neural network procedure to solve inverse chemical kinetic problems is discussed in this work. Rate constants are calculated from the product concentration of an irreversible consecutive reaction: the hydrogenation of Citral molecule, a process with industrial interest. Simulated and experimental data are considered. Errors in the simulated data, up to 7% in the concentrations, were assumed to investigate the robustness of the inverse procedure. Also, the proposed method is compared with two common methods in nonlinear analysis; the Simplex and Levenberg-Marquardt approaches. In all situations investigated, the neural network approach was numerically stable and robust with respect to deviations in the initial conditions or experimental noises.

Methane combustion kinetic rate constants determination: an ill-posed inverse problem analysis

Methane combustion was studied by the Westbrook and Dryer model. This well-established simplified mechanism is very useful in combustion science, for computational effort can be notably reduced. In the inversion procedure to be studied, rate constants are obtained from [CO] concentration data. However, when inherent experimental errors in chemical concentrations are considered, an ill-conditioned inverse problem must be solved for which appropriate mathematical algorithms are needed. A recurrent neural network was chosen due to its numerical stability and robustness. The proposed methodology was compared against Simplex and Levenberg-Marquardt, the most used methods for optimization problems.

Ajuste do modelo de Schumacher e Hall e aplicação de redes neurais artificiais para estimar volume de árvores de eucalipto

Objetivou-se, neste trabalho, avaliar o ajuste do modelo volumétrico de Schumacher e Hall por diferentes algoritmos, bem como a aplicação de redes neurais artificiais para estimação do volume de madeira de eucalipto em função do diâmetro a 1,30 m do solo (DAP), da altura total (Ht) e do clone. Foram utilizadas 21 cubagens de povoamentos de clones de eucalipto com DAP variando de 4,5 a 28,3 cm e altura total de 6,6 a 33,8 m, num total de 862 árvores. O modelo volumétrico de Schumacher e Hall foi ajustado nas formas linear e não linear, com os seguintes algoritmos: Gauss-Newton, Quasi-Newton, Levenberg-Marquardt, Simplex, Hooke-Jeeves Pattern, Rosenbrock Pattern, Simplex, Hooke-Jeeves e Rosenbrock, utilizado simultaneamente com o método Quasi-Newton e com o princípio da Máxima Verossimilhança. Diferentes arquiteturas e modelos (Multilayer Perceptron MLP e Radial Basis Function RBF) de redes neurais artificiais foram testados, sendo selecionadas as redes que melhor representaram os dados. As estimativas dos volumes foram avaliadas por gráficos de volume estimado em função do volume observado e pelo teste estatístico L&O. Assim, conclui-se que o ajuste do modelo de Schumacher e Hall pode ser usado na sua forma linear, com boa representatividade e sem apresentar tendenciosidade; os algoritmos Gauss-Newton, Quasi-Newton e Levenberg-Marquardt mostraram-se eficientes para o ajuste do modelo volumétrico de Schumacher e Hall, e as redes neurais artificiais apresentaram boa adequação ao problema, sendo elas altamente recomendadas para realizar prognose da produção de florestas plantadas.

Thermal reactions of the major hydrocarbon components of biomass gasification gas

Gasification of biomass is an efficient method process to produce liquid fuels, heat and electricity. It is interesting especially for the Nordic countries, where raw material for the processes is readily available. The thermal reactions of light hydrocarbons are a major challenge for industrial applications. At elevated temperatures, light hydrocarbons react spontaneously to form higher molecular weight compounds. In this thesis, this phenomenon was studied by literature survey, experimental work and modeling effort. The literature survey revealed that the change in tar composition is likely caused by the kinetic entropy. The role of the surface material is deemed to be an important factor in the reactivity of the system. The experimental results were in accordance with previous publications on the subject. The novelty of the experimental work lies in the used time interval for measurements combined with an industrially relevant temperature interval. The aspects which are covered in the modeling include screening of possible numerical approaches, testing of optimization methods and kinetic modelling. No significant numerical issues were observed, so the used calculation routines are adequate for the task. Evolutionary algorithms gave a better performance combined with better fit than the conventional iterative methods such as Simplex and Levenberg-Marquardt methods. Three models were fitted on experimental data. The LLNL model was used as a reference model to which two other models were compared. A compact model which included all the observed species was developed. The parameter estimation performed on that model gave slightly impaired fit to experimental data than LLNL model, but the difference was barely significant. The third tested model concentrated on the decomposition of hydrocarbons and included a theoretical description of the formation of carbon layer on the reactor walls. The fit to experimental data was extremely good. Based on the simulation results and literature findings, it is likely that the surface coverage of carbonaceous deposits is a major factor in thermal reactions.

Técnicas de controlo não-linear baseadas em Redes Neuronais: do algoritmo à implementação

O presente trabalho analisa soluções de controlo não-linear baseadas em Redes Neuronais e apresenta a sua aplicação a um caso prático, desde o algoritmo de treino até à implementação física em hardware. O estudo inicial do estado da arte da utilização das Redes Neuronais para o controlo leva à proposta de soluções iterativas para a definição da arquitectura das mesmas e para o estudo das técnicas de Regularização e Paragem de Treino Antecipada, através dos Algoritmos Genéticos e à proposta de uma forma de validação dos modelos obtidos. Ao longo da tese são utilizadas quatro malhas para o controlo baseado em modelos, uma das quais uma contribuição original, e é implementado um processo de identificação on-line, tendo por base o algoritmo de treino Levenberg-Marquardt e a técnica de Paragem de Treino Antecipada que permite o controlo de um sistema, sem necessidade de recorrer ao conhecimento prévio das suas características. O trabalho é finalizado com um estudo do hardware comercial disponível para a implementação de Redes Neuronais e com o desenvolvimento de uma solução de hardware utilizando uma FPGA. De referir que o trabalho prático de teste das soluções apresentadas é realizado com dados reais provenientes de um forno eléctrico de escala reduzida.

Síntese de superfícies seletivas de frequência atráves de técnicas de computação natural

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Equilíbrio líquido-líquido de sistemas aquosos com tensoativos polietoxilados: dados experimentais e modelagem

The nonionic surfactants when in aqueous solution, have the property of separating into two phases, one called diluted phase, with low concentration of surfactant, and the other one rich in surfactants called coacervate. The application of this kind of surfactant in extraction processes from aqueous solutions has been increasing over time, which implies the need for knowledge of the thermodynamic properties of these surfactants. In this study were determined the cloud point of polyethoxylated surfactants from nonilphenolpolietoxylated family (9,5 , 10 , 11, 12 and 13), the family from octilphenolpolietoxylated (10 e 11) and polyethoxylated lauryl alcohol (6 , 7, 8 and 9) varying the degree of ethoxylation. The method used to determine the cloud point was the observation of the turbidity of the solution heating to a ramp of 0.1 ° C / minute and for the pressure studies was used a cell high-pressure maximum ( 300 bar). Through the experimental data of the studied surfactants were used to the Flory - Huggins models, UNIQUAC and NRTL to describe the curves of cloud point, and it was studied the influence of NaCl concentration and pressure of the systems in the cloud point. This last parameter is important for the processes of oil recovery in which surfactant in solution are used in high pressures. While the effect of NaCl allows obtaining cloud points for temperatures closer to the room temperature, it is possible to use in processes without temperature control. The numerical method used to adjust the parameters was the Levenberg - Marquardt. For the model Flory- Huggins parameter settings were determined as enthalpy of the mixing, mixing entropy and the number of aggregations. For the UNIQUAC and NRTL models were adjusted interaction parameters aij using a quadratic dependence with temperature. The parameters obtained had good adjust to the experimental data RSMD < 0.3 %. The results showed that both, ethoxylation degree and pressure increase the cloudy points, whereas the NaCl decrease

Artificial neural networks for load flow and external equivalents studies

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (C) 2010 Elsevier B.V. All rights reserved.

Estudos de sorção de um corante aniônico modelo em partículas de quitosana reticulada

Textile activity results in effluents with a variety of dyes. Among the several processes for dye-uptaking from these wastewaters, sorption is one of the most effective methods, chitosan being a very promising alternative for this end. The sorption of Methyl Orange by chitosan crosslinked particles was approached using equilibrium and kinetic analyses at different pH s. Besides the standard pseudo-order analysis normally effectuated (i.e. pseudo-first-order and pseudo-second-order), a novel approach involving a pseudo-nth-order kinetics was used, nbeing determined via non-linear regression, using the Levenberg-Marquardt method. Zeta potential measurements indicated that electrostatic interactions were important for the sorption process. Regarding equilibrium experiments, data were well fitted to a hybrid Langmuir-Freundlich isotherm, and estimated Gibbs free energy of adsorption as a function of mass of dye per area of chitosan showed that the process of adsorption becomes more homogeneous as the pH of the continuous phase decreased. Considering the kinetics of sorption, although a pseudo-nth-order description yielded good fits, a kinetic equation involving diffusion adsorption phenomena was found to be more consistent in terms of a physicochemical description of the sorption process

Modelagem do escoamento ao longo de evaporadores de serpentina com tubos aletados

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Previsão de Preço (LMP) por Redes GRNN

Pós-graduação em Engenharia Elétrica - FEIS

Ein inverses Problem für die degeneriert parabolische Richardsgleichung

In der vorliegenden Arbeit werden zwei physikalischeFließexperimente an Vliesstoffen untersucht, die dazu dienensollen, unbekannte hydraulische Parameter des Materials, wiez. B. die Diffusivitäts- oder Leitfähigkeitsfunktion, ausMeßdaten zu identifizieren. Die physikalische undmathematische Modellierung dieser Experimente führt auf einCauchy-Dirichlet-Problem mit freiem Rand für die degeneriertparabolische Richardsgleichung in derSättigungsformulierung, das sogenannte direkte Problem. Ausder Kenntnis des freien Randes dieses Problems soll dernichtlineare Diffusivitätskoeffizient derDifferentialgleichung rekonstruiert werden. Für diesesinverse Problem stellen wir einOutput-Least-Squares-Funktional auf und verwenden zu dessenMinimierung iterative Regularisierungsverfahren wie dasLevenberg-Marquardt-Verfahren und die IRGN-Methode basierendauf einer Parametrisierung des Koeffizientenraumes durchquadratische B-Splines. Für das direkte Problem beweisen wirunter anderem Existenz und Eindeutigkeit der Lösung desCauchy-Dirichlet-Problems sowie die Existenz des freienRandes. Anschließend führen wir formal die Ableitung desfreien Randes nach dem Koeffizienten, die wir für dasnumerische Rekonstruktionsverfahren benötigen, auf einlinear degeneriert parabolisches Randwertproblem zurück.Wir erläutern die numerische Umsetzung und Implementierungunseres Rekonstruktionsverfahrens und stellen abschließendRekonstruktionsergebnisse bezüglich synthetischer Daten vor.