O aprimoramento dos meios para obtenção do bioetanol a partir de diferentes tipos de biomassa traz novos problemas e desafios para a engenharia. O Brasil, devido a fatores climáticos e uma produção de etanol a partir da cana de açúcar já estabilizada, possui uma posição mundial vantajosa na produção sucroalcooleira. Otimizar os meios já existentes e os em desenvolvimento pode não somente aumentar a eficiência da produção, como também reduzir os impactos ambientais causados pelo modelo de produção atualmente utilizado. O processo de fermentação é utilizado tanto na produção de etanol de primeira como de segunda geração, portanto melhorias no desempenho dos fermentadores contribui de maneira significante para o melhor aproveitamento da matéria prima. Diversas tentativas de melhorias são apresentadas na literatura, principalmente por meio do estudo de parâmetros do escoamento que podem influenciar o processo fermentativo, como tensão de cisalhamento, perfis de velocidade e tempo de residência, assim como a influência da geometria do fermentador sobre esses parâmetros. Em alguns estudos, algoritmos de otimização são utilizados para determinar os melhores coeficientes das reações químicas, mas não há estudos, até o momento, que proporcionem otimização simultânea dos parâmetros da geometria e do escoamento em um fermentador contínuo, presentes em cerca de 30% das usinas brasileiras. O objetivo deste trabalho é obter os parâmetros geométricos ideais para um fermentador contínuo, de forma a minimizar a tensão de cisalhamento a variância da distribuição de tempos de residência (DTR) no fermentador. O Ansys CFX® foi utilizado como ferramenta na simulação computacional do escoamento. As geometrias dos fermentadores ideais para cada um dessas análises, obtidas utilizando Algoritmos Genéticos e otimização univariada, respectivamente, foram propostas neste estudo. / The enhancement of bioethanol production means from different types of biomass presents significant problems and engineering challenges. Due to climate and a well-established sugar-cane ethanol production, Brazil is in a privileged position in the global ethanol production scenario. Providing effective means to optimize existing production methods can both improve the efficiency and reduce the environmental impact of the currently used production model. Improvements on this process can have a significant effect in several stages of production, once the production process is used both for first and second-generation ethanol. Several attempts to improve the ethanol production process are presented in the literature. Most studies have investigated how to improve parameters such as shear stress, velocity profiles and residence time, and of the influence of the bioreactor geometry on the parameters. The use of genetic algorithms has been reported in some cases, but there have not been reports on studies combining the optimization of flow parameters and algorithms to choose ideal geometric parameters for continuous fermenters, used in 30% of Brazilian industries in the field. The main aim of this study is to obtain ideal geometric parameters for a continuous fermenter, in order to maximize or minimize flow parameters that can influence on the fermenting process. The aim of this study is obtain the ideal geometry parameters for a continuous fermenter, minimizing two of flow parameters which can influence the fermentation process, namely the shear stress and the variance of residence time distribution (RTD). The flow parameters was obtained by computational fluid dynamics. The ideal fermenter geometries was obtained by two different optimization methods: the genetic algorithms and univariate optimization. The ideal geometries was proposed in this study.

In insurance risk theory, the surplus of an insurance company is modelled to monitor and quantify its risks. With the outgo of claims and inflow of premiums, the insurer needs to determine what financial portfolio ensures the soundness of the company’s future while satisfying the shareholders’ interests. It is usually assumed that the net profit condition (i.e. the expectation of the process is positive) is satisfied, which then implies that this process would drift towards infinity. To correct this unrealistic behaviour, the surplus process was modified to include the payout of dividends until the time of ruin. Under this more realistic surplus process, a topic of growing interest is determining which dividend strategy is optimal, where optimality is in the sense of maximizing the expected present value of dividend payments. This problem dates back to the work of Bruno De Finetti (1957) where it was shown that if the surplus process is modelled as a random walk with ± 1 step sizes, the optimal dividend payment strategy is a barrier strategy. Such a strategy pays as dividends any excess of the surplus above some threshold. Since then, other examples where a barrier strategy is optimal include the Brownian motion model (Gerber and Shiu (2004)) and the compound Poisson process model with exponential claims (Gerber and Shiu (2006)). In this thesis, we focus on the optimality of a barrier strategy in the more general Lévy risk models. The risk process will be formulated as a spectrally negative Lévy process, a continuous-time stochastic process with stationary increments which provides an extension of the classical Cramér-Lundberg model. This includes the Brownian and the compound Poisson risk processes as special cases. In this setting, results are expressed in terms of “scale functions”, a family of functions known only through their Laplace transform. In Loeffen (2008), we can find a sufficient condition on the jump distribution of the process for a barrier strategy to be optimal. This condition was then improved upon by Loeffen and Renaud (2010) while considering a more general control problem. The first chapter provides a brief review of theory of spectrally negative Lévy processes and scale functions. In chapter 2, we define the optimal dividends problem and provide existing results in the literature. When the surplus process is given by the Cramér-Lundberg process with a Brownian motion component, we provide a sufficient condition on the parameters of this process for the optimality of a dividend barrier strategy. Chapter 3 focuses on the case when the claims distribution is given by a univariate mixture of Erlang distributions with a common scale parameter. Analytical results for the Value-at-Risk and Tail-Value-at-Risk, and the Euler risk contribution to the Conditional Tail Expectation are provided. Additionally, we give some results for the scale function and the optimal dividends problem. In the final chapter, we propose an expectation maximization (EM) algorithm similar to that in Lee and Lin (2009) for fitting the univariate distribution to data. This algorithm is implemented and numerical results on the goodness of fit to sample data and on the optimal dividends problem are presented.

In insurance risk theory, the surplus of an insurance company is modelled to monitor and quantify its risks. With the outgo of claims and inflow of premiums, the insurer needs to determine what financial portfolio ensures the soundness of the company’s future while satisfying the shareholders’ interests. It is usually assumed that the net profit condition (i.e. the expectation of the process is positive) is satisfied, which then implies that this process would drift towards infinity. To correct this unrealistic behaviour, the surplus process was modified to include the payout of dividends until the time of ruin. Under this more realistic surplus process, a topic of growing interest is determining which dividend strategy is optimal, where optimality is in the sense of maximizing the expected present value of dividend payments. This problem dates back to the work of Bruno De Finetti (1957) where it was shown that if the surplus process is modelled as a random walk with ± 1 step sizes, the optimal dividend payment strategy is a barrier strategy. Such a strategy pays as dividends any excess of the surplus above some threshold. Since then, other examples where a barrier strategy is optimal include the Brownian motion model (Gerber and Shiu (2004)) and the compound Poisson process model with exponential claims (Gerber and Shiu (2006)). In this thesis, we focus on the optimality of a barrier strategy in the more general Lévy risk models. The risk process will be formulated as a spectrally negative Lévy process, a continuous-time stochastic process with stationary increments which provides an extension of the classical Cramér-Lundberg model. This includes the Brownian and the compound Poisson risk processes as special cases. In this setting, results are expressed in terms of “scale functions”, a family of functions known only through their Laplace transform. In Loeffen (2008), we can find a sufficient condition on the jump distribution of the process for a barrier strategy to be optimal. This condition was then improved upon by Loeffen and Renaud (2010) while considering a more general control problem. The first chapter provides a brief review of theory of spectrally negative Lévy processes and scale functions. In chapter 2, we define the optimal dividends problem and provide existing results in the literature. When the surplus process is given by the Cramér-Lundberg process with a Brownian motion component, we provide a sufficient condition on the parameters of this process for the optimality of a dividend barrier strategy. Chapter 3 focuses on the case when the claims distribution is given by a univariate mixture of Erlang distributions with a common scale parameter. Analytical results for the Value-at-Risk and Tail-Value-at-Risk, and the Euler risk contribution to the Conditional Tail Expectation are provided. Additionally, we give some results for the scale function and the optimal dividends problem. In the final chapter, we propose an expectation maximization (EM) algorithm similar to that in Lee and Lin (2009) for fitting the univariate distribution to data. This algorithm is implemented and numerical results on the goodness of fit to sample data and on the optimal dividends problem are presented.

Submitted by Mario BC (mario@bc.ufrpe.br) on 2016-08-01T12:46:49Z No. of bitstreams: 1 Luciano Souza.pdf: 1424173 bytes, checksum: 75d7ff2adb5077203e1371925327b71e (MD5) / Made available in DSpace on 2016-08-01T12:46:49Z (GMT). No. of bitstreams: 1 Luciano Souza.pdf: 1424173 bytes, checksum: 75d7ff2adb5077203e1371925327b71e (MD5) Previous issue date: 2015-11-13 / In this thesis, four new probabilistic distribution classes are presented and investigated: sine, cosine, tangent and secant. For each of which a new kind of distribution was created, which were used for modelling real life data.By having an exponential distribution to compare the biases, a numerical simulation was obtained, making it possible to verify that the bias tends to zero as the sample size is increased. In addition to that, some numerical results for checking maximum likelihood estimates, as well as the results for finite samples, were obtained, just as much as several class properties and their respective distributions were also obtained, along with the expansions, maximum likelihood estimates, Fisher information, the first four moments, average, variance, skewness, and kurtosis, the generating function of moments and Renyi’s entropy. It was evidenced that all distributions have shown good fit when applied to real life data, when in comparison to other models. In order to compare the models, the Akaike Information Criterion (AIC), the Corrected Akaike Information Criterion (CAIC), the Bayesian Information Criterion (BIC), the Hannan Quinn Information Criterion (HQIC) were used, along with two other main statistic sources: Cramer-Von Mises and Anderson-Darling. As a final step, the results of the analyses and the comparison of the results are brought up, as well as a few directions for future works. / Nesta tese apresentamos e investigamos quatro novas classes trigonométricas de distribuições probabilísticas. As classes seno, cosseno, tangente e secante. Para cada uma das novas classes foi criada uma nova distribuição. Estas quatro novas distribuições foram usadas na modelagem de dados reais. Obtivemos uma simulação numérica, usando como base a distribuição exponencial, para se comparar os vicios (bias) e verificamos que, a medida que aumentamos o tamanho da amostra, o bias tende a zero. Alguns resultados numéricos para ver estimativas de máxima verossimilhança e os resultados para amostras finitas foram obtidos. Várias propriedades das classes e as suas distribuições foram obtidos. Obtemos as expansões, as estimativas de máxima verossimilhança, informações de Fisher, os quatro primeiros momentos, média, variância, assimetria e curtose, a função geradora de momentos e a entropia Rényi. Mostramos que todas as distribuições têm proporcionado bons ajustes quando aplicadas a dados reais, em comparação com outros modelos. Na comparação dos modelos foram utilizados: o Akaike Information Criterion (AIC), o Akaike Information Criterion Corrigido (CAIC), a informação Bayesian Criterion (BIC), o critério de informação Hannan Quinn (HQIC) e duas das principais estatísticas também foram utilizadas: Cramer -von Mises e Anderson-Darling. Por fim, apresentamos os resultados da análise e comparação dos resultados, e orientações para trabalhos futuros.

O aprimoramento dos meios para obtenção do bioetanol a partir de diferentes tipos de biomassa traz novos problemas e desafios para a engenharia. O Brasil, devido a fatores climáticos e uma produção de etanol a partir da cana de açúcar já estabilizada, possui uma posição mundial vantajosa na produção sucroalcooleira. Otimizar os meios já existentes e os em desenvolvimento pode não somente aumentar a eficiência da produção, como também reduzir os impactos ambientais causados pelo modelo de produção atualmente utilizado. O processo de fermentação é utilizado tanto na produção de etanol de primeira como de segunda geração, portanto melhorias no desempenho dos fermentadores contribui de maneira significante para o melhor aproveitamento da matéria prima. Diversas tentativas de melhorias são apresentadas na literatura, principalmente por meio do estudo de parâmetros do escoamento que podem influenciar o processo fermentativo, como tensão de cisalhamento, perfis de velocidade e tempo de residência, assim como a influência da geometria do fermentador sobre esses parâmetros. Em alguns estudos, algoritmos de otimização são utilizados para determinar os melhores coeficientes das reações químicas, mas não há estudos, até o momento, que proporcionem otimização simultânea dos parâmetros da geometria e do escoamento em um fermentador contínuo, presentes em cerca de 30% das usinas brasileiras. O objetivo deste trabalho é obter os parâmetros geométricos ideais para um fermentador contínuo, de forma a minimizar a tensão de cisalhamento a variância da distribuição de tempos de residência (DTR) no fermentador. O Ansys CFX® foi utilizado como ferramenta na simulação computacional do escoamento. As geometrias dos fermentadores ideais para cada um dessas análises, obtidas utilizando Algoritmos Genéticos e otimização univariada, respectivamente, foram propostas neste estudo. / The enhancement of bioethanol production means from different types of biomass presents significant problems and engineering challenges. Due to climate and a well-established sugar-cane ethanol production, Brazil is in a privileged position in the global ethanol production scenario. Providing effective means to optimize existing production methods can both improve the efficiency and reduce the environmental impact of the currently used production model. Improvements on this process can have a significant effect in several stages of production, once the production process is used both for first and second-generation ethanol. Several attempts to improve the ethanol production process are presented in the literature. Most studies have investigated how to improve parameters such as shear stress, velocity profiles and residence time, and of the influence of the bioreactor geometry on the parameters. The use of genetic algorithms has been reported in some cases, but there have not been reports on studies combining the optimization of flow parameters and algorithms to choose ideal geometric parameters for continuous fermenters, used in 30% of Brazilian industries in the field. The main aim of this study is to obtain ideal geometric parameters for a continuous fermenter, in order to maximize or minimize flow parameters that can influence on the fermenting process. The aim of this study is obtain the ideal geometry parameters for a continuous fermenter, minimizing two of flow parameters which can influence the fermentation process, namely the shear stress and the variance of residence time distribution (RTD). The flow parameters was obtained by computational fluid dynamics. The ideal fermenter geometries was obtained by two different optimization methods: the genetic algorithms and univariate optimization. The ideal geometries was proposed in this study.

This dissertation documents an investigation into forecasting U.S. stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective is to compare the forecasting performance of ARIMA to SSA, as SSA is a relatively recent development and has shown much potential. Stochastic characteristics of stock market data are analyzed and found to be definitely not Gaussian, but instead better fit to a generalized t-distribution. Probability distribution models are validated with goodness-of-fit tests. For analysis, stock data is segmented into non-overlapping time “windows” to support unconditional statistical evaluation. Univariate and multivariate ARIMA and SSA time series models are evaluated for independence. ARIMA models are found to be independent, but SSA models are not able to reach independence. Statistics for out-of-sample forecasts are computed for every stock in every window, and multivariate-univariate confidence interval shrinkages are examined. Results are compared for univariate, bivariate, and trivariate combinations of highly-correlated stocks. Effects are found to be mixed. Bivariate modeling and forecasting with three different covariates are investigated. Examination of results with covariates of trading volume, principal component analysis (PCA), and volatility reveal that PCA exhibits the best overall forecasting accuracy in the entire field of investigated elements, including univariate models. Bivariate-PCA structures are applied in a back-testing environment to evaluate economic significance and robustness of the methods. Initial results of back-testing yielded similar results to those from earlier independent testing. Inconsistent performance across test intervals inspired the development of a second technique that yields improved results and positive economic significance. Robustness is validated through back-testing across multiple market trends.

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This study uses a combination of user testing and analysis to evaluate the impact of brush and tooltip on the comprehension of time series visualizations. Employing a sequential mixed-methods approach, with qualitative data from semi-structured interviews used to inform the design of a visualization tool, followed by a quantitative user study to validate it. Sixteen (16) participants from various fields of study, predominantly computer science, participated in the study. A MANOVA test was conducted with results indicating a significant statistical difference between the groups. Results deriving from the study show that the use of brush and tooltip increases user accuracy on detecting outliers, as for perception of trends and patterns. The study’s context was limited to desktop usage, and all participants were treated as a homogenous group, presenting potential limitations in applying these findings to other devices or more diverse user groups. The results provide information about improving time series data visualizations for facilitating more efficient and effective understanding, which can be relevant specifically to data analysts and academic researchers.

No description available.

No description available.