Reforma a vapor catalítica do metano: Otimização da produção e seletividade em hidrogênio por absorção in situ do CO2 produzido

Topics of research related to energy and environment have significantly grown in recent years, with the need of its own energy as hydrogen. More particularly, numerous researches have been focused on hydrogen as energy vector. The main portion of hydrogen is presently obtained by reforming of methane or light hydrocarbons (steam, oxy, dry or auto reforming). During the methane steam reforming process the formation of CO2 undesirable (the main contributor to the greenhouse effect) is observed. Thus, an oxide material (sorbent) can be used to capture the CO2 generated during the process and simultaneously shifting the equilibrium of water gas shift towards thermodynamically more favorable production of pure hydrogen. The aim of this study is to develop a material with dual function (catalyst/sorbent) in the reaction of steam reforming of methane. CaO is well known as CO2 sorbent due to its high efficiency in reactions of carbonation and easy regeneration through calcination. However the kinetic of carbonation decreases quickly with time and carbonation/calcination cycles. A calcium aluminate (Ca12Al14O33) should be used to avoid sintering and increase the stability of CaO sorbents for several cycles. Nickel, the industrial catalyst choice for steam reforming has been added to the support from different manners. These bi-functional materials (sorbent/catalyst) in different molar ratios CaO.Ca12Al14O33 (48:52, 65:35, 75:25, 90:10) were prepared by different synthesis methodologies, among them, especially the method of microwave assisted self-combustion. Synthesis, structure and catalytic performances of Ni- CaO.Ca12Al14O33 synthesized by the novel method (microwave assisted selfcombustion) proposed in this work has not being reported yet in literature. The results indicate that CO2 capture time depends both on the CaO excess and on operating conditions (eg., temperature and H2O/CH4 ratio). To be efficient for CO2 sorption, temperature of steam reforming needs to be lower than 700 °C. An optimized percentage corresponding to 75% of CaO and a ratio H2O/CH4 = 1 provides the most promising results since a smaller amount of water avoids competition between water and CO2 to form carbonate and hydroxide. If this competition is most effective (H2O/CH4 = 3) and would have a smaller amount of CaO available for absorption possibly due to the formation of Ca(OH)2. Therefore, the capture time was higher (16h) for the ratio H2O/CH4 = 1 than H2O/CH4 = 3 (7h) using as catalyst one prepared by impregnating the support obtained by microwave assisted self-combustion. Therefore, it was demonstrated that, with these catalysts, the CO2 sorption on CaO modifies the balance of the water gas-shift reaction. Consequently, steam reforming of CH4 is optimized, producing pure H2, complete conversion of methane and negligible concentration of CO2 and CO during the time of capture even at low temperature (650 °C). This validates the concept of the sorption of CO2 together with methane steam reforming

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Uma abordagem por nuvem de partículas para problemas de otimização combinatória

Combinatorial optimization problems have the goal of maximize or minimize functions defined over a finite domain. Metaheuristics are methods designed to find good solutions in this finite domain, sometimes the optimum solution, using a subordinated heuristic, which is modeled for each particular problem. This work presents algorithms based on particle swarm optimization (metaheuristic) applied to combinatorial optimization problems: the Traveling Salesman Problem and the Multicriteria Degree Constrained Minimum Spanning Tree Problem. The first problem optimizes only one objective, while the other problem deals with many objectives. In order to evaluate the performance of the algorithms proposed, they are compared, in terms of the quality of the solutions found, to other approaches

Um estudo algorítmico para otimização do plano de tratamento da radioterapia conformal

This work performs an algorithmic study of optimization of a conformal radiotherapy plan treatment. Initially we show: an overview about cancer, radiotherapy and the physics of interaction of ionizing radiation with matery. A proposal for optimization of a plan of treatment in radiotherapy is developed in a systematic way. We show the paradigm of multicriteria problem, the concept of Pareto optimum and Pareto dominance. A generic optimization model for radioterapic treatment is proposed. We construct the input of the model, estimate the dose given by the radiation using the dose matrix, and show the objective function for the model. The complexity of optimization models in radiotherapy treatment is typically NP which justifyis the use of heuristic methods. We propose three distinct methods: MOGA, MOSA e MOTS. The project of these three metaheuristic procedures is shown. For each procedures follows: a brief motivation, the algorithm itself and the method for tuning its parameters. The three method are applied to a concrete case and we confront their performances. Finally it is analyzed for each method: the quality of the Pareto sets, some solutions and the respective Pareto curves

Aplicação do algoritmo de otimização por colônia de formigas sobre o problema do passeio do robô seletivo

This work seeks to propose and evaluate a change to the Ant Colony Optimization based on the results of experiments performed on the problem of Selective Ride Robot (PRS, a new problem, also proposed in this paper. Four metaheuristics are implemented, GRASP, VNS and two versions of Ant Colony Optimization, and their results are analyzed by running the algorithms over 32 instances created during this work. The metaheuristics also have their results compared to an exact approach. The results show that the algorithm implemented using the GRASP metaheuristic show good results. The version of the multicolony ant colony algorithm, proposed and evaluated in this work, shows the best results

Análise das medidas de boa e má diversidade na construção de comitês de classificadores através de metaheurísticas de otimização multiobjetivo

Committees of classifiers may be used to improve the accuracy of classification systems, in other words, different classifiers used to solve the same problem can be combined for creating a system of greater accuracy, called committees of classifiers. To that this to succeed is necessary that the classifiers make mistakes on different objects of the problem so that the errors of a classifier are ignored by the others correct classifiers when applying the method of combination of the committee. The characteristic of classifiers of err on different objects is called diversity. However, most measures of diversity could not describe this importance. Recently, were proposed two measures of the diversity (good and bad diversity) with the aim of helping to generate more accurate committees. This paper performs an experimental analysis of these measures applied directly on the building of the committees of classifiers. The method of construction adopted is modeled as a search problem by the set of characteristics of the databases of the problem and the best set of committee members in order to find the committee of classifiers to produce the most accurate classification. This problem is solved by metaheuristic optimization techniques, in their mono and multi-objective versions. Analyzes are performed to verify if use or add the measures of good diversity and bad diversity in the optimization objectives creates more accurate committees. Thus, the contribution of this study is to determine whether the measures of good diversity and bad diversity can be used in mono-objective and multi-objective optimization techniques as optimization objectives for building committees of classifiers more accurate than those built by the same process, but using only the accuracy classification as objective of optimization

Otimização em braquiterapia de alta taxa de dose com algoritmo transgenético

Este trabalho aborda o problema de otimização em braquiterapia de alta taxa de dose no tratamento de pacientes com câncer, com vistas à definição do conjunto de tempos de parada. A técnica de solução adotada foi a Transgenética Computacional apoiada pelo método L-BFGS. O algoritmo desenvolvido foi empregado para gerar soluções não denominadas cujas distribuições de dose fossem capazes de eiminar o câncer e ao mesmo tempo preservar as regiões normais

Otimização de imagens mamográficas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Aspectos relacionados com a otimização do treinamento aeróbio para o alto rendimento

The objective of this work was to present recommendations aiming the aerobic training optimization, from the knowledge of the indexes of functional fitness and their physiological mechanisms. Concerning highly trained athletes, the accuracy in training elaboration can be the safest way to improve aerobic performance, since for these individuals, it is normal that the training load is changeable between an insufficient stimulus and the overtraining syndrome symptoms onset. Therefore, there are several factors that should be taken into account for the elaboration of a training program. The knowledge on fatigue mechanisms and physiological responses at different exercise intensities and durations is essential for the correct training session elaboration. Moreover, high-intensity interval training is indispensable to improve performance in highly trained athletes; however, it should be performed only after adequate recovery period. Thus, a good relationship between coach and athlete is also important for planning suitable recovery periods prior to excessive fatigue. The coach should keep accurate records of training loads and recovery times, learning hence the kinds of loads that can be individually tolerated. Among the important factors that can affect aerobic performance during competition and should be considered, we can name appropriate warm-up planning and adverse environmental conditions. After collecting all this information, it is possible to elaborate the training bases (frequency, volume, intensity and recovery) aiming at progressive improvement of aerobic performance.

Estrógenos em água: otimização da extração em fase sólida utilizando ferramentas quimiométricas

Muitos métodos analíticos estão sendo desenvolvidos visando à determinação de contaminantes orgânicos, especialmente alteradores endócrinos. Tais métodos baseiam-se geralmente na extração em fase sólida (SPE) seguida por determinação cromatográfica (CG ou HPLC). No presente trabalho utilizou-se ferramentas quimiométricas no processo de SPE para avaliar os principais fatores que influenciam tal processo e as interações entre os mesmos. Foram analisadas matrizes de água subterrânea fortificada com hormônios (17 b estradiol, estrona e 17 b etinilestradiol) e a determinação analítica foi feita por HPLC/Fluorescência. Um planejamento fatorial completo foi utilizado. Os fatores escolhidos incluíram: condicionamento da fase sólida, concentração dos analitos, volume da amostra e solvente de eluição. As melhores condições obtidas foram: 500 mL da amostra, condicionamento da fase sólida (C18) com acetona (4mL), metanol (6 mL) e água pH 3(10 mL), e eluição dos analitos com 4 mL de acetona.

Planejamento experimental na otimização da extração dos frutos de Syzygium cumini (L.) skeels

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

Otimização e validação de uma metodologia analítica para determinação de 1-hidroxipireno em urina de cortadores de cana-de-açúcar

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

Otimização e validação de metodologia cromatográfica para determinação simultânea de composição monomérica em polímero biodegradável

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Uma abordagem usando redes neurais artificiais para resolução de problemas de otimização restrita

Sistemas baseados em redes neurais artificiais fornecem altas taxas de computação devido ao uso de um número massivo de elementos processadores simples. Redes neurais com conexões realimentadas fornecem um modelo computacional capaz de resolver uma rica classe de problemas de otimização. Este artigo apresenta uma nova abordagem para resolver problemas de otimização restrita utilizando redes neurais artificiais. Mais especificamente, uma rede de Hopfield modificada é desenvolvida cujos parâmetros internos são calculados usando a técnica de subespaço válido de soluções. A partir da obtenção destes parâmetros a rede tende a convergir aos pontos de equilíbrio que representam as possíveis soluções para o problema. Exemplos de simulação são apresentados para justificar a validade da abordagem proposta.

Detecção de falhas em estruturas inteligentes usando otimização por nuvem de partículas: fundamentos e estudo de casos

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