Made available in DSpace on 2014-12-17T14:54:59Z (GMT). No. of bitstreams: 1 JoseMAJ.pdf: 711051 bytes, checksum: 6bfbf1b93a8a49314295062e59672543 (MD5) Previous issue date: 2007-11-23 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / Artificial Intelligence techniques are applied to improve performance of a simulated oil distillation system. The chosen system was a debutanizer column. At this process, the feed, which comes to the column, is segmented by heating. The lightest components become steams, by forming the LPG (Liquefied Petroleum Gas). The others components, C5+, continue liquid. In the composition of the LPG, ideally, we have only propane and butanes, but, in practice, there are contaminants, for example, pentanes. The objective of this work is to control pentane amount in LPG, by means of intelligent set points (SP s) determination for PID controllers that are present in original instrumentation (regulatory control) of the column. A fuzzy system will be responsible for adjusting the SP's, driven by the comparison between the molar fraction of the pentane present in the output of the plant (LPG) and the desired amount. However, the molar fraction of pentane is difficult to measure on-line, due to constraints such as: long intervals of measurement, high reliability and low cost. Therefore, an inference system was used, based on a multilayer neural network, to infer the pentane molar fraction through secondary variables of the column. Finally, the results shown that the proposed control system were able to control the value of pentane molar fraction under different operational situations / No presente trabalho, aplicamos t?cnicas de intelig?ncia artificial em um sistema simulado de destila??o de petr?leo, mais especificamente em uma coluna debutanizadora. Nesse processo, o produto que chega ? coluna, conhecido como LGN, ? fracionado por meio de aquecimento. Os componentes mais leves s?o transformados em vapor, que v?o constituir o GLP (G?s Liquefeito de Petr?leo), enquanto as fra??es mais pesadas continuam l?quidas, sendo, comumente, chamadas de C5+. Na composi??o do GLP, idealmente, temos apenas propanos e butanos, por?m, na pr?tica, temos a presen?a de contaminantes, como, por exemplo, pentanos (ipentanos e n-pentanos). O objetivo do trabalho ? regular ? quantidade de pentano presente no GLP, por meio da determina??o inteligente dos sets points (SP) de controladores presentes na instrumenta??o original da coluna. Para isso ? utilizado um sistema fuzzy, que ser? respons?vel por ajustar os valores desses SP s, a partir da compara??o entre a fra??o molar do pentano na sa?da da planta (GLP) e a quantidade desejada. Optou-se por controlar apenas a fra??o molar de i-pentano, por esta ser, normalmente, maior que a fra??o molar do n-pentano, e ainda, devido ao fato de que ambas apresentam din?micas extremamente semelhantes em fun??o das condi??es de opera??o da coluna. Por?m, a fra??o molar de pentano, seja do i-pentano ou n-pentano, ? de dif?cil medi??o on-line devido a limita??es, como: longos intervalos de medi??o, pouca confiabilidade e alto custo. Por essa raz?o, foi utilizado um sistema de infer?ncia, constru?do a partir de uma rede neural de m?ltiplas camadas para inferir o percentual de i-pentano a partir de vari?veis secund?rias da coluna. Os resultados obtidos mostram que o sistema fuzzy conseguiu controlar o valor da fra??o molar do i-pentano para diversas situa??es, mostrando ser um sistema de controle avan?ado vi?vel e com um n?vel satisfat?rio de confiabilidade

Made available in DSpace on 2014-12-17T14:55:56Z (GMT). No. of bitstreams: 1 EmanuelGBF_DISSERT.pdf: 5309348 bytes, checksum: dac342ed8ab6114cd0046b442b6a126b (MD5) Previous issue date: 2011-12-09 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This work presents the design and construction of an X-Y table of two degrees of freedom, as well as the development of a fuzzy system for its position and trajectory control. The table is composed of two bases that move perpendicularly to each other in the horizontal plane, and are driven by two DC motors. Base position is detected by position sensors attached to the motor axes. A data acquisition board performs the interface between a laptop and the plant. The fuzzy system algorithm was implemented in LabVIEW? programming environment that processes the sensors signals and determines the control variables values that drive the motors. Experimental results using position reference signals (step type signal) and straight and circular paths reference signals are presented to demonstrate the dynamic behavior of fuzzy system / Apresentam-se, neste trabalho, o projeto e a constru??o de uma mesa de coordenadas de dois graus de liberdade, bem como o desenvolvimento de um sistema fuzzy para o controle de posi??o e trajet?ria dessa mesa. A mesa ? composta de duas bases que se movimentam perpendicularmente entre si, no plano horizontal, e s?o acionadas por dois motores de corrente cont?nua. As posi??es das bases s?o detectadas por dois sensores de posi??o acoplados aos eixos dos motores. Uma placa de aquisi??o de dados realiza a interface entre um computador port?til e a planta. O algoritmo do sistema fuzzy foi implementado no ambiente de programa??o LabVIEW?, que processa os sinais provenientes dos sensores e determina as vari?veis de controle que acionam os motores. Resultados experimentais utilizando sinais de refer?ncia de posi??o (sinais tipo degrau) e sinais de refer?ncia de trajet?rias retil?neas e circulares s?o apresentados para mostrar o comportamento din?mico do sistema fuzzy

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-15T12:20:57Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese SILVA AMRB.pdf: 10197611 bytes, checksum: dfa95dac75e87b0ffef8a344cb8d9996 (MD5) / Made available in DSpace on 2016-07-15T12:20:57Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese SILVA AMRB.pdf: 10197611 bytes, checksum: dfa95dac75e87b0ffef8a344cb8d9996 (MD5) Previous issue date: 2015-01-30 / CNPq / Petrobrás / A aplicação de técnicas de Análises de Componentes Principais (ACP), Redes Neurais Artificiais (RNA), Lógica Fuzzy e Sistema Neurofuzzy para investigar as alterações da característica da água das barragens de Utinga e do Bita que abastecem de água bruta a ETA Suape é de fundamental importância em função do grande número de variáveis utilizadas para definir a qualidade. Neste trabalho, foram realizadas 10 coletas de água em cada área, no período de novembro de 2007 a agosto de 2012, totalizando 120 amostras. Ainda que o conjunto de dados experimentais obtidos seja reduzido, houve múltiplos esforços em demanda da aquisição de informações da qualidade da água junto aos órgãos oficiais de monitoramento ambiental. Os resultados mostraram uma tendência à degradação da propriedade da água das barragens em decorrência da presença de microrganismos, sais e nutrientes, responsáveis pelo processo de eutrofização, o que se configurou pela maior concentração de fósforo total, Coliformes termotolerantes, e diminuição de pH e OD, provavelmente devido à ocorrência de descarte de efluentes da agroindústria canavieira, industrial e doméstico. A ACP caracterizou mais 76% das amostras permitindo visualizar a existência de mudanças sazonais e uma pequena variação espacial d`água nas barragens. A condição da água das duas barragens foi modelada satisfatoriamente, razoável precisão e confiabilidade com os modelos estatístico e computacionais, para uma quantidade de parâmetros e dados ambientais, que embora limitados foram suficientes para realização deste trabalho. Ainda assim, fica evidente a eficiência e sucesso da utilização do Sistema Neurofuzzy (coeficiente de regressão de 0,608 a 0,925) que combina as vantagens das Redes Neurais e da Lógica Fuzzy em modelar o conjunto de dados da qualidade da água das barragens de Utinga e Bita. / The application of techniques such as the Principal Components Analysis (PCAs), Artificial Neural Networks (ANNs), Fuzzy Logic and Neuro-fuzzy Systems for investigating the changes in the water quality characteristics in the Utinga and Bita dams, which supplies raw water to the Suape Wastewater Treatment Plant (WWP), is of great importance due to the high number of variables used to define water quality. In this work were collected 10 water samples used to define water quality, in a period ranging from November 2007 to August 2012, with a total of 120 samples. Although the experimental dataset was limited, there were multiple efforts in gathering information from the Environmental Control Agencies. The results showed a tendency of degradation of the water properties in the dams studied due to the presence of microorganisms, salts and nutrients, responsible for the eutrophication process; result of the higher concentration of total phosphorus, Thermotolerant Coliforms and decrease in pH and DO, probably from the discharge of the sugarcane agroindustry and domestic waste. The PCAs characterised more than 76% of the samples collected, and consequently observing the existence of seasonal changes and small spatial variation of water levels in the dams. The water quality conditions in both dams were satisfactorily modelled, obtaining a reasonable precision and statistical and computational reliability for a certain amount of parameters and environmental data that, even though considered limited, were enough to run this trial. Nonetheless, it becomes evident the efficiency and success in using the Neuro- Fuzzy System (regression coefficient of 0.608 to 0.925), which combines the advantages of both the Neural Networks and Fuzzy Logic in modelling the water quality dataset in the Utinga and Bita dams.

Orientador: Osvaldir Pereira Taranto / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-27T00:40:14Z (GMT). No. of bitstreams: 1 Silva_CarlosAlexandreMoreirada_D.pdf: 33350422 bytes, checksum: 046e0a2c090474593621166c81042136 (MD5) Previous issue date: 2015 / Resumo: As indústrias química, alimentícia e farmacêutica têm empregado extensivamente a operação de fluidização em inúmeros processos, devido às suas características bastante atrativas, que possibilitam um contato efetivo entre a fase sólida e fluida, o que reflete na geração de altas taxas de transferência de calor e de massa. No entanto, o regime de fluidização borbulhante, o qual é condição de partida dos processos que envolvem esta operação, frequentemente é afetado pelas condições operacionais. As temperaturas elevadas, o conteúdo de umidade excessivo das partículas e a introdução de líquidos no leito fluidizado podem conduzir a instabilidades no regime fluidodinâmico e provocar o colapso parcial ou total do leito, reduzindo a eficiência do processo. A manutenção de condições estáveis do regime de fluidização durante processos de recobrimento de partículas em leitos fluidizados é de fundamental importância para garantir uma eficiência de recobrimento favorável e evitar a formação de zonas sem movimentação e aglomeração das partículas no leito, pois estes fatores indesejáveis comprometem a mistura entre as fases e conseqüentemente a qualidade do produto final. Dentro deste contexto, a utilização de um sistema de monitoramento e controle em tempo real de processos de recobrimento de partículas é extremamente desejável para permitir a operação de regimes de fluidização estáveis e garantir um filme de recobrimento uniforme e boas condições de escoabilidade dos sólidos. A presente proposta de tese de doutorado tem por objetivo aplicar a metodologia de análise espectral Gaussiana dos sinais de flutuação de pressão (Parise et al. (2008)), para o desenvolvimento de sistemas de controle baseados em inteligência artificial (Lógica Fuzzy), visando monitorar a estabilidade do regime de fluidização em processo de recobrimento de partículas. Comparações entre as condições fluidodinâmicas dos processos com e sem controle foram analisadas para operações em leito fluidizado em escala de laboratorio. Para avaliar a qualidade das partículas foi utilizada uma sonda de monitoramento in-line (Parsum IPP70), onde se pôde verificar os instantes iniciais da aglomeração indesejada. Com a aplicação desde sistema automatizado foi possível associar a estabilidade da fluidização em função do elevado grau de aglomeração. O ponto de parada do processo pôde ser definido em 420 µm (inicial em 360 µm) e a partir deste o mecanismo de recobrimento acontece simultaneamente com o de aglomeração. Os parâmetros de monitoramento do regime conseguiram não somente identificar a fase inicial da defluidização, como também foi possível a partir deles, controlar o processo por Lógica Fuzzy-PI e estabilizar a operação para altas taxas de suspensão atomizadas / Abstract: The chemical, food and pharmaceutical industries have extensively used fluidization operation in many cases, due to its very attractive features that enable effective contact between the solid and fluid phase, which reflects the generation of high heat and mass transfer rates. However, the bubbling fluidization regime, which is the starting condition of the processes involved in this operation is often affected by operating conditions. Elevated temperatures, excessive moisture content of the particles and introduction of liquid into the fluidized bed may lead to instabilities in the fluid-dynamic regime and cause partial or total collapse of the bed, reducing the process efficiency. The maintenance of stable conditions of the fluidization regime for particle coating processes in fluidized beds is of fundamental importance to ensure a favorable coating efficiency and to avoid zones without movement and agglomeration of particles in the bed, because these undesirable factors compromise the mixing between the phases and therefore the quality of the final product. Within this context, the use of a monitoring system and real-time control of particle coating processes is highly desirable to allow operation in stable fluidization regimes and to ensure a uniform coating film and good condition of flowability of the solids. This doctoral thesis aims to apply the Gaussian spectral analysis methodology of the pressure fluctuation signals (Parise et al. (2008)) , for the development of control systems based on artificial intelligence (Fuzzy Logic), to monitor the stability of fluidization regime particle coating process. Comparisons between the fluid dynamic conditions of the processes with and without control were analyzed for operations in fluidized bed laboratory scale. To assess early stages of unwanted agglomeration, a monitoring in-line probe (Parsum IPP70) was used. With the application of this automated system, it was possible to associate the stability of fluidization with a high degree of agglomeration. The process stopping point could be set at 420 µm (initial in 360 µm) and after, the coating mechanism takes place simultaneously with the agglomeration one. The monitoring parameters of the system were able to identify the initial phase of defluidization, as well as it was possible to control the process by using Fuzzy Logic and to stabilize the operation for high rates of the coating suspension atomized onto the bed / Doutorado / Engenharia de Processos / Doutor em Engenharia Química

Die zuverlässige Einhaltung von Lieferzusagen stellt ein wichtiges Kriterium bei der Auswahl der Teilnehmer eines auftragsbezogenen Produktionsnetzes dar. Für die objektive Bewertung der Lieferzuverlässigkeit der potenziellen Netzwerkteilnehmer bedarf es der Quantifizierung der relevanten Unsicherheiten integriert in einen allgemein gültigen Ansatz der Verfügbarkeitsprüfung. Die Arbeit stellt daraus resultierend Ansätze zur Berechnung der Unsicherheit vor. Durch die Quantifizierung der Unsicherheit innerhalb der Unternehmen ergibt sich zudem die Möglichkeit der flexiblen, situationsabhängigen Nutzung des für langfristige Rahmenverträge reservierten Sicherheitsbestandes zur Befriedigung kurzfristiger Anfragen. Diese Aufgabe unterstützt ein konfigurierbares Modell zur Entscheidungsunterstützung, das auf einem Neuro-Fuzzy-System basiert. Die Kennzahlen der Lieferzuverlässigkeit unterliegen einem dynamischen Verhalten während des Wertschöpfungsprozesses in dem auftragsbasierten Produktionsnetz. Durch die Integration dieser Kennzahlen in das Management dieses Prozesses ergibt sich die Möglichkeit, aus der Zunahme der Unsicherheit mögliche Störungen und deren Auswirkungen bereits vor ihrem Eintreten zu erfassen und im Rahmen eines präventiven Störungsmanagements zu agieren.

No description available.

The aim of this research is to calibrate conductive polymer nanocomposite materials for large strain sensing and develop a structural health monitoring algorithm for gossamer structures by using nanocomposites as strain sensors. Any health monitoring system works on the principle of sensing the response (strain, acceleration etc.) of the structure to an external excitation and analyzing the response to find out the location and the extent of the damage in the structure. A sensor network, a mathematical model of the structure, and a damage detection algorithm are necessary components of a structural health monitoring system. In normal operating conditions, a gossamer structure can experience normal strain as high as 50%. But presently available sensors can measure strain up to 10% only, as traditional strain sensor materials do not show low elastic modulus and high electrical conductivity simultaneously. Conductive polymer nanocomposite which can be stretched like rubber (up to 200%) and has high electrical conductivity (sheet resistance 100 Ohm/sq.) can be a possible large strain sensor material. But these materials show hysteresis and relaxation in the variation of electrical properties with mechanical strain. It makes the calibration of these materials difficult. We have carried out experiments on conductive polymer nanocomposite sensors to study the variation of electrical resistance with time dependent strain. Two mathematical models, based on the modified fractional calculus and the Preisach approaches, have been developed to model the variation of electrical resistance with strain in a conductive polymer. After that, a compensator based on a modified Preisach model has been developed. The compensator removes the effect of hysteresis and relaxation from the output (electrical resistance) obtained from the conductive polymer nanocomposite sensor. This helps in calibrating the material for its use in large strain sensing. Efficiency of both the mathematical models and the compensator has been shown by comparison of their results with the experimental data. A prestressed square membrane has been considered as an example structure for structural health monitoring. Finite element analysis using ABAQUS has been carried out to determine the response of the membrane to an uniform transverse dynamic pressure for different damage conditions. A neuro-fuzzy system has been designed to solve the inverse problem of detecting damages in the structure from the strain history sensed at different points of the structure by a sensor that may have a significant hysteresis. Damage feature index vector determined by wavelet analysis of the strain history at different points of the structure are taken by the neuro-fuzzy system as input. The neuro-fuzzy system detects the location and extent of the damage from the damage feature index vector by using some fuzzy rules. Rules associated with the fuzzy system are determined by a neural network training algorithm using a training dataset, containing a set of known input and output (damage feature index vectors, location and extent of damage for different damage conditions). This model is validated by using the sets of input-output other than those which were used to train the neural network. / Ph. D.

O objetivo desse trabalho foi de fazer a modelagem por meio de sistemas de inferência fuzzy da concentração dos gases dissolvidos em óleo mineral isolante à partir dos resultados de ensaios físico-químicos. Dessa forma, objetivou-se estender as técnicas de identificação de falhas em transformadores por meio da análise dos ensaios físico-químicos do óleo isolante. Para tanto adotou-se um mapeamento entre os dados de ensaios físico-químicos e de cromatografia gasosa feito por meio de sistemas de inferência fuzzy. Assim, por meio de resultados de ensaios físico-químicos, como cor, densidade, unidade, entre outro, tem-se uma estimativa da concentração dos gases dissolvidos no óleo mineral isolante do transformador. Assim, torna-se possível empregar técnicas de identificação de falhas baseadas na concentração dos gases dissolvidos, mas, valendo-se dos dados de ensaios físico-químicos.O sistema proposto foi validado por meio de dados reais e os resultados alcançados são compatíveis com aqueles obtidos por meio das técnicas convencionais. / The objective of this work was to do a modeling using the inference fuzzy system of the concentration of dissolved gases in insulating mineral oil getting from the physical-chemical results. The idea was to understand the techniques to identify failures on transformers by analyzing the physical-chemical results of the insulating mineral oil.To do that, the data from physical-chemical results and chromatographic results was mapped using the inference fuzzy system. So, by the results of the physical-chemical experiment such as color, density, humidity and so on, its possible to have a estimation of the concentration of dissolved gases in insulating mineral oil. Therefore, its possible to implement techniques to identify failures based on the concentration of dissolved gases using physical-chemical techniques.The propose system was validated by real data. The results using physical-chemical techniques were similar with the results using conventional techniques.

O objetivo desse trabalho foi de fazer a modelagem por meio de sistemas de inferência fuzzy da concentração dos gases dissolvidos em óleo mineral isolante à partir dos resultados de ensaios físico-químicos. Dessa forma, objetivou-se estender as técnicas de identificação de falhas em transformadores por meio da análise dos ensaios físico-químicos do óleo isolante. Para tanto adotou-se um mapeamento entre os dados de ensaios físico-químicos e de cromatografia gasosa feito por meio de sistemas de inferência fuzzy. Assim, por meio de resultados de ensaios físico-químicos, como cor, densidade, unidade, entre outro, tem-se uma estimativa da concentração dos gases dissolvidos no óleo mineral isolante do transformador. Assim, torna-se possível empregar técnicas de identificação de falhas baseadas na concentração dos gases dissolvidos, mas, valendo-se dos dados de ensaios físico-químicos.O sistema proposto foi validado por meio de dados reais e os resultados alcançados são compatíveis com aqueles obtidos por meio das técnicas convencionais. / The objective of this work was to do a modeling using the inference fuzzy system of the concentration of dissolved gases in insulating mineral oil getting from the physical-chemical results. The idea was to understand the techniques to identify failures on transformers by analyzing the physical-chemical results of the insulating mineral oil.To do that, the data from physical-chemical results and chromatographic results was mapped using the inference fuzzy system. So, by the results of the physical-chemical experiment such as color, density, humidity and so on, its possible to have a estimation of the concentration of dissolved gases in insulating mineral oil. Therefore, its possible to implement techniques to identify failures based on the concentration of dissolved gases using physical-chemical techniques.The propose system was validated by real data. The results using physical-chemical techniques were similar with the results using conventional techniques.

Helicopter rotor system operates in a highly dynamic and unsteady aerodynamic environment leading to severe vibratory loads on the rotor system. Repeated exposure to these severe loading conditions can induce damage in the composite rotor blade which may lead to a catastrophic failure. Therefore, an interest in the structural health monitoring (SHM) of the composite rotor blades has grown markedly in recent years. Two important issues are addressed in this thesis; (1) structural modeling and aeroelastic analysis of the damaged rotor blade and (2) development of a model based rotor health monitoring system. The effect of matrix cracking, the first failure mode in composites, is studied in detail for a circular section beam, box-beam and two-cell airfoil section beam. Later, the effects of further progressive damages such as debonding/delamination and fiber breakage are considered for a two-cell airfoil section beam representing a stiff-inplane helicopter rotor blade. It is found that the stiffness decreases rapidly in the initial phase of matrix cracking but becomes almost constant later as matrix crack saturation is reached. Due to matrix cracking, the bending and torsion stiffness losses at the point of matrix crack saturation are about 6-12 percent and about 25-30 percent, respectively. Due to debonding/delamination, the bending and torsion stiffness losses are about 6-8 percent and about 40-45 percent after matrix crack saturation, respectively. The stiffness loss due to fiber breakage is very rapid and leads to the final failure of the blade. An aeroelastic analysis is performed for the damaged composite rotor in forward flight and the numerically simulated results are used to develop an online health monitoring system. For fault detection, the variations in rotating frequencies, tip bending and torsion response, blade root loads and strains along the blade due to damage are investigated. It is found that peak-to-peak values of blade response and loads provide a good global damage indicator and result in considerable data reduction. Also, the shear strain is a useful indicator to predict local damage. The structural health monitoring system is developed using the physics based models to detect and locate damage from simulated noisy rotor system data. A genetic fuzzy system (GFS) developed for solving the inverse problem of detecting damage from noise contaminated measurements by hybridizing the best features of fuzzy logic and genetic algorithms. Using the changes in structural measurements between the damaged and undamaged blade, a fuzzy system is generated and the rule-base and membership functions optimized by genetic algorithm. The GFS is demonstrated using frequency and mode shape based measurements for various beam type structures such as uniform cantilever beam, tapered beam and non-rotating helicopter blade. The GFS is further demonstrated for predicting the internal state of the composite structures using an example of a composite hollow circular beam with matrix cracking damage mode. Finally, the GFS is applied for online SHM of a rotor in forward flight. It is found that the GFS shows excellent robustness with noisy data, missing measurements and degrades gradually in the presence of faulty sensors/measurements. Furthermore, the GFS can be developed in an automated manner resulting in an optimal solution to the inverse problem of SHM. Finally, the stiffness degradation of the composite rotor blade is correlated to the life consumption of the rotor blade and issues related to damage prognosis are addressed.