Orientadores: Fabio Augusto, Ronei Jesus Poppi / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-21T16:37:50Z (GMT). No. of bitstreams: 1 Mogollon_NoroskaGabrielaSalazar_M.pdf: 3540897 bytes, checksum: 1c33a87e76ee946114306b0838e6978b (MD5) Previous issue date: 2012 / Resumo: Neste trabalho,foram aplicadas metodologias quimiométricas para a caracterização de biodieseis de diversas origens, junto à sua quantificação em misturas BXX (biodiesel em diesel mineral), por meio de dados fornecidos por GC×GC-FID, com uma alta capacidade de separação e detectabilidade, em conjunto com algoritmos de classificação e de resolução de curvas para quantificação de misturas complexas. Primeiramente, foram otimizadas as condições de trabalho aplicadas no preparo de biodiesel, escolhendo-se em seguida o melhor conjunto de colunas que separou os ésteres metílicos provenientes do biodiesel. Foram analisadas amostras de biodieseis de soja com tempos de envelhecimento diferentes nos distintos conjuntos de colunas. Com os dados fornecidos, foi aplicado o algoritmo de classificação analise de componente principal multimodo (MPCA), observando-se a separação entre os biodieseis de óleo de soja novo e envelhecido e, com tais dados, escolheu-se o conjunto que melhor executou a classificação. Em seguida, foram analisadas amostras de biodieseis de diferentes matérias primas, realizando-se a classificação dos mesmos pelo MPCA, observando-se os compostos responsáveis pela diferença entre os mesmos, também identificados por GC×GC-qMS. Finalmente, para a quantificação, foi construída uma curva de calibração analítica com misturas BXX de biodiesel de óleo de soja novo, e para a validação da curva, utilizaram-se biodieseis de diferentes matérias primas, desprezando-se os compostos que diferiram nas diferentes amostras, resultando em um algoritmo útil para quantificar misturas BXX com biodieseis de qualquer fonte / Abstract: Chemometric methods were applied for characterization of biodiesel from different origins, along with their quantification in mixtures BXX (biodiesel in mineral diesel) through data collected from GCxGC-FID. This takes advantage of the high separation capacity and the detectability of the two-dimensional system along with classification algorithms and curve resolution for complex mixture quantification. And the first step, the working conditions for preparing biodiesel were optimized, taking the best set of columns that separated the methyl esters from the biodiesel. Samples of soybean oil biodiesel were analyzed with different aging times in different sets of columns. With the data obtained, the MPCA classification algorithm was applied. Observing the separation between the biodiesels, the data was used for picking the best set of columns for the execution of the classification. Next, biodiesel samples from different raw materials were analysis, performing their classifications by MPCA, observing the compounds responsible for the difference between the biodiesels. These were also identified by GCxGC-qMS. Finally for quantification, a calibration curve with BXX mixtures of new soybean oil biodiesel was constructed for validation. Using biodiesel made out of different raw materials from various sources, ignoring compounds that differ in the biodiesel, a useful algorithm to quantify biodiesel blends BXX with any raw material was obtained / Mestrado / Quimica Analitica / Mestra em Química

ABSRACTThis thesis focuses on the monitoring, fault detection and diagnosis of Wastewater Treatment Plants (WWTP), which are important fields of research for a wide range of engineering disciplines. The main objective is to evaluate and apply a novel artificial intelligent methodology based on situation assessment for monitoring and diagnosis of Sequencing Batch Reactor (SBR) operation. To this end, Multivariate Statistical Process Control (MSPC) in combination with Case-Based Reasoning (CBR) methodology was developed, which was evaluated on three different SBR (pilot and lab-scales) plants and validated on BSM1 plant layout. / ENEsta tesis se enfoca en la monitorización, detección de defectos y diagnosis de Plantas de Tratamiento de Aguas Residuales (Wastewater Treatment Plants - WWTP), el cual son importantes campos de investigación par un amplio rango de disciplinas en Ingeniería.El objetivo principal es evaluar y aplicar una metodología novel de inteligencia artificial basada en evaluación, monitorización y diagnosis de la operación de Reactores de secuencia por lotes (Sequencing Batch Reactor -SBR). Para lograr este fin, se desarrolla una metodología que combina Control de Procesos Multivariable (Multivariate Statistical Process Control -MSPC) con Razonamiento Basado en Casos (Case-Based Reasoning -CBR)., el cual se evalúa en tres diferentes plantas SBR y se valida en una planta BSM1.

Made available in DSpace on 2014-06-12T15:56:15Z (GMT). No. of bitstreams: 2 arquivo2793_1.pdf: 1387248 bytes, checksum: e99d52780679d746f07f5ff17549301a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010 / É crescente a necessidade de controle de acesso a lugares, serviços e informações. É crescente também a busca por soluções mais eficientes na identificação pessoal. Neste contexto, a biometria, que consiste no uso de características biológicas como mecanismo de identificação, tem sido utilizada com resultados bastante promissores. Dentre as informações utilizadas para identificação dos indivíduos podem ser destacadas a íris, a retina, a face, a impressão digital ou até mesmo a geometria da mão. Dentre as biometrias, o reconhecimento de faces destaca-se por ser uma técnica que apresenta ótimos resultados com baixo custo de implantação. Ela pode ser utilizada nos mais diversos tipos de dispositivos e, em sua forma mais simples, não exige hardware dedicado. A técnica destaca-se ainda por não necessitar da interação do usuário ou qualquer tipo de contato físico para captura e classificação das faces. O presente trabalho é focado no reconhecimento de faces baseado em imagens (2D). Mais precisamente o trabalho visa reduzir ou eliminar os efeitos de variações no ambiente ou na própria face que prejudiquem a sua classificação final. As técnicas examinadas e propostas fazem uso da análise de componentes principais (PCA) para extração de características das imagens de faces frontais. Elas baseiam-se em estudos recentes com o objetivo de melhorar as taxas de classificação mesmo sob condições adversas de aquisição de imagens ou oclusão parcial das faces. Os resultados obtidos mostraram uma superioridade nas taxas de acerto das abordagens propostas em relação às suas técnicas-base quando executadas sobre imagens com algum tipo de variação local. Foi constatado também um grande ganho no tempo de processamento das imagens, o que contribui para aplicar as técnicas propostas em dispositivos com menor capacidade computacional

Alpha-ketoglutaric acid (AKGA) is an organic acid important for the metabolism of essential amino acids as well as for the transfer of cellular energy. It is a precursor of glutamic acid which is produced by the human body during the Krebs Cycle. AKGA has a specific industrial interest as it can be taken as a dietary supplement and is also widely used as a building block in chemical synthesis. Collectively termed as hydrazine (HZs), hydrazine (HZ) and monomethylhydrazine (MMH) are hypergolic fuels that do not need an ignition source to burn. Because of the particular HZs' characteristics the National Aeronautics and Space Administration (NASA) at Kennedy Space Center (KSC) and the US Air Force at Cape Canaveral Air Force Station (CCAFS) consistently use HZ and MMH as hypergolic propellants. These propellants are highly reactive and toxic, and have carcinogenic properties. The handling, transport, and disposal of HZ waste are strictly regulated under the Resource Conservation and Recovery Act (RCRA) to protect human health and the environment. Significant quantities of wastewater containing residuals of HZ and MMH are generated at KSC and CCAFS that are subsequently disposed off-site as hazardous waste. This hazardous waste is shipped for disposal over public highways, which presents a potential threat to the public and the environment in the event of an accidental discharge in transit. NASA became aware of research done using AKGA to neutralize HZ waste. This research indicated that AKGA transformed HZ in an irreversible reaction potentially leading to the disposal of the hypergols via the wastewater treatment facility located at CCAFS eliminating the need to transport most of the HZ waste off-site. New Mexico Highlands University (NMHU) has researched this transformation of HZ by reaction with AKGA to form stabilized pyridazine derivatives. NMHU's research suggests that the treatment of HZ and MMH using AKGA is an irreversible reaction; once the reaction takes place, HZ and/or MMH cannot re-form from the byproducts obtained. However, further knowledge relating to the ultimate end products of the reaction, and their effects on human health and the environment, must still be addressed. The known byproduct of the AKGA/HZ neutralization reaction is 6-oxo-1,4,5,6-tetrahydro-pyridazine-3-carboxylic acid (PCA), and the byproduct of the AKGA/MMH reaction is 1-methyl-6-oxo-4,5-dihydro-pyridazine-3-carboxylic acid (mPCA). This research addressed several primary areas of interest to further the potential use of AKGA for HZ and MMH neutralization: 1) isolation of the end-product of the MMH-AKGA degradation process, 1-methyl-6-oxo-4,5-dihydro-pyridazine-3-carboxylic acid (mPCA), and determination of several physical properties of this substance, 2) evaluation of the kinetics of the reaction of AKGA with HZ or MMH, 3) verification of the chemical mechanism for the reaction of the individual hypergols with AKGA, 4) determination of whether the addition of a silicone-based antifoaming agent (AF), citric acid (CA) and/or isopropyl alcohol (IPA) to the AKGA and HZ or MMH solution interferes with the degradation reaction, 4) application of laboratory bench scale experiments in field samples, and 5) determination of the reaction enthalpy of these reactions.

The main types of hypergolic propellants used at Kennedy Space Center (KSC) are hydrazine (HZ) and monomethylhydrazine (MMH). HZ and MMH are classified as hazardous materials and they are also known to be potentially carcinogenic to humans; therefore, handling these substances and their waste is strictly regulated. The wastes streams from HZ and MMH have been estimated to be the main hazardous wastes streams at KSC. Currently at KSC these wastes are first neutralized using citric acid and then they are transported on public roads for incineration as hazardous materials. A new method using alpha ketoglutaric acid (AKGA) was proposed to treat HZ and MMH wastes. From the reaction of AKGA with HZ and MMH two stable products are formed, 1,4,5,6-tetrahydro-6-oxo-3-pyridazinecarboxylic acid (PCA) and lmethyl-1,4,5,6-tetrahydro-6-oxo-3-pyridazinecarboxylic acid (mPCA), respectively. The cost of purchasing AKGA is greater than the cost of purchasing citric acid; thus, AKGA can only become a cost effective alternative for the treatment of HZ and MMH wastes if the products of the reactions (PCA and mPCA) can be safely disposed of into the sewage system without affecting the treatment efficiency and effluent quality of the wastewater treatment plant (WWTP). In this research mPCA and PCA were analyzed for acute toxicity using fish and crustaceans as well as their effect on the wastewater treatment efficiency and viability using AS microbes, and their biodegradability by AS organisms. Acute toxicity on fish and crustaceans was investigated according to the methods for acute toxicity by USEPA (USEPA Method EPA- 821-R-02-012) using Ceriodaphnia dubia (96 hours) and Pimephales promelas (96 hours) as the test organisms. The effect of mPCA and PCA in the treatment efficiency and viability were iii estimated from respiration inhibition tests (USEPA Method OCSPP 850.3300) and heterotrophic plate counts (HPCs). Lastly, the biodegradability of mPCA and PCA was assessed using the Closed Bottle Test (USEPA Method OPPTS 835.3110). For mPCA, the 96 hours LC50 for C. dubia was estimated at 0.77 ± 0.06 g/L (with a 95% confidence level) and the NOEC was estimated at 0.5 g/L. For P. promelas, the LC50 was above 1.5 g/L but it was noticed that mPCA had an effect on their behavior. Abnormal behavior observed included loss of equilibrium and curved spine. The NOEC on the fish was estimated at 0.75 g/L. PCA did not exhibit a significant mortality on fish or crustaceans. The LC50 of PCA in P. promelas and C. dubia was > 1.5 g/L and the NOEC was 1.5 g/L for both organisms. An Inhibitory effect on the heterotrophic respiration of activated sludge organisms was not observed after exposing them for 180-min to PCA and mPCA at concentrations of up to 1.5 g/L compared to the blank controls. Overall the impact of PCA and mPCA on total respiration rates was small, and only observed at 1,500 mg/L if at all. The difference was apparently caused by inhibition of nitrification rather than heterotrophic inhibition. However due to the variability observed in the measurements of the replicates, it is not possible to firmly conclude that PCA or mPCA at 1,500 mg/L was inhibitory to nitrification. Based on the results from the HPCs, mPCA and PCA did not affect the viability of heterotrophic organisms at 750 mg/L. In the BOD-like closed bottle test using a diluted activated sludge mixed liquor sample, the AS microorganisms were capable of biodegrading up to 67% of a 2 mg/L concentration of PCA (with respect to its theoretical oxygen demand, or ThOD) in 28 days. No biodegradation was observed in the samples containing 2 and 5 mg/L of mPCA after 28 days of incubation using a diluted activated sludge mixed liquor sample as inoculum. iv The results of this study show that mPCA is more toxic than PCA to Ceriodaphnia dubia and Pimephales promelas. However neither mPCA nor PCA had an effect on the heterotrophic respiration of an AS mixed liquor sample at 1.5 g/L and there was probably no significant inhibition of the nitrification respiration. Samples of PCA and mPCA at 2 and 5 mg/L could not be completely degraded (with respect to their total theoretical oxygen demand) by dilute AS biomass during a 28 day incubation period. mPCA did not show significant degradation in the two different biodegradation tests performed.

No description available.

The Integrated Vehicle Dynamics Control (IVDC) concept can influence the vehicle behaviour both longitudinally and laterally with just one upper level control concept and further lower level controllers. This demands for state estimation of the vehicle which also includes estimating parameters of interest for the vehicle dynamicist. The approach to this research is firstly in developing a robust unscented Kalman filter (UKF) estimator for the vehicle side slip tracking and also for cornering stiffness estimation which is then fed to the existing model predictive control allocation (MPCA) controller to enhance the lateral stability of the vehicle for the different manoeuvres studied. Based on these developments, two types of filters are created. One with adaption of distance between center of gravity (COG) and roll center height and another without adaption. The key factor in the estimator development is the time adaptive process covariance matrix for the cornering stiffnesses, with which only the initial values have to be parameterised. Combining this research encompasses effective and adaptive method for a better quality of estimation with a kinematic vehicle model which behaves like a real world vehicle, at least virtually.This study is carried out with the understanding of various optimal estimators, parametric sensitivity analysis and statistical inferences, facilitating a base for robust estimation. Keywords: kalametric, state estimation, design matrix, aliasing, kalman filter, projection algorithm, resolution / Konceptet Integrated Vehicle Dynamics Control (IVDC) kan påverka fordonets beteende både longitudinellt och lateralt med bara ett regler koncept iett övre lager och ytterligare regulatorer på lägre nivåer. Detta kräver tillståndsuppskattning av fordonet som också inkluderar uppskattning av parametrar av intresse för en fordonsdynamiker. Tillvägagångssättet för denna studie är för det första att utveckla en robust tillståndsestimering med hjälp av ett Unscented Kalman Filter (UKF) för att uppskatta ett fordons avdriftsvinkel och även för uppskattning av ett däcks sidkraftskoefficient, vilket sedan används i den befintliga modell-prediktiva regleralgoritmen (MPCA) för att förbättra lateralstabiliteten hos fordonet för de olika studerade manövrarna. Baserat på denna utveckling skapades två typer av filter, ett med anpassning av avståndet mellan tyngdpunkten (COG) och krängcentrumhöjden och ett annat utan anpassning. Nyckelfaktorn i estimeringsutvecklingen är den tidsberoende adaptiva inställningenav processkovariansmatrisen för sidkraftskoefficienterna, med vilken endast de initiala värdena behöver parametriseras. Efter filterutvecklingen identifieras parametrar baserade på en förväntad kundanvändning och en statistisk variansanalys (ANOVA) utförs för att bestämma de mest inflytelserika faktorerna i gruppen. En parameteroptimering utförs för att förbättra uppskattningskvaliteten. Kombinationen av detta arbete omfattar en effektiv och anpassningsbar metod för en bättre uppskattningskvalitet med en kinematisk fordonsmodell som har en fordonsrespons som ett verkligt fordon, åtminstone praktiskt taget. Denna studie har genomförts med förståelse för olika optimala estimatorer, parametrisk känslighetsanalys och statistiska slutsatser, vilket underlättaren bas för robust uppskattning. Nyckelord: kalametric, tillståndsestimering, designmatris, vikningsdistorsion, kalmanfilter,projection algorithm, upplösning

This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.

This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.