O rio Corumbataí, principal tributário do rio Piracicaba, e fonte de abastecimento da cidade de Piracicaba, vêm sofrendo degradação desde a sua nascente, na cidade de Analândia até sua foz, no rio Piracicaba. Entre 2005 e 2006, foram determinados os teores dos elementos: Be, Al, Na, Mg, Ca, K, V, Mn, Fe, Cr, Co, Ni, Cu, Zn, As, Se, Ag, Cd, Sb, Ba, Hg, Pb, Th, Tl, U por Espectrofotometria de massas com fonte de plasma induzido (ICP-MS) e as propriedades físicas, químicas e microbiológicas, na água e sedimento do rio Corumbataí. As coletas foram realizadas em setembro e dezembro de 2005 e em março, junho e setembro de 2006. Na água, os elementos foram determinados na fração dissolvida, filtrando-se a amostra em campo e preservando-se com HNO3 e na fração total, utilizando-se o método de extração aberta com o uso de HNO3 e HCl. No sedimento, os elementos foram determinados na fração trocável, agitando-se por 12 horas em HCl 1 mol L-1 e na fração total, com a extração em forno microonda. Nas amostras de água, foi calculado o Índice de qualidade da água (IQA), o que demonstrou uma diminuição na qualidade ao longo dos anos, principalmente nos pontos localizados na região à jusante de Rio Claro (RJ) e Piracicaba (PI). O Fósforo Total, Turbidez, pH, OD, Clorofila-a, Escherichia Coli, DBO e os metais Fe e Al dissolvido, Mn, Cd e Hg total, apresentaram concentrações acima do Conama 357/2005 para rios classe 2 em diversos pontos e épocas de coleta, ao longo do rio. No sedimento, os metais Cr, Ni, Cu, As, Cd, Hg, Pb não apresentaram valores acima dos preconizados pela Companhia de Tecnologia de Saneamento - CETESB como causadores de efeitos tóxicos. Os outros metais não possuem valores máximos de referência na legislação brasileira. Os elementos que apresentaram fração trocável maior que 50% em todos os pontos de coleta foram: Na, Ca, Mn, Tl, Co, Cu, Be, Pb. A análise estatística multivariada de componentes principais e a análise de agrupamento indica os pontos RJ e PI, como os mais comprometidos e demonstra que o IQA é um bom indicador da qualidade ao longo da bacia hidrográfica. / The Corumbataí River, main tributary of the Piracicaba river and source of water supply to the city of Piracicaba has been suffering degradation from its waterhead in the city of Analândia to its mouth in the Piracicaba river. In 2005 and 2006 were determined the concentration of the elements, Be, Al, Na, Mg, Ca, K, V, Mn, Fe, Cr, Co, Ni, Cu, Zn, As, Se, Ag, Cd, Sb, Ba, Hg, Pb, Th, Tl, U by Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), as well as physicochemical and microbiological proprieties, in the water and sediments of the Corumbataí River. The collections were carried out in September and December of 2005 and March, June and September of 2006. In the water, the elements in the dissolved fraction were determined by filtering the sample in field and preserving themselves with HNO3, and in the total fraction, by using the method of open extration using HNO3 and HCl. In the sediment, the elements were determined in the exchangeable fraction, by agitating for 12 hours in HCl 1 mol L-1 and in the total fraction, with the extration in microwave. In the samples of water, the water quality Index (WQI) was calculated, and the results showed a reduction in the quality throughout the years, mainly in the points located in the region downstream regions of Rio Claro, (RJ), and Piracicaba, (PI). The Total Phosphorus, Turbidity, pH, Dissolved Oxygen, Chlorophyill-a, Escherichia Coli, BOD and the dissolved metals Al and Fe, and total Hg, Mn, Cd presented concentrations above Conama 357/2005 for rivers class 2 in several points and periods of collection, along the river. In the sediment, the Cr, Ni, Cu, As, Cd, Hg, Pb did not present values above the recommended ones by Companhia de Tecnologia de Saneamento - CETESB as responsible for toxic effect. Reference values for the the other metals are not indicated in the Brazilian legislation. The elements that presented bigger exchangeable fraction than 50% in all the collection points were: Na, Ca, Mn, Tl, Co, Cu, Be, Pb. The multivaried statistical analysis of main components and the Hierarchical Cluster Analysis indicated RJ and PI points, as the most damaged and showed that the WQI is a good indicative of quality along the hydrographic basin.

La notion de biomarqueurs est définie comme « une caractéristique mesurée objectivement et évaluée comme indicateur de processus biologiques normaux ou pathologiques, ou de réponses pharmacologiques à une intervention thérapeutique ». L'intérêt scientifique pour les biomarqueurs est de plus en plus important. Ils permettent, entre autres,une meilleure compréhension des processus pathologiques et de diagnostiquer, voire pronostiquer ces pathologies. Les études « omiques » telles que la lipidomique jouent un rôle essentiel dans la découverte de nouveaux biomarqueurs. La lipidomique consiste à explorer le lipidome d'un échantillon biologique et à déceler l'impact de la pathologie sur ce dernier. Les lipides constituent une vaste et importante famille de métabolites retrouvés dans toutes les cellules vivantes, dont leur nombre est estimé à plus de 100 000 espèces chez les mammifères. Ils sont impliqués, notamment, dans le stockage d'énergie et la transduction de signal. Mon travail de thèse a reposé sur la réalisation d'approches lipidomiques en LC-MS sur diverses applications en santé telles que le syndrome de déficit immunitaire combiné sévère associé à une alopécie et une dystrophie des ongles, le syndrome du nystagmus infantile et le rejet de greffe rénale. A cette fin, des analyses statistiques multivariées et univariées ont été employées pour déceler des potentiels lipides biomarqueurs. / Biomarker was defined as "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic intervention". The scientific interest in biomarkers is more and more important. They allow, in particular, to better understand pathogenic processes and to diagnose, even to predict pathologies. "Omics" studies, such as lipidomics, play an essential role in the new biomarkers discovery. Lipidomics consist in exploring biological samples lipidome and in detecting pathogenic impact on this latter. Lipids are a large and important metabolite family found in all living cells. Their quantity is estimated to more than 100,000 species in mammals. They are involved, in particular, in the energy storage and the signal transduction. My PhD thesis involved carrying out lipidomics approaches with LC-MS through various health applications such as severe combined immunodeficiency associated with alopecia syndrome, infantile nystagmus syndrome and renal graft rejection. For this purpose, multivariate and univariate statistical analyses were carried out in order to detect potential lipid biomarkers.

O uso das técnicas de análise multivariada está reservado aos grandes centros de pesquisa, µas grandes empresas e ao ambiente acad^emico. Essas técnicas s~ao muito interessantes porque utilizam simultaneamente todas as variáveis respostas na interpretação teórica do conjunto de dados, levando em conta as correlações existentes entre elas. Uma das principais barreiras para a utilização dessas técnicas é o seu desconhecimento pelos pesquisadores interessados na pesquisa quantitativa. A outra dificuldade é que a grande maioria de softwares que permitem esse tipo de análise (SAS, MINITAB, BMDP, STATISTICA, S-PLUS, SYSTAT, etc.) não são de domínio público. A disseminação do uso das técnicas multivariadas pode melhorar a qualidade das pesquisas, proporcionar uma economia relativa de tempo e de custo, e facilitar a interpretação das estruturas dos dados, diminuindo a perda de informação. Neste trabalho, foram confirmadas algumas vantagens das técnicas multivariadas sobre as univariadas na análise de dados de expe- rimentos agropecuários. As análises foram realizadas com o auxílio do software R, um software aberto, \"amigável\" e gratuito, com inúmeros recursos disponíveis. / The use of the techniques of multivariate analysis is restricted to large centers of research, the higher companies and the academic environment. These techniques are very inte- resting because of the use of all answers variables simultaneously in theoretical interpretation of the data set, considering the correlations between them. One of the main obstacle to the usage of these techniques is that researchers interested in the quantitative research do not know them. The other di±culty is that most of the software that allow this type of analysis (SAS, MINITAB, BMDP, STATISTICA, S-PLUS, SYSTAT etc.) are not in public domain. Publishing the use of Multivariate techniques can improve the quality of the research, decrease the time spend and the cost, and make easy the interpretation of the structures of the data without cause damage of the information. In this report, were con¯rmed some advantages of the multivariate techniques in a univariate analysis for data of agricultural experiments. The analysis were taken with R software, a open software, \"friendly\" and free, with many statistical resources available.

1990 - 2008 Fertility factors in the disctrict of Most in 2000-2010 period Abstract The objective of this study was to find causes of regional fertility differences and context with social and economic indicators. Then to find situation of district of Most in regional comparison and social and economic causes of fertility developement in this district in 2000-2010 period. In first descriptive part there was made the comparative analysis of age structure and fertility indicators in Czech republic, Ústí region and district of Most. The result is different values of the fertility indicators (intensity and timing) and also younger age structure. In the second part were found social and economic factors of regional fertility differences and factors in district of Most with multivariate statistical methods. The cluster analysis confirmed the differences of district of Most and border of North Bohemia too. For use factor analysis and canonical correlation analysis the conclusion was the important factor of regional differences is especially education, but also availability and quality of housing, economic level. Less importace of religion. In the district of Most are important factors of education and indicators of economic level. Key words: fertility factors, regional fertility differences, district of Most,...

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / Na identificaÃÃo das possÃveis fontes e cargas poluidoras das Ãguas superficiais da bacia hidrogrÃfica do rio Jaibaras, desenvolveu-se um trabalho composto por trÃs etapas, com o objetivo de se obter um melhor conhecimento da qualidade das Ãguas. Na primeira etapa (Etapa I), identificaram-se as fontes de poluiÃÃo hÃdrica da parte alta desta sub-bacia, desde as nascentes do rio Jaibaras e seus tributÃrios atà o aÃude Ayres de Souza. O levantamento ocorreu no perÃodo de maio de 2004 a julho de 2005. As principais fontes poluidoras estÃo ligadas aos esgotos domÃsticos e resÃduos sÃlidos despejados no leito e prÃximo Ãs margens dos mananciais. Poucas fontes de poluiÃÃo ligadas à agricultura foram identificadas. O aÃude Ayres de Souza sofre intervenÃÃes de esgotos domÃsticos, disposiÃÃo de resÃduos sÃlidos, inclusive lixÃes a cÃu aberto, carga de produtos oriundos da piscicultura intensiva, ocupaÃÃo das Ãreas de PreservaÃÃo Permanente (APPs) por balneÃrios, residÃncias e atividades de pecuÃria e agricultura. NÃo se tÃm dados conclusivos sobre a contribuiÃÃo da piscicultura para a poluiÃÃo do manancial. Na etapa seguinte (Etapa 2), identificou-se a influÃncia do clima na variabilidade temporal e os fatores determinantes da qualidade das Ãguas no aÃude Ayres de Souza, pelo emprego da AnÃlise de Agrupamento HierÃrquico (AAH) e AnÃlise da Componente Principal (ACP). Os parÃmetros fÃsicos, quÃmicos e biolÃgicos, somando 28 variÃveis, foram medidos em 4 pontos distintos na bacia hidrÃulica, no perÃodo de setembro de 2004 a maio de 2005. Observou-se por meio da AAH que a qualidade da Ãgua sofre maior influÃncia da sazonalidade climÃtica, com descontinuidade geogrÃfica no processo de agrupamentos. A ACP mostrou que 82% da variÃncia total dos dados foi explicada por um modelo com duas componentes. A primeira componente principal està relacionada ao aporte antropogÃnico na bacia hidrÃulica, sendo K+, PO-3 4, Ca2+ e DBO, os elementos de maior peso nesta componente. A segunda componente relaciona-se ao processo de sodificaÃÃo (Na+ e RAS) das Ãguas pelos solos, provenientes da Ãrea sedimentar à montante da bacia hidrÃulica. Na Ãltima etapa (Etapa III), identificaram-se os parÃmetros quÃmicos mais importantes na variabilidade espacial da qualidade da Ãgua no trecho perenizado do rio Jaibaras, pelo emprego da AnÃlise da Componente Principal. Foram realizadas 24 campanhas de coletas de Ãgua, no perÃodo de abril/2002 a junho/2005, em dois pontos ao longo do rio (na saÃda da galeria do aÃude Ayres de Souza e na foz). As variÃveis consideradas foram: pH, condutividade elÃtrica (CE), cÃlcio (Ca2+), magnÃsio (Mg2+), sÃdio (Na+), potÃssio (K+), bicarbonato (HCO- 3), fosfato (PO-3 4), cloreto (Cl-), amÃnia (NH+ 4), nitrato (NO- 3), sulfato (SO- 4) e RelaÃÃo de AdsorÃÃo de sÃdio (RAS). Observou-se por meio da ACP que no inÃcio do trecho perenizado, a qualidade da Ãgua relaciona-se com trÃs fatores, explicando 80% da variÃncia total. O primeiro fator expressa uma componente mineral. ImportÃncia secundÃria teve a poluiÃÃo orgÃnica, seguida pela presenÃa de detergentes e esgotos domÃsticos. Na confluÃncia do rio Jaibaras com o rio AcaraÃ, dois fatores explicaram 81% da variÃncia total dos dados. Neste ponto, a qualidade da Ãgua recebe maior influÃncia da aÃÃo antrÃpica (dejetos dos balneÃrios, fertilizantes nitrogenados e esgotos domÃsticos). A ACP permitiu verificar o efeito dos parÃmetros quÃmicos na variaÃÃo espacial da qualidade das Ãguas superficiais no trecho perenizado do rio Jaibaras. / Aiming to identify the apportionment of pollution sources/factors with a view to get better information about the water quality at Jaibaras watershed, a research composed of three steps was developed. In the first step (Step I), it was identified the water pollution sources at the upland of Jaibaras watershed until Ayres de Souza dam. The survey occurred from May/2004 to Jul/2005. The pollution sources are related about domestical sewages and land disposal, which are poured in the river and close to the repair zones. Few pollution sources can be related to agricultural activity. The Ayres de Souza dam is also under the effect of domestical swage, land disposal as well as other human activities. In the second step (Step II) multivariate statistical techniques (Cluster Analysis/Principal Component Analysis, CA/PCA) were applied to identify weather influence on temporal variability and the factors that determined the water quality in Ayres de Souza dam. It was measured 28 physical, chemical and biological variables in four sampling stations sited in the reservoir, and in four different campaigns from Sept/2004 to May/2005. The CA allowed the identification of the weather variability influency under water quality. Two principal components were extracted, explaining the 82% of the data variance. The first principal component was mainly associated with K+, PO-3 4, Ca2+ e DBO (anthropogenic factor). The second principal component showed a strong loading on Na+ and SAR. It was basically assigned to sodicity factor. Although, there are an intensive fishing activities in the dam it was not possible to relate this activity as a source of water pollution. In the least step (Step III) PCA was applied to the data set on water quality of the Jaibaras River, from Ayres de Souza dam to the mouth of the river, generated during three years (Apr/2002-Jun/2005), to define pollution factors. It was measured 13 physico-chemical variables (pH, EC, Ca2+, Mg2+, Na+, K+), bicarbonato (HCO- 3), fosfato (PO- 3 4), cloreto (Cl-), amÃnia (NH+ 4), nitrato (NO- 3), sulfato (SO- 4) and RelaÃÃo de AdsorÃÃo de SÃdio (RAS) in the two sampling stations (Ayres de Souza dam gallery and Jaibaras mouth). At Ayres de Souza aqueduct three factors were extracted which explained 80% of the total variance. The first factor represent âweatheringâ processes from rocks (meneralization factor). The second factor can be interpreted as an organic pollution and domestical sewages. At Jaibaras River mouth two factors were extracted, explaing 81% of the total variance in the original data set. The identified factors were related do human action (fertilizers and sewages). The multivariate statistical technique allowed the identification of the chemical and weather actions over the water quality at Jaibaras River.

This study assessed the usefulness of multivariate statistical tools to characterize watershed dynamics and prioritize streams for remediation. Three multiple regression models were developed using water quality data collected from Sinking Creek in the Watauga River watershed in Northeast Tennessee. Model 1 included all water quality parameters, model 2 included parameters identified by stepwise regression, and model 3 was developed using canonical discriminant analysis. Models were evaluated in seven creeks to determine if they correctly classified land use and level of fecal pollution. At the watershed level, the models were statistically significant (p < 0.001) but with low r2 values (Model 1 r2 = 0.02, Model 2 r2 = 0.01, Model 3 r2 = 0.35). Model 3 correctly classified land use in five of seven creeks. These results suggest this approach can be used to set priorities and identify pollution sources, but may be limited when applied across entire watersheds.

<p>Vid analys av fluider med akustisk spektroskopi finns ett behov av att finna multivariata metoder för att utifrån akustiska spektra prediktera storheter såsom viskositet och densitet. Användning av artificiella neurala nätverk och bayesiska nätverk för detta syfte utreds genom teoretiska och praktiska undersökningar. Förbehandling och uppdelning av data samt en handfull linjära och olinjära multivariata analysmetoder beskrivs och implementeras. Prediktionsfelen för de olika metoderna jämförs och PLS (Partial Least Squares) framstår som den starkaste kandidaten för att prediktera de sökta storheterna.</p> / <p>When analyzing fluids using acoustic spectrometry there is a need of finding multivariate methods for predicting properties such as viscosity and density from acoustic spectra. The utilization of artificial neural networks and Bayesian networks for this purpose is analyzed through theoretical and practical investigations. Preprocessing and division of data along with a handful of linear and non-linear multivariate methods of analysis are described and implemented. The errors of prediction for the different methods are compared and PLS (Partial Least Squares) appear to be the strongest candidate for predicting the sought-after properties.</p>

Plant safety is the most important concern of chemical industries. Process faults can cause economic loses as well as human and environmental damages. Most of the operational faults are normally considered in the process design phase by applying methodologies such as Hazard and Operability Analysis (HAZOP). However, it should be expected that failures may occur in an operating plant. For this reason, it is of paramount importance that plant operators can promptly detect and diagnose such faults in order to take the appropriate corrective actions. In addition, preventive maintenance needs to be considered in order to increase plant safety. Fault diagnosis has been faced with both analytic and data-based models and using several techniques and algorithms. However, there is not yet a general fault diagnosis framework that joins detection and diagnosis of faults, either registered or non-registered in records. Even more, less efforts have been focused to automate and implement the reported approaches in real practice. According to this background, this thesis proposes a general framework for data-driven Fault Detection and Diagnosis (FDD), applicable and susceptible to be automated in any industrial scenario in order to hold the plant safety. Thus, the main requirement for constructing this system is the existence of historical process data. In this sense, promising methods imported from the Machine Learning field are introduced as fault diagnosis methods. The learning algorithms, used as diagnosis methods, have proved to be capable to diagnose not only the modeled faults, but also novel faults. Furthermore, Risk-Based Maintenance (RBM) techniques, widely used in petrochemical industry, are proposed to be applied as part of the preventive maintenance in all industry sectors. The proposed FDD system together with an appropriate preventive maintenance program would represent a potential plant safety program to be implemented. Thus, chapter one presents a general introduction to the thesis topic, as well as the motivation and scope. Then, chapter two reviews the state of the art of the related fields. Fault detection and diagnosis methods found in literature are reviewed. In this sense a taxonomy that joins both Artificial Intelligence (AI) and Process Systems Engineering (PSE) classifications is proposed. The fault diagnosis assessment with performance indices is also reviewed. Moreover, it is exposed the state of the art corresponding to Risk Analysis (RA) as a tool for taking corrective actions to faults and the Maintenance Management for the preventive actions. Finally, the benchmark case studies against which FDD research is commonly validated are examined in this chapter. The second part of the thesis, integrated by chapters three to six, addresses the methods applied during the research work. Chapter three deals with the data pre-processing, chapter four with the feature processing stage and chapter five with the diagnosis algorithms. On the other hand, chapter six introduces the Risk-Based Maintenance techniques for addressing the plant preventive maintenance. The third part includes chapter seven, which constitutes the core of the thesis. In this chapter the proposed general FD system is outlined, divided in three steps: diagnosis model construction, model validation and on-line application. This scheme includes a fault detection module and an Anomaly Detection (AD) methodology for the detection of novel faults. Furthermore, several approaches are derived from this general scheme for continuous and batch processes. The fourth part of the thesis presents the validation of the approaches. Specifically, chapter eight presents the validation of the proposed approaches in continuous processes and chapter nine the validation of batch process approaches. Chapter ten raises the AD methodology in real scaled batch processes. First, the methodology is applied to a lab heat exchanger and then it is applied to a Photo-Fenton pilot plant, which corroborates its potential and success in real practice. Finally, the fifth part, including chapter eleven, is dedicated to stress the final conclusions and the main contributions of the thesis. Also, the scientific production achieved during the research period is listed and prospects on further work are envisaged. / La seguridad de planta es el problema más inquietante para las industrias químicas. Un fallo en planta puede causar pérdidas económicas y daños humanos y al medio ambiente. La mayoría de los fallos operacionales son previstos en la etapa de diseño de un proceso mediante la aplicación de técnicas de Análisis de Riesgos y de Operabilidad (HAZOP). Sin embargo, existe la probabilidad de que pueda originarse un fallo en una planta en operación. Por esta razón, es de suma importancia que una planta pueda detectar y diagnosticar fallos en el proceso y tomar las medidas correctoras adecuadas para mitigar los efectos del fallo y evitar lamentables consecuencias. Es entonces también importante el mantenimiento preventivo para aumentar la seguridad y prevenir la ocurrencia de fallos. La diagnosis de fallos ha sido abordada tanto con modelos analíticos como con modelos basados en datos y usando varios tipos de técnicas y algoritmos. Sin embargo, hasta ahora no existe la propuesta de un sistema general de seguridad en planta que combine detección y diagnosis de fallos ya sea registrados o no registrados anteriormente. Menos aún se han reportado metodologías que puedan ser automatizadas e implementadas en la práctica real. Con la finalidad de abordar el problema de la seguridad en plantas químicas, esta tesis propone un sistema general para la detección y diagnosis de fallos capaz de implementarse de forma automatizada en cualquier industria. El principal requerimiento para la construcción de este sistema es la existencia de datos históricos de planta sin previo filtrado. En este sentido, diferentes métodos basados en datos son aplicados como métodos de diagnosis de fallos, principalmente aquellos importados del campo de “Aprendizaje Automático”. Estas técnicas de aprendizaje han resultado ser capaces de detectar y diagnosticar no sólo los fallos modelados o “aprendidos”, sino también nuevos fallos no incluidos en los modelos de diagnosis. Aunado a esto, algunas técnicas de mantenimiento basadas en riesgo (RBM) que son ampliamente usadas en la industria petroquímica, son también propuestas para su aplicación en el resto de sectores industriales como parte del mantenimiento preventivo. En conclusión, se propone implementar en un futuro no lejano un programa general de seguridad de planta que incluya el sistema de detección y diagnosis de fallos propuesto junto con un adecuado programa de mantenimiento preventivo. Desglosando el contenido de la tesis, el capítulo uno presenta una introducción general al tema de esta tesis, así como también la motivación generada para su desarrollo y el alcance delimitado. El capítulo dos expone el estado del arte de las áreas relacionadas al tema de tesis. De esta forma, los métodos de detección y diagnosis de fallos encontrados en la literatura son examinados en este capítulo. Asimismo, se propone una taxonomía de los métodos de diagnosis que unifica las clasificaciones propuestas en el área de Inteligencia Artificial y de Ingeniería de procesos. En consecuencia, se examina también la evaluación del performance de los métodos de diagnosis en la literatura. Además, en este capítulo se revisa y reporta el estado del arte correspondiente al “Análisis de Riesgos” y a la “Gestión del Mantenimiento” como técnicas complementarias para la toma de medidas correctoras y preventivas. Por último se abordan los casos de estudio considerados como puntos de referencia en el campo de investigación para la aplicación del sistema propuesto. La tercera parte incluye el capítulo siete, el cual constituye el corazón de la tesis. En este capítulo se presenta el esquema o sistema general de diagnosis de fallos propuesto. El sistema es dividido en tres partes: construcción de los modelos de diagnosis, validación de los modelos y aplicación on-line. Además incluye un modulo de detección de fallos previo a la diagnosis y una metodología de detección de anomalías para la detección de nuevos fallos. Por último, de este sistema se desglosan varias metodologías para procesos continuos y por lote. La cuarta parte de esta tesis presenta la validación de las metodologías propuestas. Específicamente, el capítulo ocho presenta la validación de las metodologías propuestas para su aplicación en procesos continuos y el capítulo nueve presenta la validación de las metodologías correspondientes a los procesos por lote. El capítulo diez valida la metodología de detección de anomalías en procesos por lote reales. Primero es aplicada a un intercambiador de calor escala laboratorio y después su aplicación es escalada a un proceso Foto-Fenton de planta piloto, lo cual corrobora el potencial y éxito de la metodología en la práctica real. Finalmente, la quinta parte de esta tesis, compuesta por el capítulo once, es dedicada a presentar y reafirmar las conclusiones finales y las principales contribuciones de la tesis. Además, se plantean las líneas de investigación futuras y se lista el trabajo desarrollado y presentado durante el periodo de investigación.

Vid analys av fluider med akustisk spektroskopi finns ett behov av att finna multivariata metoder för att utifrån akustiska spektra prediktera storheter såsom viskositet och densitet. Användning av artificiella neurala nätverk och bayesiska nätverk för detta syfte utreds genom teoretiska och praktiska undersökningar. Förbehandling och uppdelning av data samt en handfull linjära och olinjära multivariata analysmetoder beskrivs och implementeras. Prediktionsfelen för de olika metoderna jämförs och PLS (Partial Least Squares) framstår som den starkaste kandidaten för att prediktera de sökta storheterna. / When analyzing fluids using acoustic spectrometry there is a need of finding multivariate methods for predicting properties such as viscosity and density from acoustic spectra. The utilization of artificial neural networks and Bayesian networks for this purpose is analyzed through theoretical and practical investigations. Preprocessing and division of data along with a handful of linear and non-linear multivariate methods of analysis are described and implemented. The errors of prediction for the different methods are compared and PLS (Partial Least Squares) appear to be the strongest candidate for predicting the sought-after properties.

We consider a principal components based decomposition of the expected value of the multivariate quadratic loss function, i.e., MQL. The principal components are formed by scaling the original data by the contents of the loss constant matrix, which defines the economic penalty associated with specific variables being off their desired target values. We demonstrate the extent to which a subset of these ``loss-scaled principal components", i.e., LSPC, accounts for the two components of expected MQL, namely the trace-covariance term and the off-target vector product. We employ the LSPC to solve a robust design problem of full and reduced dimensionality with deterministic models that approximate the true solution and demonstrate comparable results in less computational time. We also employ the LSPC to construct a test statistic called loss-scaled T^2 for multivariate statistical process control. We show for one case how the proposed test statistic has faster detection than Hotelling's T^2 of shifts in location for variables with high weighting in the MQL. In addition we introduce a principal component based decomposition of Hotelling's T^2 to diagnose the variables responsible for driving the location and/or dispersion of a subgroup of multivariate observations out of statistical control. We demonstrate the accuracy of this diagnostic technique on a data set from the literature and show its potential for diagnosing the loss-scaled T^2 statistic as well.