86 research outputs found

    Multivariate statistical process control of chemical processes

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    PhD ThesisThe thesis describes the application of Multivariate Statistical Process Control (MSPC) to chemical processes for the task of process performance monitoring and fault detection and diagnosis. The applications considered are based upon polymerisation systems. The first part of the work establishes the appropriateness of MSPC methodologies for application to modern industrial chemical processes. The statistical projection techniques of Principal Component Analysis and Projection to Latent Structures are considered to be suitable for analysing the multivariate data sets obtained from chemical processes and are coupled with methods and techniques for implementing MSPC. A comprehensive derivation of these techniques are presented. The second part introduces the procedures that require to be followed for the appropriate implementation of MSPC-based schemes for process monitoring, fault detection and diagnosis. Extensions of the available projection techniques that can handle specific types of chemical processes, such as those that exhibit non-linear characteristics or comprise many distinct units are also presented. Moreover, the novel technique of Inverse Projection to Latent Structures that extends the application of MSPC-based schemes to processes where minimal process data is available is introduced. Finally, the proposed techniques and methodologies are illustrated by applications to a batch and a continuous polymerisation process.BR1TE EURAM CT 93 0523 (INTELPOL: ESPRTT PROJECT 22281 (PROGNOSIS): Centre of Process Analysis, Chemometrics and Control, University of Newcastle: Chemical Process Engineering Research Institute, Thessaloniki, Greece

    Multivariate hydrometeorological extreme events and their impacts on vegetation: potential methods and applications

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    Trockenheiten und Hitzewellen beeinflussen unsere Gesellschaft und die Vegetation. Insbesondere im Zusammenhang mit dem Klimawandel sind die Auswirkungen auf die Vegetation von besonderer Bedeutung. Im globalen Kohlenstoffkreislauf sind terrestrische Ökosysteme normalerweise Senken von Kohlenstoffdioxid, können sich aber während und nach Klimaextremereignissen in Kohlenstoffquellen verwandeln. Ein entscheidender Aspekt hierbei ist die Rolle verschiedener Pflanzenarten und Vegetationstypen auf verschiedenen Skalen, die die Auswirkungen auf den Kohlenstoffkreislauf beeinflussen. Obwohl durch physiologische Unterschiede zwischen verschiedenen Pflanzenarten unterschiedliche Reaktionen auf Extremereignisse naheliegen, sind diese Unterschiede auf globaler Ebene nicht systematisch ausgewertet und vollständig verstanden. Ein weiter Aspekt ist, dass Klimaextremereignissen von Natur aus multivariat sind. Beispielsweise kann heiße Luft mehr Wasser aufnehmen als kalte Luft. Extremereignisse mit starken Auswirkungen waren in der Vergangenheit häufig multivariat, wie beispielsweise in Europa 2003, Russland 2012, oder den USA 2012. Diese multivariate Natur von Klimaextremen erfordert eine multivariate Perspektive auf diese Ereignisse. Bisher werden meistens einzelne Variablen zu Detektion von Extremereignissen genutzt und keine Kovariation oder Nichtlinearitäten berücksichtigt. Neue generische Workflows, die solche multivariaten Strukturen berücksichtigen, müssen erst entwickelt oder aus anderen Disziplinen übertragen werden, um uns eine multivariate Perspektive auf Klimaextreme zu bieten. Das übergeordnete Ziel der Dissertation ist es, die Erkennung und das Verständnis von Klimaextremen und deren Auswirkungen auf die Vegetation zu verbessern, indem eine breitere multivariate Perspektive ermöglicht wird, die bisherige Ansätze zur Erkennung von Extremereignissen ergänzt

    Development of a Monitoring Framework for the Detection of Diversion of Intermediate Products in a Generic Natural Uranium Conversion Plant

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    The objective of this work is the development of an on-line monitoring and data analysis framework that could detect the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride in a natural uranium conversion plant (NUCP) using a multivariate statistical approach. This was an initial effort to determine the feasibility of this approach for safeguards applications. This study was limited to a 100 metric ton of uranium (MTU) per year NUCP using the wet solvent extraction method for the purification of uranium ore concentrate. A key component in the multivariate statistical methodology was the Principal Component Analysis (PCA) approach for the analysis of data, development of the base model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix. Component mole balances were used to model each of the process units in the NUCP. The decision framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. The IAEA goal for NUCPs of this size is to have a 50% probability of detecting the diversion of 10 MTU over a period of one year; this was also used as the goal of detection for the monitoring framework. An initial sensitivity analysis was also performed on the relationship between the component molar flow rates (state variables) and the process parameters. This sensitivity study identified a few parameters to which some of the state variables were highly sensitive. Several faulty scenarios were developed to test the monitoring framework after the base case or “normal operating conditions” of the PCA model was established. In nearly all of the scenarios, the monitoring framework was able to detect the fault. The detection limit varied depending on the scenario, but it satisfied the limit stated above in nearly of the all cases. For the cases that the goal was not achieved, additional scaling may be able to lower the detection limit to satisfy the goal. Overall this study was successful at meeting the stated objective

    Developing a method for continuously monitoring dissolved organic carbon concentration in boreal forest headwater streams

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    Headwater streams are an important medium through which carbon from the landscape is transported into aquatic ecosystems in the form of dissolved organic carbon (DOC), an ecologically significant and, until the last decade, underestimated pool of mobile carbon. Boreal forests contain a great fraction of the worlds terrestrial carbon and are considered a large carbon sink. However, they are vulnerable to climate change and can easily become sources of atmospheric carbon. To better understand how our landscapes are responding to climate change we can monitor DOC within headwater streams which integrate and quickly respond to changes of the surrounding landscape. However, continuous monitoring for DOC is difficult and must be monitored via proxy-measurements. This thesis demonstrates an approach to develop and monitor the performance of a model developed to estimate DOC from UV-VIS absorbance and other data from in-situ probes

    Application of dynamic partial least squares to complex processes

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    PhD ThesisMultivariate statistical modelling and monitoring is an active area of research and development in both academia and industry. This is due to the economic and safety benefits that can be attained from the implementation of process modelling and monitoring schemes. Most industrial processes in the chemistry-using sector exhibit complex characteristics including process dynamics, non-linearity and changes in operational behaviour which are compounded by the occurrence of non-conforming data points. To date, modelling and monitoring methodologies have focussed on processes exhibiting one of the aforementioned characteristics. This Thesis considers the development and application of multivariate statistical methods for the modelling and monitoring of the whole process as well as individual unit operations with a particular focus on the complex dynamic nonlinear behaviour of continuous processes. Following a review of Partial Least Squares (PLS), which is applicable for the analysis of problems that exhibit high dimensionality and correlated/collinear variables, it was observed that it is inappropriate for the analysis of data from complex dynamic processes. To address this issue, a multivariate statistical method Robust Adaptive PLS (RAPLS) was proposed, which has the ability to distinguish between non-conforming data, i.e. statistical outliers and a process fault. Through the analysis of data from a mathematical simulation of a time varying and non-stationary process, it is observed that RAPLS shows superior monitoring performance compared to conventional PLS. The model has the ability to adapt to changes in process operating conditions without losing its ability to detect process faults and statistical outliers. A dynamic extension, RADPLS, using an autoregressive with exogenous inputs (ARX) representation was developed to model and monitor the complex dynamic and nonlinear behaviour of an Ammonia Synthesis Fixed-bed Reactor. The resultant model, which is resistant to outliers, shows significant improvement over other dynamic PLS based representations. The proposed method shows some limitations in terms of the detection of the fault for its full duration but it significantly reduces the false alarm rate. The RAPLS algorithm is further extended to a dynamic multi-block algorithm, RAMBDPLS, through the conjunction of a finite impulse response (FIR) representation and multiblock PLS. It was applied to the benchmark Tennessee Eastman Process to illustrate its applicability for the monitoring of the whole process and individual unit operations and to demonstrate the concept of fault propagation in a dynamic and nonlinear continuous system. The resulting model detects the faults and reduces the false alarm rate compared to conventional PLS.Ministry of Higher Education and King Abdulaziz University, Saudi Arabi

    Spatiotemporal brain imaging and modeling

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    Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2004.Includes bibliographical references.This thesis integrates hardware development, data analysis, and mathematical modeling to facilitate our understanding of brain cognition. Exploration of these brain mechanisms requires both structural and functional knowledge to (i) reconstruct the spatial distribution of the activity, (ii) to estimate when these areas are activated and what is the temporal sequence of activations, and (iii)to determine how the information flows in the large-scale neural network during the execution of cognitive and/or behavioral tasks. Advanced noninvasive medical imaging modalities are able to locate brain activities at high spatial and temporal resolutions. Quantitative modeling of these data is needed to understand how large-scale distributed neuronal interactions underlying perceptual, cognitive, and behavioral functions emerge and change over time. This thesis explores hardware enhancement and novel analytical approaches to improve the spatiotemporal resolution of single (MRI) or combined (MRI/fMRI and MEG/EEG) imaging modalities. In addition, mathematical approaches for identifying large-scale neural networks and their correlation to behavioral measurements are investigated. Part I of the thesis investigates parallel MRI. New hardware and image reconstruction techniques are introduced to improve spatiotemporal resolution and to reduce image distortion in structural and functional MRI. Part II discusses the localization of MEG/EEG signals on the cortical surface using anatomical information from AMTRI, and takes advantage of the high temporal resolution of MEG/EEG measurements to study cortical oscillations in the human auditory system. Part III introduces a multivariate modeling technique to identify "nodes" and "connectivity" in a(cont.) large-scale neural network and its correlation to behavior measurements in the human motor system.by Fa-Hsuan Lin.Ph.D

    Dielectrophoretic discrimination of pluripotent myoblast with Raman spectroscopic analysis of the cell plasma membrane for application in Huntington's disease

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    Myoblasts are muscle derived mesenchymal stem cell progenitors that have great potential for use in regenerative medicine, especially for cardiomyogenesis grafts and intracardiac cell transplantation. To utilise such cells for pre -clinical and clinical applications, and especially for personalized medicine, it is essential to generate a synchronised, homogenous, population of cells that display phenotypic and genotypic homogeneity within a population of cells. This thesis demonstrates that the biomarker -free technique of dielectrophoresis (DEP) can be used to discriminate cells between stages of differentiation in the C2C12 myoblast pluripotent mouse model. Terminally differentiated myotubes were separated from C2C12 myoblasts to better than 96% purity, a result validated by flow cytometry and Western blotting. To determine the extent to which cell membrane capacitance, rather than cell size, determined the DEP response of a cell, C2C12 myoblasts were co- cultured with GFP- expressing fibroblasts of comparable size distributions (mean diameter -10 gm). A DEP sorting efficiency greater than 98% was achieved for these two cell types, a result concluded to arise from the fibroblasts possessing a larger membrane capacitance than the myoblasts. It is currently assumed that differences in membrane capacitance primarily reflect differences in the extent of folding or surface features of the membrane. However, our finding by Raman spectroscopy that the fibroblast membranes contained a smaller proportion of saturated lipids than those of the myoblasts suggests that the membrane chemistry should also be taken into account.These high levels of discrimination raised more questions about the cell plasma membrane characteristics that may be responsible for the dielectrophoretic response. This prompted to extend the work to a specific neurodegenerative disease, Huntington's disease. Several studies have been revealing the association between plasma membrane dysregulation and Huntington's disease. In particular the feasibility to use peripheral fibroblasts cells from donors affected by the disease, as a forecasting model marker for Huntington. Although there are substantial evidences about the indirect effect of the disease on the plasma membrane, a non -invasive technique that can discriminate and characterise a cell sample is not available. Raman spectroscopy with associated statistical multivariate analysis was used to characterise sub -cellular differences in extracted plasma membranes from peripheral fibroblastic cells in order to elucidate the differences between cells affect and non - affected by the disease. The results clearly showed that indeed the plasma membrane carries differences that can be attributed to the presence of the disease making the plasma membrane an amenable and novel biomarker for Huntington's diseas

    New methods and models for the ongoing commissioning of HVAC systems in commercial and institutional buildings

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    The performance of the HVAC systems in buildings tends to decrease after few years of operation. Equipment and sensors degradation lead to remarkable wastes of energy and money, as well as to the increase of building occupants thermal discomfort. HVAC ongoing commissioning (OCx), the continuation of HVAC commissioning well into the occupancy and operation phase of a building life, has been recognized as a cost-effective strategy to reduce energy wastes, equipment degradation and thermal discomfort. Building Automation Systems (BAS) collect and store huge amount of data for the purpose of building systems control. Those data represent a golden mine of information that can be used for the OCx of the building HVAC systems. This research work develops and validates new methods and models to be used for the OCx of HVAC systems using BAS measurements from commonly installed sensors. A Fault Detection and Identification (FD&I) method for chillers operation, and several virtual sensor models for variables of interest in Air Handling Units (AHUs) are presented. A FD&I method based on Principal Components Analysis (PCA) has been developed and used to detect abnormal operation conditions in an existing chiller operation and identify the responsible variables. The proposed FD&I method has been trained using measurements from summer 2009, and then used to detect abnormal observations from the following seven summer seasons (2010-2016). When the detected abnormal observations were replaced with artificially generated fault-free data, the proposed FD&I method did not detect any abnormal value along those artificially faulty-free variables. In summer 2016 the building operators changed several HVAC system operation set points, the FD&I method was effective in detecting almost 100% of the observations and properly identifying those variables whose set point was changed. For two different operation modes of an AHU several virtual outdoor air flow meters have been developed and the predictions have been compared against short-term measurements using uncertainty analysis and statistical indices. Three models have been investigated when the heat recovery coil was off. Results showed that the model with the simplest mathematical formulation was the most accurate, with the lowest value of uncertainty. When a heat recovery coil at the fresh air intake was on, two virtual flow meters have been developed to predict the outdoor air flow rate without the need of additional sensors. Both the models predicted the outdoor air ratio with good statistical indices: the Mean Absolute Error (MAE) was 0.015 for model a and 0.016 for model b. Three methods for the virtual measurement and/or calibration of air temperature and relative humidity have been developed for different AHU operation modes. These methods are different in terms of modelling strategy, information needed and technical knowledge required for implementation. For instance, results from the correction of the faulty measurements of the outdoor air temperature along a 24 hours period using Method A showed a high virtual calibration capability: MAE = 0.2°C and the Coefficient of Variation, CV-RMSE = 1.7%. A new definition of virtual sensor is proposed at the end of this research work. From a review of publications on virtual sensors for building application, the two most recurrent reason for the implementation of virtual sensor models (costs and practical issues) have been highlighted and integrated into the proposed new definition
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