Approaches to Handle Nonlinearities and Nonnormalities in Process Chemometrics
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[S.l. : s.n.]
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RU Radboud Universiteit Nijmegen, 28 juni 2004
Promotor : Buydens, L.M.C. Co-promotor : Melssen, W.J.
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For every industrial process, it is of paramount interest to online monitor the performance of the process and to assess the quality of the products made. In order to meet these goals, the field of process control works on understanding and improving industrial processes. Process chemometrics can be seen as an important contributor to this field. The key approach in process chemometrics is the use of multivariate statistical process control (MSPC). This technique plays an important role, not only for fault detection, but also for the related steps of fault identification, fault diagnosis, fault estimation, and fault reconstruction. Additionally, to enable online and early fault detection it is often necessary to derive the process or product parameters in an indirect way using multivariate calibration (MVC) methods. This makes it possible to predict future parameters on basis of current and past parameter values (time series prediction) but also allows process or product parameters to be obtained from easily measurable parameters that only give indirect information. The drawbacks of the methods described above are that MSPC only uses linear multivariate analysis techniques and strictly assumes the data to be normally distributed. Furthermore, many MVC methods used are also linear or do not result in global or unique solutions. Therefore, the goal of this thesis is twofold: (1) possibly improving the framework of MSPC for fault detection by using techniques that allow both nonlinear and linear multivariate data analysis but also loosen the strict assumption of using normal distributed data and (2) investigating the theoretical and practical benefits of using Support Vector Machines (SVMs) and Least Squares SVMs (LS-SVMs) for nonlinear MVC in process chemometrics. The research has been performed by using actual (semi-) industrial problem cases both with an attempt to solve them but also to use them as an illustration for broader applications.
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