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dc.contributor.authorVarziri, M. Saeeden
dc.date2008-06-20 16:34:44.586
dc.date.accessioned2008-06-25T19:16:55Z
dc.date.available2008-06-25T19:16:55Z
dc.date.issued2008-06-25T19:16:55Z
dc.identifier.urihttp://hdl.handle.net/1974/1248
dc.descriptionThesis (Ph.D, Chemical Engineering) -- Queen's University, 2008-06-20 16:34:44.586en
dc.description.abstractModel-based control and process optimization technologies are becoming more commonly used by chemical engineers. These algorithms rely on fundamental or empirical models that are frequently described by systems of differential equations with unknown parameters. It is, therefore, very important for modellers of chemical engineering processes to have access to reliable and efficient tools for parameter estimation in dynamic models. The purpose of this thesis is to develop an efficient and easy-to-use parameter estimation algorithm that can address difficulties that frequently arise when estimating parameters in nonlinear continuous-time dynamic models of industrial processes. The proposed algorithm has desirable numerical stability properties that stem from using piece-wise polynomial discretization schemes to transform the model differential equations into a set of algebraic equations. Consequently, parameters can be estimated by solving a nonlinear programming problem without requiring repeated numerical integration of the differential equations. Possible modelling discrepancies and process disturbances are accounted for in the proposed algorithm, and estimates of the process disturbance intensities can be obtained along with estimates of model parameters and states. Theoretical approximate confidence interval expressions for the parameters are developed. Through a practical two-phase nylon reactor example, as well as several simulation studies using stirred tank reactors, it is shown that the proposed parameter estimation algorithm can address difficulties such as: different types of measured responses with different levels of measurement noise, measurements taken at irregularly-spaced sampling times, unknown initial conditions for some state variables, unmeasured state variables, and unknown disturbances that enter the process and influence its future behaviour.en
dc.format.extent1585120 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectParameter estimationen
dc.subjectDynamic modelsen
dc.subjectBSplineen
dc.subjectMaximum likelihooden
dc.subjectDifferential equationsen
dc.subjectStochastic differential equationsen
dc.subjectNonlinear modelsen
dc.titleParameter estimation in nonlinear continuous-time dynamic models with modelling errors and process disturbancesen
dc.typethesisen
dc.description.degreePhDen
dc.contributor.supervisorMcAuley, Kimberly B.en
dc.contributor.supervisorMcLellan, P. Jamesen
dc.contributor.departmentChemical Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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