Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler

dc.contributor.advisorBoje, Edward
dc.contributor.authorPatel, Zubeida
dc.date.accessioned2022-03-16T03:10:20Z
dc.date.available2022-03-16T03:10:20Z
dc.date.issued2021
dc.date.updated2022-03-16T00:14:39Z
dc.description.abstractThis thesis contributes to non-linear continuous-discrete Kalman filtering of multiplex systems through the development of two main ideas, namely, integration of the unscented transforms with linearly implicit methods and incorporation of simulation errors in the state estimation problem. The newly developed techniques are then applied to the technically relevant problem of state estimation on the main components of a utility boiler. State estimators in industrial systems are used as soft-sensors in monitoring and control applications as the most cost effective and practical alternative to telemetering all variables of interest. One such example is in utility boilers where reliable and real-time data characterising its behaviour is used to detect faults and optimise performance. With respect to the state-of-the-art, state estimators display limitations in real-time applications to large-scale systems. This motivates theoretical developments in state estimation as a first part in this thesis. These developments are aimed at producing more practical and efficient algorithms in non-linear continuous discrete Kalman filtering for stiff large-scale industrial systems. This is achieved using two novel ideas. The first is to exploit the similarities between the extended and unscented Kalman filter in order to estimate the Jacobian required for linearly implicit schemes, thereby tightly coupling state propagation and continuous-time simulation. The second is to account for numerical integration error by appending a stochastic local error model to the system's stochastic differential equation. This allows for coarser integration time steps in systems that are otherwise only suited to relatively small step sizes, making the filter more computationally efficient without lowering its potential to construct accurate estimates. The second part of this thesis uses these algorithms to demonstrate the feasibility of on-line state estimation on the main components of a once-through utility power boiler that require in excess of a hundred state variables to capture its behaviour with adequate fidelity. Two separate models of the boiler are developed, a MATLAB® and a Flownex® model, comprising the economiser, evaporators, reheaters, superheaters and furnace. The mathematical MATLAB® model is better suited to real-time execution and is used in the filter. The more sophisticated model is based on a commercial thermal-hydraulic simulation environment, Flownex® , and is used to validate the mathematical modelling philosophies and construct filter observation data. After validating the performance of the filter against ground-truth data provided by the Flownex® model, the filter is demonstrated on historical plant data to illustrate its utility.
dc.identifier.apacitationPatel, Z. (2021). <i>Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler</i>. (). ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/36131en_ZA
dc.identifier.chicagocitationPatel, Zubeida. <i>"Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler."</i> ., ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2021. http://hdl.handle.net/11427/36131en_ZA
dc.identifier.citationPatel, Z. 2021. Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler. . ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/36131en_ZA
dc.identifier.ris TY - Doctoral Thesis AU - Patel, Zubeida AB - This thesis contributes to non-linear continuous-discrete Kalman filtering of multiplex systems through the development of two main ideas, namely, integration of the unscented transforms with linearly implicit methods and incorporation of simulation errors in the state estimation problem. The newly developed techniques are then applied to the technically relevant problem of state estimation on the main components of a utility boiler. State estimators in industrial systems are used as soft-sensors in monitoring and control applications as the most cost effective and practical alternative to telemetering all variables of interest. One such example is in utility boilers where reliable and real-time data characterising its behaviour is used to detect faults and optimise performance. With respect to the state-of-the-art, state estimators display limitations in real-time applications to large-scale systems. This motivates theoretical developments in state estimation as a first part in this thesis. These developments are aimed at producing more practical and efficient algorithms in non-linear continuous discrete Kalman filtering for stiff large-scale industrial systems. This is achieved using two novel ideas. The first is to exploit the similarities between the extended and unscented Kalman filter in order to estimate the Jacobian required for linearly implicit schemes, thereby tightly coupling state propagation and continuous-time simulation. The second is to account for numerical integration error by appending a stochastic local error model to the system's stochastic differential equation. This allows for coarser integration time steps in systems that are otherwise only suited to relatively small step sizes, making the filter more computationally efficient without lowering its potential to construct accurate estimates. The second part of this thesis uses these algorithms to demonstrate the feasibility of on-line state estimation on the main components of a once-through utility power boiler that require in excess of a hundred state variables to capture its behaviour with adequate fidelity. Two separate models of the boiler are developed, a MATLAB® and a Flownex® model, comprising the economiser, evaporators, reheaters, superheaters and furnace. The mathematical MATLAB® model is better suited to real-time execution and is used in the filter. The more sophisticated model is based on a commercial thermal-hydraulic simulation environment, Flownex® , and is used to validate the mathematical modelling philosophies and construct filter observation data. After validating the performance of the filter against ground-truth data provided by the Flownex® model, the filter is demonstrated on historical plant data to illustrate its utility. DA - 2021_ DB - OpenUCT DP - University of Cape Town KW - Electrical Engineering LK - https://open.uct.ac.za PY - 2021 T1 - Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler TI - Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler UR - http://hdl.handle.net/11427/36131 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/36131
dc.identifier.vancouvercitationPatel Z. Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler. []. ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/36131en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Electrical Engineering
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.subjectElectrical Engineering
dc.titleKalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationlevelPhD
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