Application of process data reconciliation in power plants

 

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dc.contributor.advisor Fuls, Wim en_ZA
dc.contributor.author Mathebula, Muhluri Calvin en_ZA
dc.date.accessioned 2017-09-28T05:30:43Z
dc.date.available 2017-09-28T05:30:43Z
dc.date.issued 2017 en_ZA
dc.identifier.citation Mathebula, M. 2017. Application of process data reconciliation in power plants. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/25449
dc.description.abstract The operation of power plants and chemical processes requires process measurements for optimal operations. Process measurements are essential for plant performance optimization, process monitoring and process control. It is vital to have reliable and accurate process data to achieve process optimization. However, process measurements are inevitably subject to measurement errors. These measurement errors are classified as random and gross errors. Data reconciliation technique is an effective data treatment method that is used in chemical processes to enhance the quality of process data. The purpose of data reconciliation is to reduce random errors to achieve measurements which are as accurate and reliable as possible. Data reconciliation technique uses available process measurements to produce consistent and accurate estimates, so close to the true values that they satisfy model constraints. Further, data reconciliation technique depends on measurement redundancy to perform reconciliation and produce reliable estimates. In addition, data reconciliation can also provide estimates of unmeasured observable variables. Process data reconciliation is not complete without a gross error detection strategy that can effectively detect and eliminate gross errors in measurements. Data reconciliation is applied to linear and nonlinear steady state processes with measured and partially measured variables. Heat exchanger and steam generator models with nonlinear mass and energy constraints are used. The reconciliation process is applied in a feed water flow measurements model to illustrate the applicability of data reconciliation. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Mechanical Engineering en_ZA
dc.title Application of process data reconciliation in power plants en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Engineering and the Built Environment
dc.publisher.department Department of Mechanical Engineering en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc (Eng) en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Mathebula, M. C. (2017). <i>Application of process data reconciliation in power plants</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering. Retrieved from http://hdl.handle.net/11427/25449 en_ZA
dc.identifier.chicagocitation Mathebula, Muhluri Calvin. <i>"Application of process data reconciliation in power plants."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering, 2017. http://hdl.handle.net/11427/25449 en_ZA
dc.identifier.vancouvercitation Mathebula MC. Application of process data reconciliation in power plants. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering, 2017 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/25449 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Mathebula, Muhluri Calvin AB - The operation of power plants and chemical processes requires process measurements for optimal operations. Process measurements are essential for plant performance optimization, process monitoring and process control. It is vital to have reliable and accurate process data to achieve process optimization. However, process measurements are inevitably subject to measurement errors. These measurement errors are classified as random and gross errors. Data reconciliation technique is an effective data treatment method that is used in chemical processes to enhance the quality of process data. The purpose of data reconciliation is to reduce random errors to achieve measurements which are as accurate and reliable as possible. Data reconciliation technique uses available process measurements to produce consistent and accurate estimates, so close to the true values that they satisfy model constraints. Further, data reconciliation technique depends on measurement redundancy to perform reconciliation and produce reliable estimates. In addition, data reconciliation can also provide estimates of unmeasured observable variables. Process data reconciliation is not complete without a gross error detection strategy that can effectively detect and eliminate gross errors in measurements. Data reconciliation is applied to linear and nonlinear steady state processes with measured and partially measured variables. Heat exchanger and steam generator models with nonlinear mass and energy constraints are used. The reconciliation process is applied in a feed water flow measurements model to illustrate the applicability of data reconciliation. DA - 2017 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2017 T1 - Application of process data reconciliation in power plants TI - Application of process data reconciliation in power plants UR - http://hdl.handle.net/11427/25449 ER - en_ZA


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