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Application of process data reconciliation in power plants
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 optimiza...
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| Format: | Thesis |
| Language: | English |
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Department of Mechanical Engineering
2017
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| _version_ | 1867613756779921408 |
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| access_status_str | Open Access |
| author | Mathebula, Muhluri Calvin |
| author2 | Fuls, Wim |
| author_browse | Fuls, Wim Mathebula, Muhluri Calvin |
| author_facet | Fuls, Wim Mathebula, Muhluri Calvin |
| author_sort | Mathebula, Muhluri Calvin |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/25449 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:41:12.808Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Department of Mechanical Engineering |
| publisherStr | Department of Mechanical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/25449 Application of process data reconciliation in power plants Mathebula, Muhluri Calvin Fuls, Wim Mechanical Engineering 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. 2017-09-28T05:30:43Z 2017-09-28T05:30:43Z 2017 Master Thesis Masters MSc (Eng) http://hdl.handle.net/11427/25449 eng application/pdf Department of Mechanical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Mechanical Engineering Mathebula, Muhluri Calvin Application of process data reconciliation in power plants |
| thesis_degree_str | Master's |
| title | Application of process data reconciliation in power plants |
| title_full | Application of process data reconciliation in power plants |
| title_fullStr | Application of process data reconciliation in power plants |
| title_full_unstemmed | Application of process data reconciliation in power plants |
| title_short | Application of process data reconciliation in power plants |
| title_sort | application of process data reconciliation in power plants |
| topic | Mechanical Engineering |
| url | http://hdl.handle.net/11427/25449 |
| work_keys_str_mv | AT mathebulamuhluricalvin applicationofprocessdatareconciliationinpowerplants |