Extensions to the data reconciliation procedure

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dc.contributor.advisorSwartz, C L Een_ZA
dc.contributor.authorSeager, Mark Thomasen_ZA
dc.date.accessioned2014-11-05T17:35:38Z
dc.date.available2014-11-05T17:35:38Z
dc.date.issued1996en_ZA
dc.descriptionBibliogaphy: leaves 148-155.en_ZA
dc.description.abstractData reconciliation is a method of improving the quality of data obtained from automated measurements in chemical plants. All measuring instruments are subject to error. These measurement errors degrade the quality of the data, resulting in inconsistencies in the material and energy balance calculations. Since important decisions are based on the measurements it is essential that the most accurate data possible, be presented. Data reconciliation attempts to minimize these measurement errors by fitting all the measurements to a least-squares model, constrained by the material and energy balance equations. The resulting set of reconciled measurements do not cause any inconsistencies in the balance equations and contain minimum measurement error. Two types of measurement error can occur; random noise and gross errors. If gross errors exist in the measurements they must be identified and removed before data reconciliation is applied to the system. The presence of gross errors invalidates the statistical basis of data reconciliation and corrupts the results obtained. Gross error detection is traditionally performed using statistical tests coupled with serial elimination search algorithms. The statistical 'tests are based on either the measurement adjustment performed by data reconciliation or the balance equations' residuals. A by-product of data reconciliation, obtained with very little additional effort, is the classification of the system variables. Unmeasured variables may be classified as either observable or unobservable. An unmeasured variable is said to be unobservable if a feasible change in its value is possible without being detected by the measurement instruments. Unmeasured variables which are not unobservable are observable. Measured variables may be classified as either redundant, nonredundant or having a specified degree of redundancy. Nonredundant variables are those which upon deletion of the corresponding measurements, become unobservable. The remaining measured variables are redundant. Measured variables with a degree of redundancy equal to one, are redundant variables that retain their redundancy in the event of a failure in any one of the remaining measurement instruments.en_ZA
dc.identifier.apacitationSeager, M. T. (1996). <i>Extensions to the data reconciliation procedure</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering. Retrieved from http://hdl.handle.net/11427/9255en_ZA
dc.identifier.chicagocitationSeager, Mark Thomas. <i>"Extensions to the data reconciliation procedure."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering, 1996. http://hdl.handle.net/11427/9255en_ZA
dc.identifier.citationSeager, M. 1996. Extensions to the data reconciliation procedure. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Seager, Mark Thomas AB - Data reconciliation is a method of improving the quality of data obtained from automated measurements in chemical plants. All measuring instruments are subject to error. These measurement errors degrade the quality of the data, resulting in inconsistencies in the material and energy balance calculations. Since important decisions are based on the measurements it is essential that the most accurate data possible, be presented. Data reconciliation attempts to minimize these measurement errors by fitting all the measurements to a least-squares model, constrained by the material and energy balance equations. The resulting set of reconciled measurements do not cause any inconsistencies in the balance equations and contain minimum measurement error. Two types of measurement error can occur; random noise and gross errors. If gross errors exist in the measurements they must be identified and removed before data reconciliation is applied to the system. The presence of gross errors invalidates the statistical basis of data reconciliation and corrupts the results obtained. Gross error detection is traditionally performed using statistical tests coupled with serial elimination search algorithms. The statistical 'tests are based on either the measurement adjustment performed by data reconciliation or the balance equations' residuals. A by-product of data reconciliation, obtained with very little additional effort, is the classification of the system variables. Unmeasured variables may be classified as either observable or unobservable. An unmeasured variable is said to be unobservable if a feasible change in its value is possible without being detected by the measurement instruments. Unmeasured variables which are not unobservable are observable. Measured variables may be classified as either redundant, nonredundant or having a specified degree of redundancy. Nonredundant variables are those which upon deletion of the corresponding measurements, become unobservable. The remaining measured variables are redundant. Measured variables with a degree of redundancy equal to one, are redundant variables that retain their redundancy in the event of a failure in any one of the remaining measurement instruments. DA - 1996 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1996 T1 - Extensions to the data reconciliation procedure TI - Extensions to the data reconciliation procedure UR - http://hdl.handle.net/11427/9255 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9255
dc.identifier.vancouvercitationSeager MT. Extensions to the data reconciliation procedure. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering, 1996 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9255en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Mechanical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherEngineeringen_ZA
dc.titleExtensions to the data reconciliation procedureen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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