Extensions to the data reconciliation procedure

 

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dc.contributor.advisor Swartz, C L E en_ZA
dc.contributor.author Seager, Mark Thomas en_ZA
dc.date.accessioned 2014-11-05T17:35:38Z
dc.date.available 2014-11-05T17:35:38Z
dc.date.issued 1996 en_ZA
dc.identifier.citation Seager, M. 1996. Extensions to the data reconciliation procedure. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/9255
dc.description Bibliogaphy: leaves 148-155. en_ZA
dc.description.abstract 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. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Engineering en_ZA
dc.title Extensions to the data reconciliation procedure en_ZA
dc.type Thesis / Dissertation en_ZA
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 & the Built Environment en_ZA
dc.publisher.department Department of Mechanical Engineering en_ZA
dc.type.qualificationlevel Masters en_ZA
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image


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