Nonlinear data reconciliation with gross error detection and identification for steady-state and dynamic processes
Sensor measurements in a process network inherently contain random noise and/or gross errors. Thus, operational plant data should be pre-conditioned for process control, monitoring, and optimization. This work developed a strategy for nonlinear steady-state and nonlinear dynamic data reconciliation...
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| Format: | Thesis |
| Idioma: | English |
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Quezon City
College of Engineering, University of the Philippines Diliman
2015.
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| Accés en línia: | Abstract |