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...
| Autore principale: | |
|---|---|
| Altri autori: | |
| Natura: | Tesi |
| Lingua: | English |
| Pubblicazione: |
Quezon City
College of Engineering, University of the Philippines Diliman
2015.
|
| Soggetti: | |
| Accesso online: | Abstract |