Nonlinear dynamic data reconciliation and bias estimation of process measurements in an adiabatic stirred-tank reactor
When process data is taken from the sensors of a plant, errors of varying degrees are inherent. Measured variables will most likely violate dynamic process models. Because of this, large volumes of data may be unreliable for process control, monitoring, and optimization. This paper describes a new m...
| Pubblicato in: | Philippine Engineering Journal 37, 2 (2016(D)). |
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| Natura: | Articolo |
| Lingua: | English |
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| Accesso online: | Also available online for University of the Philippines Diliman. Click here |