Online uses of first-principles models include nonlinear model predictive control, softsensors, real-time optimization, and real-time process monitoring, among others. The industrial implementation of these applications needs accurate adaptive models and reconciled data. The simultaneous reconciliation and update of parameters of a first- principles model can be achieved using an optimization framework that exploits physical and analytical redundancy of information. This paper demonstrates this concept by means of an industrial case-study. The case-study is a multi-stage centrifugal compressor for which a first-principles model was recently developed. The update of the model parameters is necessary to capture slowly progressing mechanical degradation (e.g. due to fouling and erosion). The reconciliation of the data is necessary for reducing downtime of the online model-based applications caused by gross errors. Two industrial cases including sensor failures were analysed. Applying the proposed framework, it was possible to reconcile the measurements for both cases. © 2014 Elsevier B.V.
|Titolo:||Simultaneous Nonlinear Reconciliation and Update of Parameters for Online Use of First-Principles Models: An Industrial Case-Study on Compressors|
|Autori interni:||MANENTI, FLAVIO|
|Data di pubblicazione:||2014|
|Appare nelle tipologie:||02.1 Contributo in Volume|