Year 2017
Sanna HyvönenSemi-automatic parametrization of dynamic models using plant data
The aim of this thesis was to develop a new methodology for estimating parameters of NAPCON ProsDS dynamic simulator models to better represent data containing several operating points. Before this thesis, no known methodology had existed for combining operating point identification with parameter estimation of NAPCON ProsDS simulator models.
The methodology was designed by assessing and selecting suitable methods for operating space partitioning, parameter estimation and parameter scheduling. Previously implemented clustering algorithms were utilized for the operating space partition. Parameter estimation was implemented as a new tool in the NAPCON ProsDS dynamic simulator and iterative parameter estimation methods were applied. Finally, lookup tables were applied for tuning the model parameters according to the state.
The methodology was tested by tuning a heat exchanger model to several operating points based on plant process data. The results indicated that the developed methodology was able to tune the simulator model to better represent several operating states. However, more testing with different models is required to verify general applicability of the methodology.
Thesis electronical version can be downloaded from here
This info last modified 27 Apr 2024 by Jukka Kortela
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