A general methodology has been established to set up GT based plant simulators to perform analysis and to identify inverse model parameters. The attention is focused on three categories of inverse problems faced in setting up the plant simulator: i) sizing of components; ii) calibration on the basis of test acceptance data; iii) actual status recognition from data collected by the plant monitoring system. Due to the different nature and requirements of the above problems different solution approaches have been adopted: hybrid stochastic-deterministic algorithms for model calibration and neural techniques for status recognition. An application to a real plant shows the capabilities of the proposed methodology.

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