We tested a neural model for variable estimation on a benchmark corresponding to a subsystem of the 320 MW power plant located at Piombino, Italy, namely one of the high pressure feedwater lines. The process simulation is based on a physical approach and was validated on real plant data. The experiments concern the estimation of the dynamic behaviour of seven state variables for the last heater in the feedwater line. The chosen neural model is a modified Resource Allocating Network. The Neural State Estimator (NSE) is structured as a MIMO (Multi Input Multi Output) neural net, able to estimate all the state variables at the same time. The NSE was tested on a realistic amount of data, obtaining industrially relevant results, much better than those obtained by classical estimation methods.

Estimation of Unmeasurable Variables in a Dynamical System by Resource Allocating Networks

CAMASTRA, Francesco;
1995

Abstract

We tested a neural model for variable estimation on a benchmark corresponding to a subsystem of the 320 MW power plant located at Piombino, Italy, namely one of the high pressure feedwater lines. The process simulation is based on a physical approach and was validated on real plant data. The experiments concern the estimation of the dynamic behaviour of seven state variables for the last heater in the feedwater line. The chosen neural model is a modified Resource Allocating Network. The Neural State Estimator (NSE) is structured as a MIMO (Multi Input Multi Output) neural net, able to estimate all the state variables at the same time. The NSE was tested on a realistic amount of data, obtaining industrially relevant results, much better than those obtained by classical estimation methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/27741
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