"In order to reduce operating and maintenance costs it is of great relevance to assess how a plant behaves and how the deterioration (in terms performance degradation due to fouling, erosion, etc.) of each component affects the plant.. This paper shows a methodology developed by the University of Roma TRE for the production optimization of a pool of power plants. The method uses data collected by the plant monitoring system as a basis for optimization, taking into account maintenance aspects in relation to plant component life consumption and related risks. This was possible in a fast and reliable way by adopting suitable neural models that represent the physical-chemical model behavior."

Cerri, G., Chennaoui, L., Gazzino, M., Giovannelli, A., Salvini, C. (2011). Automatic Operator Support System Based on a Neural Network Monitoring System. In Proceedings of the 24th International Congress on Condition Monitoring and Diagnostic Engineering Management.

Automatic Operator Support System Based on a Neural Network Monitoring System

CERRI, Giovanni;GIOVANNELLI, AMBRA;SALVINI, Coriolano
2011-01-01

Abstract

"In order to reduce operating and maintenance costs it is of great relevance to assess how a plant behaves and how the deterioration (in terms performance degradation due to fouling, erosion, etc.) of each component affects the plant.. This paper shows a methodology developed by the University of Roma TRE for the production optimization of a pool of power plants. The method uses data collected by the plant monitoring system as a basis for optimization, taking into account maintenance aspects in relation to plant component life consumption and related risks. This was possible in a fast and reliable way by adopting suitable neural models that represent the physical-chemical model behavior."
0-9541307-2-3
Cerri, G., Chennaoui, L., Gazzino, M., Giovannelli, A., Salvini, C. (2011). Automatic Operator Support System Based on a Neural Network Monitoring System. In Proceedings of the 24th International Congress on Condition Monitoring and Diagnostic Engineering Management.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/278279
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