Diagnostics and control of pressurized reactors using artificial neural networks

Andreas Ikonomopoulos; Lefteri H. Tsoukalas; Robert E. Uhrig

doi:10.1117/12.139988

16 September 1992 Diagnostics and control of pressurized reactors using artificial neural networks

Andreas Ikonomopoulos, Lefteri H. Tsoukalas, Robert E. Uhrig

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139988
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

A methodology employing artificial neural networks and fuzzy arithmetic in the diagnosis and control of complex systems such as pressurized water reactors is presented. Fuzzy numbers represent the linguistic values of plant-specific variables, e.g., performance or availability. The notion of a virtual instrument, i.e., a software-based measuring device calibrated to the idiosyncrasies of a specific system is used. Neural networks perform a mapping of physically measurable parameters to fuzzy numbers called Virtual Measurement Values (VMV). The methodology is tested with start-up data from an experimental nuclear reactor. The results demonstrate the very good capacity of such virtual instruments for failure-tolerance and suggest the possibility of developing alternative algorithms for diagnostics and control.

Citation Download Citation

Andreas Ikonomopoulos, Lefteri H. Tsoukalas, and Robert E. Uhrig "Diagnostics and control of pressurized reactors using artificial neural networks", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139988

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