Learning Fault Diagnosis Knowledge for a FCC Expert System by Genetic Algorithms

Learning Fault Diagnosis Knowledge for a FCC Expert System by Genetic Algorithms

This paper introduces a GA-based Fault Matrix Learning System(GAFMLS) which applies Genetic Algorithms to a FCC expert system (FCCES) to learn a near-optimal fault matrix used in the fault diagnosis of an oil catalytic and cracking unit. The practical running results show that more effective fault m...

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Veröffentlicht in:High technology letters 1997, Vol.3 (2), p.22-26
1. Verfasser: 陈恩红
Format: Artikel
Sprache:eng
Schlagworte:
algorithms
Applied sciences
Crude oil, natural gas and petroleum products
diagnosis
Energy
Exact sciences and technology
Expert
Fault
Fuels
Genetic
Processing of crude oil and oils from shales and tar sands. Processes. Equipment. Refinery and treatment units
systems
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Zusammenfassung:This paper introduces a GA-based Fault Matrix Learning System(GAFMLS) which applies Genetic Algorithms to a FCC expert system (FCCES) to learn a near-optimal fault matrix used in the fault diagnosis of an oil catalytic and cracking unit. The practical running results show that more effective fault matrixes can be generated by GAFMLS, and the reliability and precision of FCC expert system are improved.
ISSN:1006-6748