Soft sensor design for a Sulfur Recovery Unit using a clustering based approach

In the paper a Soft Sensor design strategy for an industrial process, via neural NMA model, is described. A general design strategy, based on the automatic selection of regressors of a NMA model is proposed. It is based on the minimization of the cost function of a Gath Geva clustering algorithm. Th...

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Vydáno v:2008 IEEE Instrumentation and Measurement Technology Conference s. 1162 - 1167
Hlavní autoři: Graziani, S., Napoli, G., Xibilia, M.G.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2008
Témata:
Algorithm design and analysis
Delay estimation
Distributed control
Fuzzy clustering
Independent component analysis
Monitoring
NMA models
Performance analysis
Pollution measurement
Principal component analysis
Refining
Regressors selection
Scattering
Soft sensors
ISBN:9781424415403, 1424415403
ISSN:1091-5281
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Shrnutí:In the paper a Soft Sensor design strategy for an industrial process, via neural NMA model, is described. A general design strategy, based on the automatic selection of regressors of a NMA model is proposed. It is based on the minimization of the cost function of a Gath Geva clustering algorithm. The obtained soft sensor will be implemented in a refinery in order to replace the measurement device during maintenance to guarantee continuity in the monitoring and control of the plant.
ISBN:9781424415403
1424415403
ISSN:1091-5281
DOI:10.1109/IMTC.2008.4547215