Title :
Soft sensor design for a Sulfur Recovery Unit using a clustering based approach
Author :
Graziani, S. ; Napoli, G. ; Xibilia, M.G.
Author_Institution :
DIEES, Univ. degli Studi di Catania, Catania
Abstract :
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.
Keywords :
computerised instrumentation; industrial plants; neural nets; process monitoring; sensors; statistical analysis; Gath Geva clustering; clustering based approach; industrial process; neural NMA model; nonlinear moving average models; plant monitoring; refinery; soft sensor; sulfur recovery unit; the cost function minimization; Algorithm design and analysis; Delay estimation; Distributed control; Independent component analysis; Monitoring; Performance analysis; Pollution measurement; Principal component analysis; Refining; Scattering; Fuzzy clustering; NMA models; Regressors selection; Soft sensors;
Conference_Titel :
Instrumentation and Measurement Technology Conference Proceedings, 2008. IMTC 2008. IEEE
Conference_Location :
Victoria, BC
Print_ISBN :
978-1-4244-1540-3
Electronic_ISBN :
1091-5281
DOI :
10.1109/IMTC.2008.4547215
