Stacking approaches for the design of a soft sensor for a Sulfur Recovery Unit

Author

Fortuna, L. ; Graziani, S. ; Napoli, G. ; Xibilia, M.G.

Author_Institution

Universita degli Studi di Catania

fYear

2006

fDate

6-10 Nov. 2006

Firstpage

229

Lastpage

234

Abstract

In the paper a soft sensor designed to estimate the acid gases hydrogen sulfide (H₂S) in the tail stream of a sulphur recovery unit (SRU) in a refinery located in Sicily, Italy, is described. In particular a model stacking approach is proposed to improve the estimation performance of the soft sensor. Neural networks, principal component analysis (PCA) and partial least squares (PLS) approaches are used for the realization of the first level´s models and Simple average, neural networks and PLS are used as combination approaches. The validity of the proposed aggregation strategies has been verified by a comparison with the performance of a neural model. The obtained soft sensor will be implemented in the refinery in order to replace the measurement device during maintenance and guarantee continuity in the monitoring and control of the plant

Keywords

computerised monitoring; industrial plants; least squares approximations; neural nets; principal component analysis; production engineering computing; refining; PCA; acid gases hydrogen sulfide; aggregation strategies; neural networks; partial least squares; plant control; plant monitoring; principal component analysis; soft sensor design; stacking approaches; sulphur recovery unit; Gas detectors; Gases; Hydrogen; Least squares methods; Monitoring; Neural networks; Principal component analysis; Sensor phenomena and characterization; Stacking; Tail; Neural models; Plant monitoring; Refineries processes; Soft sensors; Stacking approaches;

fLanguage

English

Publisher

ieee

Conference_Titel

IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on

Conference_Location

Paris

ISSN

1553-572X

Print_ISBN

1-4244-0390-1

Type

conf

DOI

10.1109/IECON.2006.347953

Filename

4153574