Application of Soft Sensor for Monitoring and Control of Refinery Emission
 

I. Mohler1, N. Bolf1, G. Galinec1, M. Golob2
1) University of Zagreb, Faculty of Chemical Engineering and Technology
2) University of Maribor, Faculty of Electrical Engineering and Computer Science
bolf(a)fkit.hr, mgolob(a)uni-mb.si


Summary:
This paper elaborates methods of soft sensor development for dynamic model identification and process control of Sulphur Recovery Unit (SRU) in refinery production. Experimental data are acquired from refinery unit and include available on-line measured variables and on-line analysis.
The results are soft sensor models for optimal control of SRU with aim to minimize SO2 and H2S emissions. The soft sensors were developed conducting multiple linear regression analysis and using neural network-based and fuzzy logic models. From a variety of different model structures the best results were achieved with multi-layer perceptrons and neuro-fuzzy soft sensor models.

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