engleski

Software sensor for sulphur recovery unit control

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.

sulphur recovery unit ; process identification ; soft sensor ; neural fuzzy network

Rad je prezentiran na skupu The Ninth International Conference on Chemical and Process Engineering (ICheaP-9) ; Sauro Pierucci (ur.) ; ISBN 978-88-95608-01-3.