Paper Info
Title
Quality assurance at continuous HOT-DIP galvanizing lines by neuro-model assisted fuzzy-control
Abstract
The importance of improving product quality at continuous hot-dip galvanizing lines with air knives steadily grows. So the developed solutions have to be intelligent, adaptive and modular. This paper describes the revision of a conventional non-adaptive control strategy towards a modem solution using methods of computational intelligence. The already existing feedforward control is complemented by a neural process model and a neuro-fuzzy controller replaces the previously used conventional process controller. Both components are embedded carefully into the control environment so that consumption of time and material for the installation period can be held low. The neural process model is optional and is used for model-based control so that the process inherent measurement dead-time is avoided. The new control arrangement is adaptive, saves zinc, guarantees a more constant coating and relieves the operators.
Year
DOI
Venue
1998
10.1142/S0218488598000136
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Keywords
Field
DocType
quality assurance,continuous hot-dip,fuzzy control,neural nets,feedforward control
Control theory,Galvanization,Computational intelligence,Control engineering,Artificial intelligence,Modular design,Fuzzy control system,Artificial neural network,Machine learning,Mathematics,Feed forward,Quality assurance
Journal
Volume
Issue
ISSN
6
2
0218-4885
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Stefan Wagner100.34
Hans-dieter Kochs2245.38