Blast Furnace Fault Diagnosis Based on Nonlinear Fuzzy Neural Identification

Abstract:

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Iron and steel industry is the pillar industry, and blast furnace smelting is an important part of it. Through analysis of parameters that affect the stability of blast furnace conditions, we can determine which fault condition to happen and promptly take appropriate measures to eliminate. Thereby we can effectively reduce economic losses. For the blast furnace process has features such as nonlinear, large time-delay and strong coupling, we take use of fuzzy neural network to have furnace fault identified, in order that it can be shown at the stage of fault premonition. To do that, we can reduce or avoid the accidence.

Info:

Periodical:

Advanced Materials Research (Volumes 204-210)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

1254-1257

DOI:

10.4028/www.scientific.net/AMR.204-210.1254

Citation:

L. Pan and X. Hui, "Blast Furnace Fault Diagnosis Based on Nonlinear Fuzzy Neural Identification", Advanced Materials Research, Vols. 204-210, pp. 1254-1257, 2011

Online since:

February 2011

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Price:

$35.00

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