Paper Title:
Blast Furnace Fault Diagnosis Based on Nonlinear Fuzzy Neural Identification
  Abstract

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, 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
$32.00
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