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Some applications of neural networks for prediction of blast furnace irregularities
Author(s) -
Zuo Guangqing,
Ma Jitang,
Björkman Bo
Publication year - 1998
Publication title -
steel research
Language(s) - English
Resource type - Journals
eISSN - 1869-344X
pISSN - 0177-4832
DOI - 10.1002/srin.199801341
Subject(s) - artificial neural network , blast furnace , multilayer perceptron , perceptron , backpropagation , engineering , artificial intelligence , classifier (uml) , feature (linguistics) , blast furnace gas , pattern recognition (psychology) , computer science , data mining , machine learning , materials science , metallurgy , linguistics , philosophy
The on‐line analysis of operational data and prediction of furnace irregularities, though difficult, are essential for the improvement of the control of blast furnace operation. Three models based on artificial neural networks for the recognition of top gas distribution, distributions of the heat fluxes through the furnace wall, and for the prediction of slips have been designed. The off‐line test results showed that a trained perceptron network could recognise various types of top gas profiles. A classifier consisting of a self‐organising feature map network and a learning vector quantizer could classify the characteristic patterns of heat flux distribution; and a model based on a back propagation network could properly predict the probability of upcoming slips in advance. The most important operational variables needed for predicting slips have also been extracted. It has been proved that the neural network used has a good capability of predicting furnace irregularities.