Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/79815
Type: Artigo de periódico
Title: Reviving traditional blast furnace models with new mathematical approach
Author: de Medeiros, FTP
Noblat, SJX
Fileti, AMF
Abstract: This paper describes how traditional analytical blast furnace (BF) models can be revived by the inclusion of new mathematical tools. Combining some fundamental models with new mathematical algorithms can create efficient and simple to use hybrid models. A hybrid model based on artificial neural network (ANN) and its industrial application to the new BF No. 3 at Companhia Siderurgica Nacional (CSN, Volta Redonda, Brazil) was developed, tested and put in use. In BF operation, which is a multivariable complex process subject to oscillations in raw material characteristics, a precise model is essential to adjust charging and blow conditions to match productivity, chemical quality and target costs. A neural model was developed in order to estimate chemical and thermal parameters to feed a first principles model capable of evaluating alternative operation standards. As a consequence, operation efficiency is being enhanced, leading to higher productivity and lower costs.
Subject: modelling
neural network
ironmaking
blast furnace
Country: Inglaterra
Editor: Maney Publishing
Rights: fechado
Identifier DOI: 10.1179/174328107X203796
Date Issue: 2007
Appears in Collections:Unicamp - Artigos e Outros Documentos

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