Publicação: Probabilistic neural network to predict cracks in taphole mud used in blast furnaces
Carregando...
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Techna Srl
Tipo
Artigo
Direito de acesso
Acesso restrito
Resumo
One of the major problems facing Blast Furnaces is the occurrence of cracks in taphole mud, as the underlying causes are not easily identifiable. The absence of this knowledge makes it difficult the use of conventional techniques for predictability and mitigation. This paper will address the application of Probabilistic Neural Network using the Matlab software as a means to detect and control such cracks. The most relevant BF operational variables were picked through the statistic tool "Principal Component Analysis - PCA." Based upon the selection of these variables a probabilistic neural network was built. A set of BF operational data, consisting of 30 controlling variables, was divided into 2 groups, one of which for network training, and the other one to validate the neural network. The neural network got 98% of the cases right. The results show the effectiveness of this tool for crack prediction in relation to clay intrinsic properties and as a result of the fluctuation in operational variables.
Descrição
Palavras-chave
Idioma
Inglês
Como citar
Industrial Ceramics. Faenza: Techna Srl, v. 28, n. 2, p. 133-137, 2008.
