A neural network based model of sinter quality and sinter plant performance indices

• Published in: Ironmaking & steelmaking Vol. 34; no. 2; pp. 109 - 114
• Main Authors: Laitinen, P. J., Saxén, H.
• Format: Journal Article
• Language: English
• Published: London, England Taylor & Francis 01.03.2007; SAGE Publications; Taylor & Francis Ltd
• Subjects: Cold; Consumption; Datasets; Energy consumption; Iron; NEURAL NETWORKS; Permeability; Productivity; Quality control; QUALITY INDICES; Sintering; SINTERMAKING; Steel; Studies; Variables; NEURAL NETWORKS; SINTERMAKING; QUALITY INDICES
• ISSN: 0301-9233; 1743-2812
• DOI: 10.1179/174328107X155312

A prerequisite of a smooth operation of the ironmaking blast furnace is that the quality of the burden is stable. In blast furnaces where sinter is used as the (main) iron bearing material, its quality plays a crucial role in productivity and fuel economy. Simultaneously the corresponding factors must be considered for the sinter plant. The present paper studies the influence of three variables characterising the bedding piles and five sinter plant operation variables on sinter quality, sinter plant productivity, specific fuel consumption and share of cold return fines. Daily mean values for a period of five years of operation were used in the data driven modelling based on feedforward neural networks. The resulting models were found to describe the major changes in the outputs well. The input-output relations captured by the models were analysed by perturbing one input variable of the networks at a time and analysing the predicted behaviour of the outputs.