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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 8
SUMMARY:Application of big data approaches for model-based prediction of sinter quality indices
DESCRIPTION:Sinter with high and consistent quality, produced with low costs and emissions is very important for iron making. Transport and storage degrade sinter quality, generating fines and segregation effects. Conventional sinter quality monitoring is insufficient as it is slow and expensive. Therefore, short-term fluctuations of sinter quality can only be represented inadequately. It is therefore difficult to draw conclusions about the actual process parameters of sinter production or they are based on empirical experience. Consequently, also a data analysis between sinter quality and its effects on daily blast furnace operation is extremely non-transparent and in need of optimization. In this work, a new approach will be introduced to strengthen the data base that describes the sinter quality in short term periods at two different sinter plants from VASD and DK. New on-line measurements will be established, combined, and analysed with Big Data technologies and should allow short term conclusions of the produced sinter. Based on this comprehensive data compilation, Machine Learning (ML) algorithms are trained to predict sinter quality indices.
In this paper, the development of the novel measurement systems, the data preparation, feature selection and first results of the predicted indices are presented. This break-through in continuous high resolution quality monitoring should help to get continuous quality indices for sinter and will support the combined optimization of sinter plant and blast furnace.
CLASS:PUBLIC
DTSTART:20230615T090000
DTEND:20230615T092000
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