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Data Mining analysis of the relationship betwen input variables and hot metal silicon in a blast furnace
2005 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The purpose of this report is to get a better understanding of the relationships between input variables and silicon's behaviour in BlueScope Steel's Port Kembla No. 5 Blast Furnace. Over a period of 13 years stretching from 1991 to 2004, potentially important variables affecting the hot metal silicon content of the hot metal produced in the furnace were collected on a daily basis. A process analysis was performed using the data mining program, 'Weka', which is a freeware program developed by the University of Waikato, New Zealand. The data collected were altered, and different datasets compiled, in order to analyse the data without interferences and to reveal previously unknown and hidden relationships. A post-pulverized coal injection dataset was compiled to remove historical variations of limited relevance to current operations. An important variable affecting the hot metal silicon was found to be the amount of Quartz charged in to the furnace. After finding the importance of Quartz charge, a short term, high frequency dataset was collected and analysed to find the true correlations. Quartz is charged in to the furnace to change the slag basicity, but this project shows that a significant fraction of the Quartz transfers into the hot metal as silicon instead of transferring in to the slag as silica as intended. The original data was compiled into a day-to-day difference dataset where each instance contained a difference between two consecutive days. In the difference dataset analysis, the long term variation was removed and the impacts of operational variables on hot metal silicon were revealed. The most significant variable affecting hot metal silicon was hot metal temperature. Hot metal silicon was corrected by hot metal temperature and the analysis was continued. The analysis showed that the relationship between hot metal sulphur and hot metal silicon was not causal, and could be explained by the impact of hot metal temperature. A data mining framework brought valuable new insight into hot metal silicon control in the blast furnace and further effort in this area is strongly recommended.

Place, publisher, year, edition, pages
Keyword [en]
Technology, blast furnace, Data Mining, silicon, silica, statistics, sulphur, clustering
Keyword [sv]
URN: urn:nbn:se:ltu:diva-55876ISRN: LTU-EX--05/241--SELocal ID: caf77ea8-94d3-4a14-bbf3-2ee3d19b31f8OAI: diva2:1029260
Subject / course
Student thesis, at least 30 credits
Educational program
Chemical Engineering, master's level
Validerat; 20101217 (root)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

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