Optimization of Cooling Process of Iron Ore Pellets Based on Mathematical Model and Data Mining

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• 作者：Gui-ming YANG ^{csuygm@sina.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xiao-hui FAN ; ^{csufanxiaohui@126.com" class="auth_mail" title="E-mail the corresponding author} ; Xu-ling CHEN ; Xiao-xian HUANG ; Xi LI
• 关键词：iron ore pellet ; circular cooler ; model ; data mining ; optimization
• 刊名：Journal of Iron and Steel Research, International
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：22
• 期：11
• 页码：1002-1008
• 全文大小：1530 K

文摘

Cooling process of iron ore pellets in a circular cooler has great impacts on the pellet quality and systematic energy exploitation. However, multi-variables and non-visualization of this gray system is unfavorable to efficient production. Thus, the cooling process of iron ore pellets was optimized using mathematical model and data mining techniques. A mathematical model was established and validated by steady-state production data, and the results show that the calculated values coincide very well with the measured values. Based on the proposed model, effects of important process parameters on gas-pellet temperature profiles within the circular cooler were analyzed to better understand the entire cooling process. Two data mining techniques—Association Rules Induction and Clustering were also applied on the steady-state production data to obtain expertise operating rules and optimized targets. Finally, an optimized control strategy for the circular cooler was proposed and an operation guidance system was developed. The system could realize the visualization of thermal process at steady state and provide operation guidance to optimize the circular cooler.