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

作　　者：Gui-ming YANG Xiao-hui FAN Xu-ling CHEN Xiao-xian HUANG Xi LI 

机构地区：[1]School of Minerals Processing and Bioengineering, Central South University

出　　处：《Journal of Iron and Steel Research International》2015年第11期1002-1008,共7页

基　　金：Item Sponsored by National Natural Science Foundation of China(51174253)

摘　　要：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.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.

关 键 词：iron ore pellet circular cooler model data mining optimization 

分 类 号：TF046.6[冶金工程—冶金物理化学]