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浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (7): 1266-1269    DOI: 10.3785/j.issn.1008-973X.2010.07.006
    
Genetic programming based twoterm prediction model of iron ore burning through point
SHANG Xiuqin, LU Jiangang, SUN Youxian
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
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Abstract  

A novel classified genetic programming (CGP) algorithm was proposed to model the burning through point (BTP) in the sintering process. The algorithm integrated the Kmedoids clustering into the genetic programming (GP). The Kmedoids clustering was adopted to classify the working conditions of sintering process into K clusters. For each cluster, the twoterm autoregression model of the predicted temperature was conducted by using GP. The two models were the mediumterm model based on the temperature inflexion and the shortterm model based on the temperature neighboring to BTP. The BTP was obtained by the cubic curve fitting of the predicted temperature. Simulation results proved the superiority of the twoterm prediction model.

Published: 01 July 2010
CLC:  TP 181  
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Cite this article:

SHANG Xiu-Qin, LEI Jian-Gang, SUN You-Xian. Genetic programming based twoterm prediction model of iron ore burning through point. J4, 2010, 44(7): 1266-1269.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2010.07.006     OR     http://www.zjujournals.com/eng/Y2010/V44/I7/1266


基于遗传规划的铁矿烧结终点2级预测模型

为了解决铁矿烧结过程中烧结终点(BTP)的建模问题,提出改进的混合分类遗传规划(CGP)算法.算法将K中心聚类算法与遗传规划(GP)相结合,通过K中心聚类算法对烧结过程工况进行分类.对每一类,采用遗传规划建立风箱温度自回归预测模型.模型为2级温度预测模型,即基于温度拐点的中期模型和临近烧结终点处的短期模型.烧结终点通过预测温度的3次曲线拟合得到.实验仿真表明了所提出的2级温度预测模型的有效性.

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