【作者】 陈庆； 范玉飞； 李少远； 席裕庚；

【Author】 CHEN Qing Fan Yufei LI Shaoyuan XI Yugeng (Institute of Automation, Shanghai Jiao Tong University, Shanghai 200030)

【机构】 上海交通大学自动化研究所；

【摘要】 结合连续退火炉研究了大工业过程的产品质量在线动态建模问题。这类生产过程由多个子过程构成,各子过程设定值与最终产品质量存在复杂的非线性关系。为了根据实际工况实时优化各子过程设定值,必须利用实际测量数据动态更新产品质量模型。为了达到这种需求,利用一种新颖的广义生长-修剪RBF网络建立了产品质量模型。该网络的学习算法是串行的,可以进行动态建模。最后,结合某钢厂的连退炉进行了应用研究,利用实际数据建立了加热炉各段炉温和出口带钢温度的质量模型。更多还原

【Abstract】 Dynamic modeling for the quality of large-scale process is studied in this paper combined with continuous annealing process. This kind of process is continued with several sub-processes. There is complex nonlinear mapping between the sub-process set points and the final quality. The quality model should be constructed and updated based on the new data from the real process in order to optimize the set point of each sub-process dynamically. To meet this command, a novel generalized growing and pruning RBF neural network is used to establish the quality model. GGAP-RBF is a sequential learning algorithm so that we can establish the model dynamically. Last, we do some on-line application study on the continuous annealing furnace in a steel factory. The quality model between the furnace temperature of each zone in the furnace and the exit strip temperature is constructed.更多还原