【摘要】 基于传统主元分析(PCA)方法的过程监测算法假定过程是线性的,对于具有强非线性的生产过程,应用其进行在线监测出现误报率过高的现象.为此提出了一种多向核主元分析(MKPCA)算法用于间歇过程的建模与在线监测.利用PenSim2.0软件将青霉素间歇生产过程的三向数据按批次方向展开为二向数据并进行标准化,采用MKPCA算法建立过程模型并用于过程的在线监测,计算T2、SPE统计量及相应的控制限.仿真结果表明,与传统PCA算法相比,MKPCA算法具有更好的监测性能,不仅大大降低了正常运行过程的误报率,而且能够较早地检测出过程中存在的底物流加速率与搅拌功率故障.MKPCA可以有效处理间歇过程批次间存在的非线性属性,获取过程变量间的非线性关系.更多还原

【Abstract】 Traditional principal component analysis(PCA) based methods have been proved to be unreliable when applied to nonlinear process monitoring and false alarms might occur.A method based on multiway kernel principal component analysis(MKPCA) was proposed to capture the nonlinear characteristics of normal batch processes.Based on PenSim2.0,the three-way data of a fed-batch Penicillin fermentation process were spread along batch direction and normalized.Then the MKPCA model of the monitored process was set up,and T2 and SPE statistics and their control limits were calculated using the model.Simulation results showed that the proposed method had better monitoring performance compared with PCA,and that fewer false alarms and small fault detection delay were obtained when the method was used to detect the substrate feed rate and the agitator power faults.MKPCA can effectively extract the nonlinear relationships among the process variables.更多还原