Enhanced Batch Process Monitoring Using Kalman Filter and Multiway Kernel Principal Component Analysis

摘要：

Batch processes are very important in most industries and are used to produce high-value-added products, which cause their monitoring and control to emerge as essential techniques. In this paper, a new method was developed based on Kalman filter(KF) and multiway kernel principal component analysis(MKPCA) for on-line batch process monitoring. Three-way batch data of normal batch process are unfolded batch-wise. Then KPCA is used to capture the nonlinear characteristics within normal batch processes and set up the more accurate monitoring model of batch processes. The on-line monitoring uses a Kalman filter which can estimate the entire trajectory of the current batch run. Comparison of the monitoring performance of the method with that of the traditional multiway principal component analysis(MPCA) method on a benchmark fed-batch penicillin fermentation process shows that the proposed method had better monitoring performance, and that fewer false alarms and small fault detection delay were obtained. In both off-line analysis and on-line batch monitoring, the proposed approach can effectively extract the nonlinear relationships among the process variables.

关键词： Multiway Kernel Principal Component Analysis Kalman Filter Fault Detection Batch Monitoring

作者: QI Yong-sheng WANG Pu FAN Shun-jie GAO Xue-jin JIANG Jun-feng

作者单位: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing Corporate Technology, Siemens Ltd. China ,Beijing 100102

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 5289-5294

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）