On-Line Monitoring of Batch Processes Using Enhanced Multiway Independent Component Analysis:A Case Study of Penicillin Cultivation Process

• Main Authors: Yu-Lung Shentu, 申屠瑀龍
• Other Authors: Chun-Chin Hsu
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/07053454395651555072

碩士 === 朝陽科技大學 === 工業工程與管理系碩士班 === 98 === Batch processes play an important role in many industries. Several methods of multivariate statistical analysis have been developed to monitor batch processes. Multiway Principal Component Analysis（MPCA）has shown a powerful monitoring performance in many batch processes. However, Principal components（PCs）of the process should be assumed to follow Gaussian distribution and traditional MPCA has a shortcoming of equal batch. In fact, the collected data from industrial processes rarely follow Gaussian distribution. In order to improve these drawbacks, Multiway Independent Component Analysis（MICA）has been developed. It combines different unfolding methods to overcome the mentioned disadvantage. Hence, MICA provides better monitoring performance than MPCA in cases with non-Gaussian variables. However, the traditional MICA based monitoring scheme only considers the magnitude of recent observations but ignores the information of previous observations. Hence, traditional MICA monitoring method will cause delayed fault detection problems. It also causes the cost of the loss and waste of time. As mentioned above, in order to enhance the detectability of traditional MICA based monitoring method, an Enhanced MICA（EI）statistic will be proposed. The study combines two unfolding methods, and then selects independent components（ICs）. The Exponentially Weighted Moving Average（EWMA） method will be used to predict the changing direction of process mean and then EI statistic will be developed. The proposed method was used to detect faults in the fed-batch penicillin cultivation process. The simulation results clearly demonstrate the power and advantages of the proposed method in comparison to MPCA and MICA.