Batch Process Monitoring with GTucker2 Model
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- 作者：Lijia Luo ; Shiyi Bao ; Zengliang Gao ; Jingqi Yuan
- 刊名：Industrial & Engineering Chemistry Research
- 出版年：2014
- 出版时间：October 1, 2014
- 年：2014
- 卷：53
- 期：39
- 页码：15101-15110
- 全文大小：430K
- ISSN：1520-5045
文摘
In this paper, the GTucker2 model is proposed for monitoring both even-length and uneven-length batch processes. The GTucker2 model has two prominent advantages. The first one is that it performs tensor decomposition on the three-way data array and thus avoids potential problems of information loss and 鈥渃urse of dimensionality鈥?induced by data unfolding. The second one is that it solves the uneven-length problem in a 鈥渘atural鈥?way without using batch trajectory synchronization, which prevents distorting data and fault patterns and guarantees higher modeling and monitoring precisions. An online batch process monitoring method is then developed by integrating GTucker2 with the moving data window technique. Three monitoring statistics named Q, R2, and T2 statistics are constructed for fault detection and diagnosis. The effectiveness and advantages of the GTucker2-based monitoring method are illustrated by two case studies in a benchmark fed-batch penicillin fermentation process.