1. Batch Process Monitoring with GTucker2 Model
- Author
-
Lijia Luo, Zengliang Gao, Jingqi Yuan, and Shiyi Bao
- Subjects
Computer science ,General Chemical Engineering ,Synchronization (computer science) ,Process (computing) ,Benchmark (computing) ,Batch processing ,General Chemistry ,Fault (power engineering) ,Algorithm ,Industrial and Manufacturing Engineering ,Fault detection and isolation - Abstract
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 “curse of dimensionality” induced by data unfolding. The second one is that it solves the uneven-length problem in a “natural” 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.
- Published
- 2014