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|Title:||Augmented dynamic PCA approach for online monitoring of multi-phase batch processes|
Dynamic time warping
|Source:||Doan, X.-T.,Srinivasan, R. (2005). Augmented dynamic PCA approach for online monitoring of multi-phase batch processes. AIChE Annual Meeting, Conference Proceedings : 6673-6695. ScholarBank@NUS Repository.|
|Abstract:||Online monitoring of batch processes using multivariate statistical process control (MSPC) techniques, PCA in particular, has been a challenging problem. The key issues include the 3-D nature of batch data, unequal batch lengths or variation in the timing for key dynamic events in reference database, and incomplete online data for evolving batch as first outlined in Nomikos and MacGregor (1995a,b,c). In addition, complex dynamics of batch processes (ie., highly nonlinear, time-varying, multi-stage/multi-phase) presents the extra challenges to their monitoring (Ündey and Çinar, 2002). To deal with each of these issues, many solutions have been proposed. However, we observe that no single method can handle all of the identified issues. Each of the methods was designed to specifically and particularly deal with one or two of the issues but not all and hence a combination of different methods is necessary. We propose a framework for such a combination by integrating dynamic feature synchronization and dynamic time warping (DTW) with Dynamic Principal Component Analysis (DPCA) proposed in Chen and Liu (2002). The strategy here is firstly identifying the singular points marking different process stages that are then aligned optimally by DTW and later analyzed by DPCA. We use the concept of singular points (SP) as defined in (Srinivasan and Qian, 2005) and observe that a SP breaks normal correlation of residuals from the best fit of recent moving window. In addition, we propose and implement an algorithm for online DTW application by removing the end-point constraint and considering the optimization of all possible end-point matching. Even though this could lead to sub-optimal warping, the modified DTW algorithm can be implemented online in a computationally efficient fashion. For DPCA monitoring, scaling against batch mean trajectory is selected because the goal is to detect deviations from the desired operation. The proposed method, which is called augmented DPCA, is implemented on PenSim simulation - a dynamic simulation of fed-batch penicillin production (Undey and Cinar, 2002). Original DPCA as proposed in Chen and Liu (2002) is also implemented. Comparison between augmented DPCA and original DPCA shows that the augmented DPCA outperforms the original one in monitoring PenSim. The superiority of augmented DPCA demonstrates the need for integrating different methods for online monitoring of multi-stage batch processes.|
|Source Title:||AIChE Annual Meeting, Conference Proceedings|
|Appears in Collections:||Staff Publications|
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