With the rapid growth of biotechnology and the PAT (Process Analytical Technology) initiative in the pharmaceutical industry, more attention is being focused on monitoring bioreactor production to create a safe production environment and obtain a high-quality product. However, a bioreactor is difficult to monitor mainly due to the following reasons: 1) The process is always batch or semi-batch rather than continuous. 2) The dynamic behavior is highly nonlinear and rarely is a high fidelity model available to describe the dynamic behavior of the process. 3) The micro-organisms can be affected when operating conditions change unpredictably.
Typically, process monitoring methods can be divided into data-driven and knowledge-driven techniques. multiway-PCA developed by Nomikos and MacGregor [1-3] is the first and most widely used data-driven method in batch process monitoring. The basic idea of MPCA is unfolding the three dimensional batch data to two dimensions so as to perform PCA on the data matrix. Based on this pioneering work, more efforts have been made to make the technique more powerful and applicable including: 1) New data unfolding methods (batch-wise; variable-wise; hybrid-wise); and 2) Batch data synchronization (variable indicator; dynamic time warping; correlation optimized warping). Besides data-driven methods, Model Based-PCA (MB-PCA) is based on the fundamental knowledge of process behavior and can be successfully used in batch and continuous processes. If the process model is accurate, then the data unfolding and synchronization steps can be avoided by applying the MB-PCA method.
In this work, we focus on finding an efficient and effective way to perform PCA on a penicillin fermenter simulation model. The detailed fermenter model was developed by G. Birol et al.. MB-PCA method is also applied and compared with MPCA with DTW. The effect of the coupling of manipulated and controlled variables on PCA-based fault detection is estimated.
1. Nomikos, P. and J.F. MacGregor, Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 1995. 37: p. 41-59.
2. Nomikos, P. and J.F. MacGregor, Monitoring of batch processes using multi-way principal component analysis. AIChE Journal, 1994. 40: p. 1361-1375.
3. Nomikos, P. and J.F. MacGregor, Multi-way partial least squares in monitoring batch process. Chemometrics and Intelligent Laboratory Systems, 1995. 30: p. 97-108.
4. Wold, S., et al., Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems, 1998. 44: p. 331-340.
5. Lee, J.-m., C.K. Yoo, and I.-B. Lee, Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis. Journal of Biotechnology, 2004. 110: p. 119-136.
6. Kassidas, A., J.F. MacGregor, and P.A. Taylor, Synchronization of Batch Trajectories Using Dynamic Time Warping. AIChE Journal, 1998. 44(4): p. 864-875.
7. Pravdova, V., B. Walczak, and D.L. Massart, A comparison of two algorithms for warping of analytical signals. Analytica Chimica Acta, 2002. 456: p. 77-92.
8. Wachs, A. and D.R. Lewin. Process Monitoring Using Model-based PCA. in Proc. IFAC Symp. on Dynamics and Control of Process Systems. 1998. Corfu.
9. Birol G., C. Undey, and A. Cinar, A modular simulation package for fed-batch fermentation: penicillin production. Computers and Chemical Engineering, 2002, 26, p. 1553-1565.