470643 Parsimonious Modeling Approaches for Batch Process Analysis
In this article, we present a contribution with a lower characteristic complexity (i.e., with a lower number of parameters to be estimated from process data, simpler model structures and more straightforward procedures) and illustrate its application in several case studies, where it is compared with current methodologies. This approach describes the trajectory of process variables by a small number of features that capture the essence of their time evolution. These features are then used for process analysis, diagnosis or prediction, according to the application goal. Models build from profile features are more parsimonious and do not require preliminary synchronization and complex preprocessing tasks. On the other hand, some information is inevitably lost when using features instead of the more detailed information (the measurements).
This framework was applied to three cases studies covering two simulated scenarios and an industrial dataset. The results obtained indicate that implementing a lower complexity framework do not necessarily imply a reduction in performance. On the contrary, the feature-oriented models often outperform conventional methods due to their parsimonious and robust nature. Furthermore, in exploratory frameworks, features can be an effective alternative to uncover process disturbances since they are often related with general characteristics of the trajectory of the process variables, which can be more easily interpreted.
1. Nomikos, P. and J.F. MacGregor, Monitoring batch processes using multiway principal component analysis. AIChE Journal, 1994. 40(8): p. 1361-1375.
2. Nomikos, P. and J.F. MacGregor, Multi-way partial least squares in monitoring batch processes. Chemometrics and intelligent laboratory systems, 1995. 30(1): p. 97-108.
3. Louwerse, D. and A. Smilde, Multivariate statistical process control of batch processes based on three-way models. Chemical Engineering Science, 2000. 55(7): p. 1225-1235.
4. Rännar, S., J.F. MacGregor, and S. Wold, Adaptive batch monitoring using hierarchical PCA. Chemometrics and intelligent laboratory systems, 1998. 41(1): p. 73-81.
5. Yoo, C.K., et al., On-line monitoring of batch processes using multiway independent component analysis. Chemometrics and Intelligent Laboratory Systems, 2004. 71(2): p. 151-163.
6. Zhao, C.H., F.R. Gao, and F.L. Wang, Nonlinear batch process monitoring using phase-based kernel independent component analysis-principal component analysis. Industrial & Engineering Chemistry Research, 2009. 48: p. 9163-9174.
7. He, Q.P. and J. Wang, Statistics pattern analysis: A new process monitoring framework and its application to semiconductor batch processes. AIChE Journal, 2011. 57(1): p. 107-121.
8. Chen, J. and K.-C. Liu, On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science, 2002. 57(1): p. 63-75.
9. Choi, S.W., J. Morris, and I.-B. Lee, Dynamic model-based batch process monitoring. Chemical Engineering Science, 2008. 63(3): p. 622-636.
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