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. 2022 Oct 31;7(45):41069-41081.
doi: 10.1021/acsomega.2c04445. eCollection 2022 Nov 15.

Dynamic Batch Process Monitoring Based on Time-Slice Latent Variable Correlation Analysis

Le Du  1   2 , Wenhao Jin  1 , Yang Wang  3 , Qingchao Jiang  1   2
Affiliations

Dynamic Batch Process Monitoring Based on Time-Slice Latent Variable Correlation Analysis

Le Du et al. ACS Omega. .

Abstract

Batch processes are generally characterized by complex dynamics and remarkable data collinearity, thereby rendering the monitoring of such processes necessary but challenging. This paper proposes a data-driven time-slice latent variable correlation analysis-based model predictive fault detection framework to ensure accurate fault detection in dynamic batch processes. The three-way batch process data are first unfolded into the two-way time slice. For each single time slice, process data are mapped to both major latent variables and residual subspaces to deal with the variable-wise data collinearity and extract dominant data information. A measurement status is then determined with a canonical correlation analysis of the major latent variables and correlated variables, using both the time and batch perspectives. Prediction-based residuals are generated, which provide the basis for identifying the property of faults detected, namely, static or dynamic. Based on experiments using a simulated penicillin production and an industrial inject molding process, the proposed monitoring scheme has been proven feasible and effective.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Time-slice data unfolding and normalization.
Figure 2
Figure 2
Illustration of the latent variable analysis and variable selection at the k-th time slice.
Figure 3
Figure 3
Schematic of the proposed predictive monitoring scheme.
Figure 4
Figure 4
Simplified flowchart of the FBPCP.
Figure 5
Figure 5
Variable selection results for the FBPCP (the yellow grid represents the selected variable, and the blue grid represents the unselected variable).
Figure 6
Figure 6
Monitoring results using predictive monitoring and MPCA for the FBPCP fault 1.
Figure 7
Figure 7
Monitoring results using predictive monitoring and MPCA for the FBPCP fault 2.
Figure 8
Figure 8
Monitoring results using predictive monitoring and MPCA for the FBPCP fault 3.
Figure 9
Figure 9
Industrial IMP (reprinted with permission from [Data-Driven Two-Dimensional Deep Correlated Representation Learning for Nonlinear Batch Process Monitoring]. Copyright [2020] [IEEE]).
Figure 10
Figure 10
Variable selection results for the IMP (the yellow grid represents the selected variable, and the blue grid represents the unselected variable).
Figure 11
Figure 11
Monitoring results for the IMP fault 1.
Figure 12
Figure 12
Monitoring results for the IMP fault 2.
Figure 13
Figure 13
Monitoring results for the IMP fault 3.
See this image and copyright information in PMC

References

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