LAPSE:2023.5315
Published Article
LAPSE:2023.5315
Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes
Luping Zhao, Xin Huang, Hao Yu
February 23, 2023
In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes.
Keywords
multi-mode model, multi-phase residual recursive model, process monitoring, quality prediction
Suggested Citation
Zhao L, Huang X, Yu H. Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes. (2023). LAPSE:2023.5315
Author Affiliations
Zhao L: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Huang X: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Yu H: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Journal Name
Processes
Volume
9
Issue
8
First Page
1321
Year
2021
Publication Date
2021-07-29
Published Version
ISSN
2227-9717
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PII: pr9081321, Publication Type: Journal Article
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LAPSE:2023.5315
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doi:10.3390/pr9081321
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