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Open AccessArticle

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

by Feifan Shen 1,*, Jiaqi Zheng 2,*, Lingjian Ye 1 and De Gu 3
1
School of Information Science and Engineering, Zhejiang University Ningbo Institute of Technology, Ningbo 315100, China
2
School of Mechanical Engineering and Automation, College of Science & Technology Ningbo University, Ningbo 315300, China
3
College of Computer Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
*
Authors to whom correspondence should be addressed.
Processes 2020, 8(2), 164; https://doi.org/10.3390/pr8020164
Received: 27 December 2019 / Revised: 31 January 2020 / Accepted: 1 February 2020 / Published: 2 February 2020
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process.
Keywords: quality-relevant monitoring; batch processes; data-driven modeling; stochastic programming; bagging algorithm; Bayesian fusion quality-relevant monitoring; batch processes; data-driven modeling; stochastic programming; bagging algorithm; Bayesian fusion
MDPI and ACS Style

Shen, F.; Zheng, J.; Ye, L.; Gu, D. Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes. Processes 2020, 8, 164.

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