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Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes. Processes (Basel) 2020. [DOI: 10.3390/pr8020164] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
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.
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Yeo WS, Saptoro A, Kumar P. Adaptive Soft Sensor Development for Non-Gaussian and Nonlinear Processes. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03821] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Wan Sieng Yeo
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
| | - Agus Saptoro
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
| | - Perumal Kumar
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
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Tao Y, Shi H, Song B, Tan S. Distributed Supervised Fault Detection and Diagnosis for a Non-Gaussian Process. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b00005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yang Tao
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Hongbo Shi
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Bing Song
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Shuai Tan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
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