Abstract
The problem of misalignment of the original measurement model is caused by nonlinear, time-varying characteristic of the batch process. In this paper, a method based on geodesic flow kernel (GFK) for feature transfer is proposed. By mapping data into the manifold space, the feature transfer from source domain to target domain is implemented. Distribution adaptation of real-time data and modeling data is performed to reduce the distribution difference between them. The historical data through distribution adaptation is used to establish a regression model to predict the real-time data, by which the unsupervised batch process soft sensor modeling is realized. The application of predicting the concentration of penicillin between different batches during the fermentation of penicillin demonstrated that the prediction accuracy of the model can be improved more effectively than the traditional soft sensor method.
Original language | English |
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Title of host publication | Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020 |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 975-980 |
ISBN (Electronic) | 978-1-7281-5854-9 |
ISBN (Print) | 978-1-7281-5855-6 |
DOIs | |
Publication status | Published - 2020 |
Event | 32nd Chinese Control and Decision Conference, CCDC 2020 - Hefei, China Duration: 22 Aug 2020 → 24 Aug 2020 |
Conference
Conference | 32nd Chinese Control and Decision Conference, CCDC 2020 |
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Country/Territory | China |
City | Hefei |
Period | 22/08/20 → 24/08/20 |
Bibliographical note
Accepted Author ManuscriptKeywords
- Batch process
- feature transfer
- geodesic flow kernel
- penicillin
- unsupervised