Unsupervised Feature Transfer for Batch Process Based on Geodesic Flow Kernel

Unsupervised Feature Transfer for Batch Process Based on Geodesic Flow Kernel

Zhang, Zheming (Taiyuan University of Technology)
Wang, Fang (Taiyuan University of Technology)
Pang, Y. (TU Delft Transport Engineering and Logistics)
Yan, Gaowei (Taiyuan University of Technology)

2020

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.

Batch process
feature transfer
geodesic flow kernel
penicillin
unsupervised

http://resolver.tudelft.nl/uuid:53a3460e-d4b0-4e30-9240-3f899d9c0bc0

https://doi.org/10.1109/CCDC49329.2020.9164102

IEEE, Piscataway, NJ, USA

978-1-7281-5855-6

Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

32nd Chinese Control and Decision Conference, CCDC 2020, 2020-08-22 → 2020-08-24, Hefei, China

Accepted Author Manuscript

Institutional Repository

conference paper

© 2020 Zheming Zhang, Fang Wang, Y. Pang, Gaowei Yan