Spectroscopy and Spectral Analysis, Volume. 42, Issue 11, 3608(2022)
Transfer Learning Modeling of 2,6-Dimethylphenol Purity Based on PLS Subspace Alignment
Fig. 2. Process flow diagrams and Reactive principle sketch
(a): Process flow diagram; (b): Reactive principle sketch
Fig. 4. Different principal component numbers impact on model performance
Fig. 5. Different sample numbers of dephenolization tower impact on model performance
(a): Impact on model performance for transferring different sample numbers of dephenolization tower;(b): Model performance improvement percentage for transferring different sample numbers of dephenolization tower
Fig. 6. Different sample numbers of o-cresol tower impact on model performance
(a): Impact on model performance for transferring different sample numbers of o-cresol tower;(b): Model performance improvement percentage for transferring different sample numbers of o-cresol tower
Fig. 7. Model curve and Scatter plot
(a): Model curve; (b): Scatter plot of prediceted and actual values
|
Get Citation
Copy Citation Text
Yun-fei WU, Xiao-li LUAN, Fei LIU. Transfer Learning Modeling of 2,6-Dimethylphenol Purity Based on PLS Subspace Alignment[J]. Spectroscopy and Spectral Analysis, 2022, 42(11): 3608
Category: Research Articles
Received: Oct. 9, 2021
Accepted: --
Published Online: Nov. 23, 2022
The Author Email: WU Yun-fei (yfwuu@vip.jiangnan.edu.cn)