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
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 11, 3608 (2022)
Fig. 1. Modeling flow diagram of transfer learning algorithm
Fig. 2. Process flow diagrams and Reactive principle sketch
(a): Process flow diagram; (b): Reactive principle sketch
(a): Process flow diagram; (b): Reactive principle sketch
Fig. 3. Spectral comparison at different detecting points
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
(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
(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
(a): Model curve; (b): Scatter plot of prediceted and actual values
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Table 1. 2,6-DMP purity distribution at different detecting points
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