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Frontiers of Chemical Science and Engineering

Front. Chem. Sci. Eng.    2019, Vol. 13 Issue (3) : 599-607     https://doi.org/10.1007/s11705-019-1807-2
RESEARCH ARTICLE
Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm
Yifei Wang1,2, Kai Wang1,2, Zhao Zhou1,2(), Wenli Du1,2()
1. Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education), East China University of Science and Technology, Shanghai 200237, China
2. School of information science and engineering, East China University of Science and Technology, Shanghai 200237, China
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Abstract

Near-infrared spectroscopy mainly reflects the frequency-doubled and total-frequency absorption information of hydrogen-containing groups (O‒H, C‒H, N‒H, S‒H) in organic molecules for near-infrared lights with different wavelengths, so it is applicable to testing of most raw materials and products in the field of petrochemicals. However, the modeling process needs to collect a large number of laboratory analysis data. There are many oil sources in China, and oil properties change frequently. Modeling of each raw material is not only unfeasible but also will affect its engineering application efficiency. In order to achieve rapid modeling of near-infrared spectroscopy and based on historical data of different crude oils under different detection conditions, this paper discusses about the feasibility of the application of transfer learning algorithm and makes it possible that transfer learning can assist in rapid modeling using certain historical data under similar distributions under a small quantity of new data. In consideration of the requirement of transfer learning for certain similarity of different datasets, a transfer learning method based on local similarity feature selection is proposed. The simulation verification of spectral data of 13 crude oils measured by three different probe detection methods is performed. The effectiveness and application scope of the transfer modeling method under different similarity conditions are analyzed.

Keywords near-infrared spectroscopy      transfer learning      similarity      modeling     
Corresponding Authors: Zhao Zhou,Wenli Du   
Just Accepted Date: 18 March 2019   Online First Date: 22 April 2019    Issue Date: 22 August 2019
 Cite this article:   
Yifei Wang,Kai Wang,Zhao Zhou, et al. Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm[J]. Front. Chem. Sci. Eng., 2019, 13(3): 599-607.
 URL:  
http://journal.hep.com.cn/fcse/EN/10.1007/s11705-019-1807-2
http://journal.hep.com.cn/fcse/EN/Y2019/V13/I3/599
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Yifei Wang
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Fig.1  The spectrum data measured by the transmittance probe, reflectance probe and transreflectance probe. (a) Transmittance spectrum, (b) reflectance spectrum, (c) transreflectance spectrum
Dataset cos?θ |ρ XY|
TF-TM 0.4237 0.4503
TF-RF 0.3593 0.3680
Tab.1  Similarity between two data sets
No. cos?θ |ρ XY|
1 0.0895 0.1378
2 ?0.1330 0.4379
3 0.4081 0.2376
4 ?0.0987 0.2717
5 0.3281 0.2366
6 0.0144 0.2303
7 ?0.1757 0.2398
8 0.0170 0.3421
9 0.0382 0.5063
10 0.6067 0.5993
11 0.3612 0.3563
12 0.9569 0.9838
13 0.5610 0.7221
Tab.2  Local similarity of transreflectance and transmittance probe data set
No. cos?θ |ρ XY|
1 0.1278 0.1466
2 0.1325 0.2183
3 ?0.0627 0.2014
4 ?0.1562 0.1974
5 ?0.3917 0.2048
6 0.0132 0.2562
7 ?0.5256 0.2534
8 0.2285 0.2522
9 0.0404 0.4213
10 0.8141 0.7394
11 0.1341 0.2823
12 0.7107 0.7553
13 0.4245 0.5454
Tab.3  Local similarity of transreflectance and reflectance probe data set
Fig.2  Local wavenumber extraction. (a) Transmittance spectrum, (b) Transreflectance spectrum
Fig.3  A flowchart of STBB
Dataset BP1 BP2 TrA TCA PCA
TF-TM 2.8975 5.7363 3.5563 4.7850 12.994
TF-RF 3.1620 4.6833 3.7548 3.6327 7.0132
Tab.4  MAPE of BP1, BP2, TrAdaBoost, TCA and PCA/%
Fig.4  Error rate curves on transreflectance and transmittance probe data set for BP1, BP2, TrAdaBoost, TCA and PCA/%
Fig.5  Error rate curves on transreflectance and reflectance probe data set for BP1, BP2, TrAdaBoost, TCA and PCA/%
Dataset Selection range of feature data cos?θ |ρ XY|
TF-TM 410?489 0.7027 0.7337
TF-RF 321?400 0.7100 0.7479
Tab.5  Similarity of part with high similarity between source data set and target data set
Fig.6  The extracted part of the transreflectance and transmittance probe data. (a) Transreflectance spectrum, (b) Transmittance spectrum
Fig.7  The extracted part of the transreflectance probe and reflectance probe data. (a) Transreflectance spectrum, (b) Reflectance spectrum
Fig.8  Error rate curves on transreflectance and transmittance probe data set for BP1, S-TrAdaBoost, S-TCA and STBB/%
Fig.9  Error rate curves on transreflectance and reflectance probe data set for BP1, S-TrAdaBoost, S-TCA and STBB/%
Dataset BP1 S-TrA S-TCA STBB
TF-TM 2.8975 1.8949 1.5372 1.2542
TF-RF 3.1620 3.3346 2.6425 2.0836
Tab.6  MAPE of BP1, S-TrAdaBoost, S-TCA and STBB/%
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