Variational PLS-Based Calibration Model Building With Semi-Supervised Learning for Moisture Measurement During Fluidized Bed Drying by NIR Spectroscopy

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Abstract
To measure the moisture content of granules during the industrial fluidized bed drying (FBD) process, a semi-supervised calibration model is proposed for using the near-infrared (NIR) spectroscopy to conduct in situ measurement. To solve the dilemma of lacking sufficiently labeled samples as often encountered in various batch FBD processes, a semi-supervised variational inference partial least-squares (PLS) method is proposed to use up all the labeled and unlabeled spectra measured for calibration model building. Moreover, an adaptive Gamma distribution-based sparsing algorithm is established to select the spectral variables for modeling, to overcome the high-dimensional input collinearity. Owing to the use of a variational inference learning approach, the constructed model can ensure not only prediction accuracy but also credibility. A numerical example and experiments on batch FBD processes of silica gel granules are shown to demonstrate the effectiveness and merit of the proposed method.
Funding Information
  • NSF China (62173058, 62003197)
  • Talent Project of Revitalizing Liaoning (XLYC1902030)
  • Ministry of Science and Technology, Taiwan (MOST 109-2221-E-033-013-MY3)

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