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

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.

Automated summary

A semi-supervised calibration model is proposed to measure the moisture content of granules during the industrial fluidized bed drying process using near-infrared spectroscopy. The model utilizes both labeled and unlabeled spectra to overcome the lack of labeled samples in batch FBD processes. An adaptive Gamma distribution-based sparsing algorithm is used to select spectral variables for modeling and overcome high-dimensional input collinearity.