Real-time monitoring and fault detection of pulsed-spray fluid-bed granulation using near-infrared spectroscopy and multivariate process trajectories

Abstract

Pulsed spray is a useful tool for granule size control in fluid bed granulation. To improve the quality control of pulsed-spray fluid bed granulation, a combination of in-line near-infrared (NIR) spectroscopy and principal component analysis was used to develop multivariate statistical process control (MSPC) charts. Different types of MSPC charts were developed, including principal component score charts, Hotelling's T2 control charts, and distance to model X control charts, to monitor the batch evolution throughout the granulation process. Correlation optimized warping was used as an alignment method to deal with the time variation in batches caused by the granulation mechanism in MSPC modeling. The control charts developed in this study were validated on normal batches and tested on four batches that deviated from normal processing conditions to achieve real-time fault analysis. The results indicated that the NIR spectroscopy-based MSPC model included the variability in the sample set constituting the model and could withstand external variability. This research demonstrated the application of synchronized NIR spectra in conjunction with multivariate batch modeling as an attractive tool for process monitoring and a fault diagnosis method for effective process control in pulsed-spray fluid bed granulation.