Application of pulsed spray and moisture content control strategies on quality consistency control in fluidized bed granulation: A comparative study

Bed Stability Control in Pulsed Fluidized-Bed Agglomeration of Instant Riceberry Powder Using an Image-Processing Technique

The problematic cohesiveness of food powders can commonly be solved using pulsed fluidized-bed agglomeration. However, progressively larger granules may result in unstable fluidization. The aims of this research study were to investigate fluid bed expansion as affected by particle enlargement and to control its stability using an image-processing technique. Instant riceberry powder (IRP) was agglomerated using varied air pulsation frequencies (1, 2.5, and 4 Hz). Bed expansion captured by image processing revealed that expanded bed height decreased with agglomeration time. The results showed an enlargement of agglomerated IRP, expressed in D10, D50, and D90, with narrower distribution presented by span, and an improvement in bulk and reconstitution properties. The reduced Carr index (22-27%) and Hausner ratio (1.28-1.38) presented fair flowability and intermediate cohesiveness, respectively. Additionally, airflow during agglomerate growth was progressively adjusted using the image-processing method to enhance bed hydrodynamic stability, leading to improved process efficiency and product quality. This proposed approach has potential applications in the food powder manufacturing industry, particularly by enhancing the fluidization of cohesive particles with cracks and channels.

Quantitative analysis of moisture content and particle size in a fluidized bed granulation process using near infrared spectroscopy and acoustic emission combined with data fusion strategies

Monitoring granule property is essential for fluidization maintenance and product quality control in fluidized bed granulation (FBG). In this study, two non-invasive techniques, near-infrared (NIR) spectroscopy and acoustic emission (AE), were applied for quantitative analysis of moisture content (MC) and median particle size (D50) in a FBG process, combined with chemometrics and data fusion strategies. Partial least squares (PLS) and support vector machine (SVM) regression models were established based on NIR and AE spectral data. The optimal quantitative models were identified considering the effect of spectra preprocessing and variable selection. In the comparison study, the best separate models for MC and D50 quantification were based on NIR and AE, respectively. The NIR model exhibited the better prediction ability with the determination coefficient of validation set (R2v) of 0.9815, root mean square error of validation set (RMSEv) of 0.2226 %, and residual predictive deviation (RPD) of 7.4674 for MC. Meanwhile, the AE model presented the better prediction performance with R2v of 0.9710, RMSEv of 18.2643 μm, and RPD of 5.9740 for D50. Furthermore, among three data fusion strategies, the high-level fusion model achieved the best overall performance on D50 quantification with R2v of 0.9863, RMSEv of 12.5707 μm, and RPD of 8.6798. The results indicated that both NIR and AE are effective monitoring tools for MC and D50 analysis in fluidized bed granulation process. In addition, a more accurate and reliable analysis of particle size can be achieved by combining NIR and AE technology with high-level data fusion.

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation

In the context of Pharma 4.0, pharmaceutical quality control (PQC) is beset by issues such as uncertainties from ever-changing critical material attributes and strong coupling between variables in the multi-unit pharmaceutical tablet manufacturing process (PTMP), and how to timely adjust the operational variables to deal with such challenges has become a key problem in PQC. In this study, we propose a novel data-knowledge-driven modeling and operational adjustment framework for PTMP by integrating Bayesian network (BN) and case-based reasoning (CBR). At the modeling level, first, a distributed concept is introduced, i.e., the BN model for each subunit of PTMP is established in accordance with the operation process sequence, and the transition variables are given by the BN model established first and retrieved as the new query for the next unit. Once the BN models of all subunits are built, they are integrated into a global BN model. At the operational adjustment level, by taking the expected critical quality attributes (CQAs) and related prior information as evidence, the operational adjustment is achieved through global BN reasoning. Finally, the case study in a sprayed fluidized-bed granulation-based PTMP demonstrates the feasibility and effectiveness in improving the terminal CQAs of the proposed method, which is also compared with other methods to showcase its efficacy and merits.