Predicting Particle Size During Fluid Bed Granulation Using Process Measurement Data

Abstract

Abstract. In this study, a new concept for particle size prediction during the fluid bed granulation is presented. Using the process measurements data obtained from a design of experimental study, predictive partial least squares models were developed for spraying and drying phases. Measured and calculated process parameters from an instrumented fluid bed granulation environment were used as explaining factors, whereas an in-line particle size data determined by spatial filtering technique were used as response. Modeling was carried out by testing all possible combinations of two to six process parameters (factors) of the total of 41 parameters. Eleven batches were used for model development and four batches for model testing. The selected models predicted particle size (d 50 ) well, especially during the spraying phase (Q 2 =0.86). While the measured in-line d 50 data were markedly influenced by different process failures, e.g., impaired fluidization activity, the predicted data remained more consistent. This introduced concept can be applied in fluid bed granulation processes if the granulation environment is soundly instrumented and if reliable real-time particle size data from the design of experiment batches are retrieved for the model development.

Review of real-time release testing of pharmaceutical tablets: State-of-the art, challenges and future perspective

“…The granule size has been measured in-line during or after granulation using a range of different techniques including spatial filter velocimetry (SFV) (Fonteyne et al, 2012a), focused beam reflectance measurements (FBRM) (Kumar et al, 2013), microwave resonance technology (MRT) (Lourenço et al, 2011), acoustic emission (AE) (Whitaker et al, 2000), photometric imaging technique (PIT) (Fonteyne et al, 2012a) and NIR spectroscopy (Khorasani et al, 2016). A different approach was demonstrated by Närvänen et al (2009) for fluid-bed granulation, who developed a soft sensor that was capable of predicting the granule size from measured and derived process data. In this approach, they used 41 parameters in total ranging from the temperature of the process room and granulation chamber to the flow rate of inlet air and cumulative enthalpy of the water vapour in outlet air.…”

Prediction Performance and Economic Efficiency of Soft Sensors for in-Line Water Content Monitoring in Fluidized Bed Granulation: PP-Based Model <i>vs.</i> NIRS-Based Model

“…Statistical models to predict granule properties, such as water content and particle size, have been constructed using linear regression methods, such as multiple linear regression 6,7) and partial least squares regression (PLSR). [8][9][10][11] Recently, nonlinear regression methods, such as locally weighted partial least squares regression (LW-PLSR), 12) have been applied in various industrial processes to increase prediction accuracy. [12][13][14][15][16][17] However, they have been rarely utilized in fluidized bed granulation processes.…”

A two-step approach for fluidized bed granulation in pharmaceutical processing: Assessing different models for design and control

“…The common mechanisms for the aggregation of powders were wetting and nucleation, consolidation and growth, and breakage and attrition [ 9 ]. Moreover, the development of in-line process analytical technology (PAT) has made it possible to understand the process and further elucidate the potential mechanisms in fluidized bed granulation using near-infrared (NIR) spectroscopy [ 10 ], spatial filtering technology (SFT) [ 11 ], photometric imaging [ 12 ], and microwave resonance technology (MRT) [ 13 ]. However, as these studies were conducted with different types of formulations and granulation units, the operational parameters of fluidized bed granulation have not received much recognition.…”