Evaluation of in-line spatial filter velocimetry as PAT monitoring tool for particle growth during fluid bed granulation

Abstract

In this study, the feasibility of spatial filter velocimetry (SFV) as Process Analytical Technology tool for the in-line monitoring of the particle size distribution during top spray fluidized bed granulation was examined. The influence of several process (inlet air temperature during spraying and drying) and formulation variables (HPMC and Tween 20 concentration) upon the particle size distribution during processing, and the end product particle size distribution, tapped density and Hausner ratio was examined using a design of experiments (DOE) (2-level full factorial design, 19 experiments). The trend in end granule particle size distributions of all DOE batches measured with in-line SFV was similar to the offline laser diffraction (LD) data. Analysis of the DOE results showed that mainly the HPMC concentration and slightly the inlet air temperature during drying had a positive effect upon the average end granule size. The in-line SFV particle size data, obtained every 10 s during processing, further allowed to explain and better understand the (in)significance of the studied DOE variables, which was not possible based on the LD data as this technique only supplied end granule size information. The variation in tapped density and Hausner ratio among the end granules of the different DOE batches could be explained by their difference in average end granule size. Univariate, multivariate PLS and multiway N-PLS models were built to relate these end granule properties to the in-line measured particle size distribution. The multivariate PLS tapped density model and the multiway N-PLS Hausner ratio model showed the highest R 2 values in combination with the lowest RMSEE values (R 2 of 82% with an RMSEE of 0.0279 for tapped density and an R 2 of 52% with an RMSEE of 0.0268 for Hausner ratio, respectively).

Development of a fluid bed granulation process control strategy based on real-time process and product measurements

“…Particles passed through an aperture with 4 mm diameter and an internal (20 L/min) and external (3 L/min) pressurized air connection were used to disperse the particles and prevent fouling of the measurement zone by the moist product. The experimental set-up was optimized in a previous study, enabling representative size measurements without disturbing the process [5]. Measured raw data were collected via an A/D converter.…”

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements

“…After restarting of liquid spraying (time point 165, figure 9c), the product temperature recovered rapidly to the average observation while not all fines could adhere to the larger granules. In a previous study, the influence of process and formulation variables upon the PSD of this formulation was studied in a DoE, which showed that the amount of HPMC had a positive effect upon granule size (Burggraeve et al, 2010). A possible action to prevent batch failure due to the large amount of fines, might for example be to spray more HPMC until the scores correspond to the average value.…”

“…The software (In-line Particle Probe V7.12a) operated in the Windows XP environment. The experimental set-up and software settings were based on the knowledge of a previous study (Burggraeve et al, 2010). During the entire top-spray granulation processes, an average PSD was saved every 10 s and product temperature values were registered every minute.…”

“…The measurement cell of the SFV probe is equipped with sapphire windows that are kept clean via an internal compressed air supply system, preventing window fouling. A previous study has shown that the technique is sensitive to any particle size changes during fluid bed granulation (Burggraeve et al, 2010). Hence, the sensitivity of the technique combined with the short measuring time make SFV an appropriate tool to continuously gather PSD information throughout a granulation process.…”

“…Hence, the change in PSD during granulation gives a direct indication of the batch progress and batch quality. Several techniques have been evaluated to monitor PSD during granulation in real-time: near infrared spectroscopy (Alcala et al, 2010;Findlay et al, 2005;Frake et al, 1997;Goebel and Steffens, 1998;Kaddour and Cuq, 2009;Luukkonen et al, 2008;Tok et al, 2008), focused beam reflectance method (Hu et al, 2008;Huang et al, 2010;Tok et al, 2008), spatial filter velocimetry (SFV) (Burggraeve et al, 2010;Lipsanen et al, 2008;Narvanen et al, 2008Narvanen et al, , 2009), image analysis (Laitinen et al, 2004;Naervanen et al, 2008;Watano and Miyanami, 1995;Watano et al 1996Watano et al , 1997Watano et al , 2000Watano et al , 2001Watano, 2001) and acoustic emission monitoring (AE) (Gamble et al, 2009;Halstensen et al, 2006;Matero et al, 2009;Tok et al, 2008;Whitaker et al, 2000). Each technique has its own advantages and shortcomings.…”