Gaining Fluid Bed Process Understanding by In-Line Particle Size Analysis

Närvänen, Tero; Lipsanen, Tanja; Antikainen, Osmo; Räikkönen, Heikki; Heinämäki, Jyrki; Yliruusi, Jouko

doi:10.1002/jps.21486

Cited by 42 publications

(26 citation statements)

References 25 publications

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“…Because one measurement represents quite a small sample from the total mass, it can still have some random noise, and therefore, a moving average of six consecutive measurements was used. It was found in previous studies that the in-line application systematically underestimates the particle size (40). Due to this, the d 50 data were finally corrected using the Eq.…”

Section: Data Pretreatment and Modelingmentioning

confidence: 99%

“…One disadvantage of these off-line applications is, however, that no data is obtained from the granule growth/attrition during the process, and therefore, real-time process adjustment is not possible. Due to this, more and more effort has been put into techniques that can be applied as in-line, online, or at-line (33)(34)(35)(36)(37)(38)(39)(40).…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this study was to introduce a new concept for real-time particle size prediction in FBG. The previous study reports focused on process understanding and determination of particle size during the FBG (32,40). The process measurement and in-line particle size data gathered in those studies were used here to evaluate the possibility of the real-time particle size prediction using the measured process data.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Particle Size During Fluid Bed Granulation Using Process Measurement Data

2009

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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.

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Section: Data Pretreatment and Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Particle Size During Fluid Bed Granulation Using Process Measurement Data

2009

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Burggraeve

Kerkhof²,

Hellings³

et al. 2011

European Journal of Pharmaceutical Sciences

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Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively flowabililty as Y-matrix, were developed. The scores of the 4 test batches were used to examine the predictive ability of the model.

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“…Until recently, these measurements have mainly been utilized for monitoring purposes and not for finding critical interactions or variation sources of the processes. In addition to various physical measurements, granule size can be measured during the process on-line and in-line from the process stream (10,11). There is clearly a need for methods to detect the changing granulation conditions in fluid bed chambers and thereby identify potential failure modes in a timely manner (12).…”

Section: Introductionmentioning

confidence: 99%

Particle Size, Moisture, and Fluidization Variations Described by Indirect In-line Physical Measurements of Fluid Bed Granulation

et al. 2008

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Abstract. The aim of this study was to evaluate an instrumentation system for a bench scale fluid bed granulator to determine the parameters expressing the changing conditions during the spraying phase of a fluid bed process. The study focused mainly on four in-line measurements (dependent variables): fluidization parameter (calculated by inlet air flow rate and rotor speed), pressure difference over the upper filters, pressure difference over the granules (lower filter), and temperature of the fluidizing mass. In-line particle size measured by the spatial filtering technique was an essential predictor variable. Other physical process measurements of the automated granulation system, 25 direct and 12 derived parameters, were also utilized for multivariate modeling. The correlation and partial least squares analyses revealed significant relationships between various process parameters highlighting the particle size, moisture, and fluidization effect. Fluidization parameter and pressure difference over upper filters were found to correlate with in-line particle size and therefore could be used as estimates of particle size during granulation. The pressure difference over the granules and the temperature of the fluidizing mass expressed the moisture conditions of wet granulation. The instrumentation system evaluated here is an invaluable aid to gaining more control for fluid bed processing to obtain repeatable granules for further processing.

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Gaining Fluid Bed Process Understanding by In-Line Particle Size Analysis

Cited by 42 publications

References 25 publications

Predicting Particle Size During Fluid Bed Granulation Using Process Measurement Data

Predicting Particle Size During Fluid Bed Granulation Using Process Measurement Data

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

Particle Size, Moisture, and Fluidization Variations Described by Indirect In-line Physical Measurements of Fluid Bed Granulation

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