scholarly journals Influence of drying conditions on process properties and parameter identification for continuous fluidized bed spray agglomeration

Author(s):  
Gerd Strenzke ◽  
Ievgen Golovin ◽  
Maximilian Wegner ◽  
Stefan Palis ◽  
Andreas Bück ◽  
...  

Agglomeration is a particle formulation process in which at least two primary particles are combined to form a new one. The growth of agglomerates depends on interactions of particles covered with wet spots that generated by depositions of binder droplets. This work experimentally compares the influence of external feed rate and sprayed binder content on product properties and process stability with internal separation at different drying conditions. Due to the identification of parameters a populations balance model (PBM) is developed. The PBM includes the agglomeration kernel function, which characterizes the kinetics, i.e. the rate at which primary particles build agglomerates. Keywords: spray fluidized bed agglomeration; drying; continuous process; internal separation; population balances

Author(s):  
Juliana G Rosa ◽  
R. F. Nascimento ◽  
K. Andreola ◽  
O. P. Taranto

Acacia gum is an important food emulsifier that presents poor instant properties which can be improved by fluidized bed agglomeration. This study investigated the influence of drying conditions on particle growth kinetics using an in-line particle size monitoring by spatial filter velocimetry. The drying conditions varied according to the binder flow rate and the fluidizing air temperature. The particle growth kinectis showed drying conditions dependence. At mild drying conditions the growth rate and the process yield were higher. The in-line particle size monitoring was useful to observe the influence of the drying conditions on the growth kinetics.Keywords: fluidized bed agglomeration; acacia gum; particle growth kinectis; in-line monitoring.  


Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7221
Author(s):  
Abhinandan Kumar Singh ◽  
Evangelos Tsotsas

Agglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morphology plays an imperative role in understanding product behavior. In the present work, the sequential tunable algorithm developed in previous studies to generate monodisperse SFB agglomerates is improved and extended to polydisperse primary particles. The improved algorithm can completely retain the given input fractal properties (fractal dimension and prefactor) for polydisperse agglomerates (with normally distributed radii of primary particles having a standard deviation of 10% from the mean value). Other morphological properties strongly agreed with the experimental SFB agglomerates. Furthermore, this tunable aggregation model is integrated into the Monte Carlo (MC) simulation. The kinetics of the overall agglomeration at various operating conditions, like binder concentration and inlet fluidized gas temperature, are investigated. The present model accurately predicts the morphological descriptors of SFB agglomerates and the overall kinetics under various operating parameters.


Author(s):  
J. Du ◽  
A. Bück ◽  
E. Tsotsas

Spray fluidized bed agglomeration is an important process in particle formation and is widely used in the chemical, pharmaceutical and food industry. In this study a continuously operated horizontal fluidized bed is employed to obtain a continuous agglomeration process. It is conducted with glass beads (dst=200 µm) and water-based binder hydroxy-propyl-methyl-cellulose (HPMC) sprayed by three top nozzles. The steady state is reached and samples are taken periodically and analyzed. The influence of fluidization air temperature and configurations of internal weirs are studied. Keywords: horizontal fluidized bed, spray agglomeration, continuous process


Author(s):  
İlyas Atalar ◽  
Abdullah Kurt ◽  
Furkan Türker Saricaoğlu ◽  
Osman Gül ◽  
Hüseyin Gençcelep

2020 ◽  
Vol 6 ◽  
pp. 275-285
Author(s):  
Jurarat Nisamaneenate ◽  
Duangduen Atong ◽  
Anun Seemen ◽  
Viboon Sricharoenchaikul

2021 ◽  
Vol 02 ◽  
Author(s):  
Thanh V. Ngo ◽  
Christopher J. Scarlett ◽  
Michael C. Bowyer ◽  
Rebecca McCullum ◽  
Quan V. Vuong

Background: S. chinensis extract contains bioactive compounds, which exhibit high antioxidant activities. However, for commercial uses, it is necessary to encapsulate the extract to protect it from degradation. Objective: This study aimed to optimise spray-drying conditions and then compare with freeze-drying to identify the most suitable conditions for encapsulation of Salacia chinensis L. root extract. Method: Three factors of spray-drying encapsulation, including maltodextrin concentration, inlet temperature and feed rate, have been tested for the impacts on the physical and phytochemical properties of S. chinensis root extract. Based on the optimal conditions, the spray-drying was then compared with freeze-drying. Results: The results showed that maltodextrin concentration, inlet temperature and feed rate had significant impacts on recovery yield, phenolics, mangiferin and antioxidant activity of the spray-dried extract. The optimal spray-drying encapsulation conditions were maltodextrin concentration of 20 %, inlet temperature of 130ºC and feed rate of 9 mL/min. Under these optimal conditions, the encapsulated extract had comparable solubility, total phenolics, mangiferin, and antioxidant activity, lower bulk density, moisture content, and water activity as compared to encapsulated extract made using the freeze-drying technique. These optimal spray-drying conditions are recommended to encapsulate the extract of S. chinensis root. Conclusion: Spray-drying was found to be more effective for encapsulation of S. chinensis root extract than freeze-drying. Therefore, spray-drying is recommended for further applications.


2004 ◽  
Vol 30 (4) ◽  
pp. 447-454
Author(s):  
Takahiro MARUMOTO ◽  
Naoki FUJIWARA ◽  
Noriyuki OHYATSU ◽  
Tetsuya IWASE

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