Kinetics of fluidized bed spray agglomeration for compact and porous particles

Particle Agglomeration and Properties of Pregelatinized Potato Starch Powder

Pregelatinized starches are used as thickeners in many instant food products. The unique properties of pregelatinized starches, such as their dispersibility in water and high viscosity, are generally desirable for instant food products. However, powdered starches cannot be easily dispersed in cold water due to clumping. The most reliable method to solve this problem is particle size enlargement by an agglomeration technique that causes a structural change in the starch. In this study, pregelatinized potato starch powder (PPSP) was agglomerated in a fluidized bed agglomerator, after which the physical, structural, and rheological properties of the PPSP agglomerated with different maltodextrin (MD) binder concentrations were investigated. The powder solubility and flowability (CI and HR) of all the agglomerated PPSPs were improved, and the particle size (D₅₀) tended to increase as the MD concentration increased, except for the control (0% MD) and the 40% MD. The changes in the particle size of the agglomerated PPSPs were consistent with the SEM image analysis. The magnitudes of the gel strength and viscoelastic moduli (G' and G″) of the agglomerated PPSPs with 10% MD were higher than those of the control due to the more stable structure formed by better intermolecular interaction in the starch and MD during the agglomeration process. Therefore, our results indicated that the fluidized bed agglomeration process and the MD addition as a binder solution greatly influence the physical, structural, and rheological properties of PPSP.

Agglomeration of Spray-Dried Milk Powder in a Spray Fluidized Bed: A Morphological Modeling

The type of solid substrate plays a critical role in determining the kinetics of the spray fluidized bed (SFB) agglomeration process. In the case of porous (also soft) primary particles (PPs), droplet aging is due to imbibition and drying. The surface properties of the substrate also change due to imbibition. The focus of the present work is to simulate the agglomeration of the spray-dried milk powder using the Monte Carlo (MC) method coupled with a drying-imbibition model. In order to extract the morphology of the formed agglomerates, an aggregation model is employed. Further, this aggregation model is employed to predict the number of positions on the PPs (later agglomerates) for droplet deposition; previously, the ‘concept of positions’ was used. The transient growth of different milk powders (whole and skim) is depicted using the enhanced MC model. The enhancement in the droplet deposition model had a prominent influence on the overall kinetics of agglomeration. As expected, this enhanced MC model predicted that the agglomeration rate of skim milk powder is higher than that of whole milk powder.

Mathematical Modeling of Changes in the Dispersed Composition of Solid Phase Particles in Technological Apparatuses of Periodic and Continuous Action

This article presents a methodological approach to modeling the processes of changing the dispersed composition of solid phase particles, such as granulation, crystallization, pyrolysis, and others. Granulation is considered as a complex process consisting of simpler (elementary) processes such as continuous particle growth, agglomeration, crushing and abrasion. All these elementary processes, which are also complex in themselves, usually participate in the formation of the dispersed composition of particles and proceed simultaneously with the predominance of one process or another, depending on the method of its organization and the physicochemical properties of substances. A quantitative description of the evolution of the dispersed composition of the solid phase in technological processes in which the particle size does not remain constant is proposed. Considering the stochastic nature of elementary mass transfer events in individual particles, the methods of the theory of probability are applied. The analysis of the change in the dispersed composition is based on the balanced equation of the particle mass distribution function. The equation accounts for all possible physical mechanisms that effect changes in particle size during chemical and technological processes. Examples of solutions to this equation for specific processes of practical importance are provided. The obtained analytical solutions are of independent interest and are in good agreement with the experimental data, which indicates the adequacy of the proposed approach. These solutions can also be used to analyze similar processes. The effectiveness has been confirmed during the analysis and calculation of the processes of granulation of various solutions and disposal of oil-containing waste to obtain a granular mineral additive.

Changes in Structural and Rheological Properties of Guar Gum Particles in Fluidized-Bed Agglomeration: Effect of Sucrose Binder Concentration

Fluidized-bed agglomeration (FBA) is known to modify the structure and rheology of food powders. In this study, guar gum (GG) powders with various concentrations of sucrose binder (0%, 10%, 20%, or 30%) were subjected to fluidized-bed agglomeration. Subsequently, changes in the characteristics of the GG powders were evaluated by using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), particle size distribution (PSD) analysis, and rheological and dispersibility measurements. SEM images and FTIR spectra revealed surface morphology changes and structural modification, respectively, in the original GG powder after FBA, although the changes observed in FTIR spectra were only slightly dependent on sucrose concentration at low concentrations (0–20%). XRD patterns confirmed that the crystallinity of the GG powder was affected by FBA, but not greatly so by binder concentration. The PSD results showed that the GG particle size was increased by FBA and there was a clear relationship between sucrose concentration (10–30%) and mean particle size. The rheological behavior and dispersibility of GG (properties that are known to be affected by the structure of a powder) were also influenced by sucrose concentration. To sum up, FBA and the concentration of sucrose binder used can serve as factors for modifying GG powder.

A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration

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.

Mechanistic modelling of fluid bed granulation, Part I: Agglomeration in pilot scale process

The present study aims to develop a mechanistic model to predict the performance of a fluid bed granulation process. Therefore, the behavior of the bed was investigated experimentally for various operating conditions. It was observed that the granule Loss on Drying (LoD) and granule size are strongly interrelated. In detail, the maximum final granule size was observed at an intermediate final LoD. Consequently, there is an optimum spray rate and inlet temperature with respect to the granule size. Besides, it was demonstrated that the experiments delivering lower LoD result in a more elongated final granule. Aimed at enabling the prediction of the bed performance numerically, a single-compartment, population-balance-based model was developed and validated against experimental data. The model parameters associated with the growth rate of granule were estimated and mechanistically correlated to the relevant operating conditions. Detailed analysis of the experimental results suggested that these model parameters may be partially connected to the granule LoD. Subsequently, in order to examine the accuracy of the developed model, a simulation was performed for a new set of operating conditions not previously accounted for in the correlations. The comparison of the simulated bed performance, when compared to the experimental results, proved with reasonable accuracy the reliability of the developed model in predicting the temporal evolution of granule size. Therefore, this study can be a step forward in developing a stand-alone granulation model, via modeling heat and mass transfer, to simulate evaporation and drying in a fluid bed granulator.

Mechanistic modelling of fluid bed granulation, Part II: Eased process development via degree of wetness

The performance of a fluid bed granulator was investigated through experimental and numerical study to develop a stand-alone fluid bed granulation model. The single-compartment model proposed in part I (for agglomeration modeling) was extended to account for i) evaporation of freely-flowing droplets, and ii) particle drying. This model enables us to predict the granule liquid content and temperature besides the granule size. Accurately, the equations of heat and mass conservation were solved in parallel to the population balance calculation of the agglomeration. In the same manner as for the agglomeration model, the model parameters associated with the drying model were estimated and correlated to the relevant quantities. The analysis of the experimental results revealed the significant contribution of the system "degree of wetness" to the bed performance, i.e., granule size and loss on drying (LoD). As the agglomeration model parameters were partially correlated to LoD in Part I, the presented model was revisited by inclusion of the degree of wetness. The reliability of the developed model in predicting the temporal evolution of granule size, liquid content, and temperature was proven through comparing the bed performance between simulation and experiment. Subsequently, to lowering the costs associated with experimental run, an approach was proposed based on the degree of wetness, aimed at reducing the number of experiments required for the design of experiment (DoE). The results of our simulation using reduced experiments demonstrated that the degree of wetness can be a promising indicator for the performance of the fluid bed granulator as well as for more efficient design of experiment.

Novel, lean and environment-friendly granulation method: Green fluidized bed granulation (GFBG)

The Green fluidized bed granulation (GFBG) technology is based on the moisture activated dry granulation (MADG) technique and consists only of a mixing and a spraying process using a fluidized bed granulator, requiring no heating process. This provides a less energy-consuming and environment-friendly granulation method compared to current fluidized bed granulation (FBG) and high-shear granulation (HSG) methods. The aim of this study is to compare and evaluate the manufacturability, and granule and tablet properties among GFBG, MADG, FBG and HSG. The GFBG process time took less than 20 min for producing final blends at a 700 g scale, which was comparable to MADG. This process time was significantly shorter than that of FBG and HSG. GFBG not only had the shortest process time but also reduced the number of manufacturing machines compared to FBG and HSG. The Hausner ratio (HR) of granules from GFBG (1.30) indicated a good flowability, and no problems were observed in the tablet mass variability during compression. Tablets produced using GFBG achieved sufficient tensile strength (>1.5 MPa) even at a low compression force and demonstrated the fastest disintegration time compared to the other manufacturing methods. Tablet disintegration is related to wettability and porosity, therefore the tablet wettability (initial and capillary wetting) and tablet porosity were investigated. As a result, the capillary wetting of the tablets produced using GFBG was 3.6 times higher than the tablets produced using FBG, which might have affected the fast disintegration of the tablets produced using GFBG.

Parameter Identification For Continuous Fluidized Bed Spray Agglomeration

Agglomeration represents an important particle formation process used in many industries. One particularly attractive process setup is continuous fluidized bed spray agglomeration, which features good mixing as well as high heat and mass transfer on the one hand and constant product throughput with constant quality as well as high flow rates compared to batch mode on the other hand. Particle properties such as agglomerate size or porosity significantly affect overall product properties such as re-hydration behavior and dissolubility. These can be influenced by different operating parameters. In this manuscript, a population balance model for a continuous fluidized bed spray agglomeration is presented and adapted to experimental data. Focus is on the description of the dynamic behavior in continuous operation mode in a certain neighborhood around steady-state. Different kernel candidates are evaluated and it is shown that none of the kernels are able to match the first six minutes with time independent parameters. Afterwards, a good fit can be obtained, where the Brownian and the volume independent kernel models match best with the experimental data. Model fit is improved for identification on a shifted time domain neglecting the initial start-up phase. Here, model identifiability is shown and parameter confidence intervals are computed via parametric bootstrap.