Spray Fluidized Bed Agglomeration Research Articles

Population Balance Modeling of Particle Size and Porosity in Fluidized Bed Spray Agglomeration

Population Balance Modeling of Particle Size and Porosity in Fluidized Bed Spray Agglomeration

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.

Morphological descriptors of agglomerates produced in continuously operated spray fluidized beds

This work presents the results of a systematic investigation of structural and morphological descriptors of agglomerates produced in continuously operated fluidized bed spray agglomeration. Experiments with glass beads as primary particles and a water-based HPMC mixture as top sprayed binder solution were carried out within a cylindrical fluidized bed of pilot plant scale. Characterization of all agglomerates was performed by X-ray micro-computed tomography. Investigated descriptors include number of primary particles, radius of gyration, coordination number and angle distribution as well as porosity and fractal properties. Utilizing these descriptors, a comparison regarding morphology of agglomerates produced in batch and continuous operation under similar process conditions and process times is drawn.

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.

Monte Carlo modeling of spray agglomeration in a cylindrical fluidized bed: From batch-wise to continuous processes

Monte Carlo method is a stochastic approach that can simulate different micro-mechanisms occurring in spray fluidized bed (SFB) agglomeration. As no model covered the simulation of continuous SFB agglomeration process existing before, an event-driven constant volume Monte Carlo model is proposed. By adding particle feed and discharge events that take place periodically, the Monte Carlo model is extended to simulate continuous SFB agglomeration process. In these events, new particles are fed, and individuals are randomly selected and removed according to the feed rate and the change in bed mass. The experimental results show that the decrease of feed rate or bed mass increases the particle size. The comparison of experimental and simulation results shows that the continuous Monte Carlo model can predict the evolution of the particle size distribution with collision frequency prefactor 0.05 and breakage probability 0.17%. The development of bed mass in experiments can be reproduced in the simulations.

Influence of polydispersity and breakage on stochastic simulations of spray fluidized bed agglomeration

Monte Carlo (MC) model is a stochastic and discrete method to enhance the understating of spray fluidized bed (SFB) agglomeration in the field of food, chemical and pharmaceutical industry. Previous studies focused on agglomerates composed of monodisperse primary particles. Size dispersity of primary particles is, though, an important aspect to fully assess the morphological features of an agglomerate. In the current work, a polydisperse tunable aggregation model is therefore developed. The radius of gyration, porosity, surface area and agglomeration rate increase with primary particle dispersity. Furthermore, the breakage of already formed agglomerates is introduced in the MC model. The influence of breakage decreases the overall growth rate and the arithmetic mean diameter of the particle size distribution by around 11%. The overall growth rate and the diameter of agglomerates with a similar number of primary particles increase with decreasing temperature of fluidization gas and increasing mass fraction of binder.

Kernel identification in continuous fluidized bed spray agglomeration from steady state data

Fluidized bed spray agglomeration is an important industrial particle formation process. In particular, the continuous operation mode is able to provide a constant stream of product particles with constant quality in terms of particle properties. Mathematical process modeling represents a valuable tool for a thorough analysis of the involved mechanistic processes and can further be used for process intensification and control. Sophisticated models describing the quantitative effect of process conditions on particle properties are particularly important. Therefore, in this contribution the influence of the seven most important operational parameters on the particle size distribution is modeled, including fluidization and binder properties. To this end, a population balance process model with the three-parametric Kapur kernel is fitted to experimental data. The first main result of this contribution is the quantitative description of the dependency between the agglomeration rate and the process conditions by multidimensional paraboloids. The second main result is the introduction of a general method by which this quantitative formulation is obtained.

Population balance modeling of volume and time dependent spray fluidized bed aggregation kernel using Monte Carlo simulation results

This paper seeks to extend the work of Hussain et al. (A new framework for population balance modeling of spray fluidized bed agglomeration, Particuology 19 (2015) 141–154) to develop a detailed one-dimensional population balance modeling (PBM) of the spatially homogeneous spray fluidized bed aggregation (SFBA) process. A new mathematical model of the volume and time dependent aggregation kernel is developed based on process specific microscopic mechanisms. In addition, the population balance equations of other important process related parameters (total number of available droplets and size distribution of wet particles) are presented. The developed PBM contains a new mathematical model which estimates the death rate of binder droplets due to the drying mechanism. For the verification of the developed PBM, a constant number Monte Carlo (MC) algorithm is used, which simulates important micro-mechanisms of the SFBA process (droplet addition, droplet drying, volume dependent particle collisions, aggregation, and rebound). The MC algorithm is capable of analyzing the effects of each microscopic event on the aggregation behavior. Volume dependency in particle collisions is used, while mimicking the SFBA process in the MC simulation algorithm. Furthermore, the volume and time dependent probability of successful wet position collisions is successfully extracted using the MC simulations and then transferred to the development of the PBM. Finally, the accuracy of the proposed PBM is verified by comparing its results against the predictions of the MC simulations.

Influence of operating parameters on process behavior and product quality in continuous spray fluidized bed agglomeration

The present work investigates continuous spray fluidized bed agglomeration with internal separation using glass primary particles and sprayed solution of cellulosic binder. Different fluidization and thermal parameters were investigated with respect to their influences on process behavior and product quality. Product quality is characterized with respect to particle size, sphericity and porosity. Trends between operation parameters and product characteristics are established, especially in view of the drying potential of the fluidization gas. In addition to the successful identification of the parameter influences, this study has detected general relationships between drying potential and agglomerate porosity, as well as between agglomerate size and porosity.

A tunable aggregation model incorporated in Monte Carlo simulations of spray fluidized bed agglomeration

Constant volume Monte Carlo (CVMC) model is a discrete and stochastic approach to enhance the understanding of spray fluidized bed (SFB) agglomeration. Previous CVMC model used a simplified approach, considering the formed agglomerates as characteristic spheres with porosity as a function of their fractal properties. This means that other morphological descriptors such as coordination number could not be assessed. In the present work, a tunable aggregation model is developed to reconstruct the SFB agglomerates consisting of monodisperse spherical primary particles by a particle-cluster algorithm using a novel approach of tuning the fractal dimension at a given prefactor (= 1). Various morphological descriptors obtained from this aggregation model are validated using experimental data. Furthermore, this aggregation model is incorporated into the CVMC framework to evaluate both, the morphological descriptors and formation kinetics of SFB agglomerates produced under different process conditions (inlet fluidized gas temperature and binder content).

Influence of process variables on spray agglomeration process in a continuously operated horizontal fluidized bed

Experiments on continuously operated spray fluidized bed agglomeration are presented in this work. The process is conducted with glass beads and water-based binder hydroxypropylmethylcellulose (HPMC). By varying the process parameters, the results show that by the decrease of air temperature or feed rate and by the increase of binder concentration, larger particles can be achieved. Larger particle size also results in higher bed mass and more fluctuations in the process. Besides parameters variations, different internal and outlet weir configurations are used. Tracers are used in the experiments to characterize the particle residence time distribution. The separation effect can be decreased and the particle residence time can be optimized by the installation of internal weirs and lower height of outlet weir. The results show that the jets from the spray nozzles are increasing the motion and mixing of particles significantly.

Stochastic model to simulate spray fluidized bed agglomeration: a morphological approach

Constant volume Monte Carlo (CVMC) method is a stochastic method used to describe the agglomerate growth during spray fluidized bed agglomeration (SFBA). The methodology overcomes the difficulties of expressing and solving multivariate population balance equations (PBE). It enables to account for micro-scale events and processes involved in the agglomeration process, especially for binder addition and drying. Previous CVMC models assume that all agglomerates have the same porosity. This simplistic assumption is replaced in the present paper by given fractal dimension, which means that porosity can change with agglomerate size and process conditions. Evaluation of measured morphological descriptors and the agglomeration rates shows that this leads to more realistic results.

Lyapunov-based online parameter estimation in continuous fluidized bed spray agglomeration processes

Abstract Fluidized bed spray agglomeration is a particle formation process in many industrial applications, e.g. pharmaceutical and food processing. The properties of the formed agglomerates, like characteristic volume, significantly affect the product quality and can be affected by variation of certain operating parameters. Mathematical modeling not only provides an abstract characterization of the effects of those on the product properties but also supports thorough understanding of the underlying physical and chemical mechanisms. Moreover, it enables application of advanced process analysis, control and intensification schemes. As characteristic properties underlie variations within the ensemble of agglomerates the process can be described as a distributed parameter system, where the resulting model equations are partial differential equations. Adaption to experimental data requires the solution of inverse problems, which tend to be ill-conditioned. As an alternative approach, in this contribution an adaptive identification procedure is presented. Therefore, a modified plant model is run in parallel to the process and adaption rates are chosen based on a Lyapunov-function. The approach is validated in a parametric study for two scenarios: In the first, it is assumed that the structure of the dynamics is fully known, while in the second, this assumption does not hold. It is shown that the proposed approach allows to reconstruct unknown kinetic information of the process dynamics.

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.

Monte Carlo modeling of binder‐Less spray agglomeration in fluidized beds

Fluidized bed spray agglomeration is used in the industry to increase the particle size and to improve several properties, for example, bulk density, flowability, and dissolution behavior of particulate products. Usually, a binder liquid is sprayed on a particle bed. If amorphous materials are used, spraying of pure water may cause agglomeration due to glass transition at wet spots on the particle surface. As no process models covering binder‐less spray agglomeration currently exist, a model based on a Monte Carlo method is presented. In this method, the process is described by events and processes on the single particle scale. Additionally, agglomeration experiments in a lab‐scale fluidized bed using three different maltodextrins are presented. For each experiment, a simulation was performed. The simulation results are compared with the obtained experimental data. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3582–3594, 2018

Influence of top spray fluidized bed agglomeration conditions on the reconstitution property and structure modification of skim yoghurt powder

Spray dried yoghurt powder (Control) has small particle size (57 µm) that cause high cohesiveness forces between particles. Wetting time of control yoghurt powder takes more than 1 hr by forming lump on the surface of water. Smaller particle size cause higher contact surface area per unit mass of powder that limits flowability of yoghurt powder. Yoghurt powder turned into instant form due to particle size enlargement by applying agglomeration process. In addition, agglomerates showed lower bulk, tapped, and particle densities and excellent flowability. Effects of inlet air temperature (50–80 °C), atomizing pressure (0.5–1.5 bars) and sprayed water amounts (50–80 g) were investigated in this study. Response surface methodology was used to find the best conditions in view of high quality reconstitution properties of yoghurt powder. The best process conditions were determined as 62.5 °C inlet temperature, 0.9 bar atomizing pressure and 68 g sprayed water amount. Practical applications Optimization of top sprayed fluid bed agglomeration process parameters has not yet been studied for dairy powders. By using optimization process, effects of agglomeration process parameters (inlet air temperature, amount of binder, and atomization pressure) on quality parameters of yoghurt powder were revealed. Improvement of yoghurt powder reconstitution properties will be the first study in the literature. These studies provide an insight for further studies about top spraying bed agglomeration process.

Interaction of droplets with porous structures: Pore network simulation of wetting and drying

ABSTRACTIn fluidized bed spray agglomeration, the time evolution of a liquid droplet deposited on a porous particle is of paramount importance for the success of the process. The combination of droplet penetration into the pores and evaporation, either directly from the droplet surface or from the surrounding wet pores, determines how long free liquid remains on the particle surface so that other particles can bind via liquid bridges. In this work, a two-dimensional pore network model that combines the algorithms of liquid migration and drying is developed to track the full droplet evolution, from its deposition on network surface to complete evaporation of the liquid. The influence of the pore structure for mono-modal and bi-modal networks with different spatial correlation of the pore size on the evolution of the liquid droplet is investigated. The effect of the liquid viscosity on the evolution of the droplet in the pore network is studied. Moreover, pore network simulations with multiple depositions of liquid droplets on the same network are presented as a rough approximation of spray agglomeration process.

Micro-Macro Transition of Population Balances in Fluidized Bed Granulation

The objective of this study is to introduce a generalized population balance model for simulating spray fluidized bed agglomeration of non-porous particles by extending the recent works of Hussain et al. [9,10]. In this context, the focus of this work is to model the average wet surface coverage fraction per wet particle and average success fraction concerning the dissipation of kinetic energy on a macroscopic scale. Average wet surface coverage fraction per wet particle is modeled by assuming the steric hindrance to be constant and discretizing the surface area of primary particles into smaller areas. Moreover, an algorithm is presented which takes into account Stokes criterion to predict an average success fraction concerning the dissipation of kinetic energy on a macroscopic scale.The proposed model with these variables is capable of predicting the particle size distribution, the total number of agglomerates, the total number of droplets and the total number of wet particles present in the system. The population balance model is validated by comparing its results with the experimental data. All comparisons show, at least qualitatively, good agreement, and the ability and generality of the proposed model to predict system dynamics with adequate accuracy.

A new framework for population balance modeling of spray fluidized bed agglomeration

Previous work (Hussain et al. (2013). Chemical Engineering Science, 101, 35) has pointed out that the conventional, one-dimensional population balance equation for aggregation can be expanded to accurately reproduce the results of discrete simulations of spray fluidized bed agglomeration. However, some parameters had to be imported from the discrete simulation (Monte-Carlo). The present paper shows how the expanded population balance can be run without importing parameters from the Monte-Carlo simulation. The expanded population balance still reproduces the results of Monte-Carlo simulations accurately, taking into account key micro-scale phenomena (sessile droplet drying, efficiency of collisions), but with much lower computational cost. Required input parameters are just the drying time of sessile droplets (calculated in advance), and the pre-factor of an equation that correlates particle collision frequency with fluidized bed expansion. In this way, the expanded population balance is, apart from autonomous, also (nearly) predictive. Its performance is demonstrated by comparisons with both Monte-Carlo results and experimental data for various operating conditions (binder mass flow rate, gas temperature). Despite formally being a one-dimensional expression, the expanded population balance captures additional properties, such as the number of wet particles and the number of droplets in the system, which are even difficult to measure in experiments.

Modeling aggregation kinetics of fluidized bed spray agglomeration for porous particles

The present work develops a comprehensive one-dimensional pure agglomeration population balance model for simulating spray fluidized bed agglomeration of porous particles. The effect of micro-processes on aggregation kinetics is analyzed using a sophisticated constant number Monte-Carlo (CNMC) approach. The CNMC is used as a virtual spray fluidized bed granulator and takes into account several micro-processes such as wetting, convective drying, imbibition, agglomeration etc. so that it is a model for the real granulator. The main components influencing the agglomeration kinetics, namely the total number of particles, the number of wet particles, the collision frequency per particle and the average wet surface coverage fraction per wet particle are modeled and incorporated in the conventional one-dimensional population balance model. Finally, the proposed population balance model is validated against the results of CNMC.