# Product-Property Guided Scale-Up of a Fluidized Bed Spray Granulation Process Using the CFD-DEM Method

^{*}

## Abstract

**:**

## 1. Introduction

- Layering granulation, where particles quickly dry and the injected liquid leaves a solid residue that forms a shell or coating, and
- agglomeration, where the cohesive forces of the liquid cause the particles to remain in contact, resulting in the formation of larger granules after solidification of the liquid or sintering.

- the product moisture content,
- the area surface roughness as analyzed by confocal microscopy,
- the modulus of elasticity using compression testing,
- the granule porosity using X-ray micro-computer tomography,
- the wetting behavior using the contact angle as given by the sessile drop experiment.

## 2. Materials and Methods

#### 2.1. CFD-DEM Simulation

- droplet injection and transport in the gas phase,
- droplet evaporation,
- deposition of droplets onto the particle surface,
- evaporation of liquid on the particle surface,
- transport of energy/enthalpy in the gas phase and
- transport of a vapor species in the gas phase.

`cfdemSolverChem`by Kinaci et al. [11] that considers heat and species transport in an implicit manner. The details of their implementation can be found in those respective publications. Another Lagrangian phase to represent the droplets was introduced using functionality from the

`sprayFoam`solver of OpenFOAM. The detail of their grid-based deposition onto particles is given in the work of Kieckhefen et al. [12].

#### 2.2. Tracked Quantities

- Solids concentration in the droplets at impact: The concentration of the solid component of droplets upon impact is an indicator for the intensity of the drying conditions that occur—determining interplay of solvent removal and aggregation of the solid components by diffusion, relating to the time available for nucleation and crystallization to occur for solutions and aggregation to take place in the case of suspensions.
- Relative velocity at impact: The relative velocity between particle and droplet, together with viscosity and surface tension (both dependent on solids concentration), should correlate with the droplet interaction regime.

#### 2.3. Product-Property Tracked Quantity Correlation

- Population-based: Distributions over all tracked quantities are analyzed separately. This has the key advantage of being easily automated and taking into consideration the spread of the entire population of particles.
- Particle-based: For single or selected particle populations, tracked quantities are correlated with each other to give a temporal sequence of events or states in which the particle is, e.g., periods of drying alternating with wetting. While this may be more intuitive to analyze, automatically scaling this analysis to the entire particle population is much more difficult.

#### 2.4. Workflow

- Calibration experiments,
- calibration simulations,
- evaluation of simulations and experiments, derivation of a mapping and
- predictive simulation.

#### 2.5. Assumptions and Limitations

#### 2.6. Laboratory-Scale Experiments

- Fluidization air flow rate,
- fluidization air temperature,
- liquid spray rate,
- atomization air pressure and
- nozzle air temperature.

#### 2.7. Particle Roughness Quantification

#### 2.8. Pilot-Scale Experiments

- the gas velocity,
- the net spray rate per distributor area and
- the bed mass per distributor area

#### 2.9. Simulation Setup

## 3. Results and Discussion

#### 3.1. Laboratory-Scale Simulations

#### 3.2. Pilot-Scale Simulations

#### 3.3. Product-Property Tracked Quantity Mapping

#### 3.4. Prediction of Product Properties on the Pilot-Scale

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Schematic of the proposed approach to correlate the tracked quantity with product property.

**Figure 3.**Example result of laser-scanning confocal microscopy: 3D profile (

**a**) and post-processing of extracted line profile for later roughness calculation (

**b**).

**Figure 4.**Dimensions of the simulation domain of the GF25 pilot-scale plant in $\mathrm{m}\mathrm{m}$.

**Figure 7.**Cross-section of the mesh used for the simulations of the pilot-scale GF25 plant in $\mathrm{m}\mathrm{m}$.

**Figure 8.**Mean particle temperature and mean particle surface liquid over time in case 3 of Orth et al. [18], with a fluidization gas velocity of ${\dot{V}}_{\mathrm{air}}=80\mathrm{N}{\mathrm{m}}^{3}{\mathrm{h}}^{-1}$ and temperature of ${T}_{\mathrm{air},\mathrm{in}}=50{}^{\circ}\mathrm{C}$, a spray rate of ${\dot{M}}_{\mathrm{spray}}=20\mathrm{g}{min}^{-1}$, an atomization pressure of ${p}_{\mathrm{spray}}=0.5\overline{}$ and a spray air temperature of $20{}^{\circ}\mathrm{C}$.

**Figure 9.**Instantaneous simulation snapshots showing gas and particle temperature, spray cloud, water concentration in the gas phase and the rate of heat transfer in the apparatus midplane ($z=0$). The process conditions were set for the reference case at a fluidization gas velocity ${\dot{V}}_{\mathrm{air}}=105\mathrm{N}{\mathrm{m}}^{3}{\mathrm{h}}^{-1}$ and temperature ${T}_{\mathrm{air},\mathrm{in}}=85{}^{\circ}\mathrm{C}$, an atomization pressure of ${p}_{\mathrm{spray}}=1.8\mathrm{bar}$ and a spray air temperature of $70{}^{\circ}\mathrm{C}$, corresponding to experiments (4)–(6).

**Figure 10.**Time-averages over 10 s of simulation time, showing gas temperature and water concentration in the gas phase in the apparatus midplane ($z=0$). The process conditions were set at a fluidization gas velocity ${\dot{V}}_{\mathrm{air}}=130\mathrm{N}{\mathrm{m}}^{3}{\mathrm{h}}^{-1}$ and temperature ${T}_{\mathrm{air},\mathrm{in}}=120{}^{\circ}\mathrm{C}$, a spray rate of ${\dot{M}}_{\mathrm{spray}}=10\mathrm{g}{min}^{-1}$, an atomization pressure of ${p}_{\mathrm{spray}}=3.0\mathrm{bar}$ and a spray air temperature of 20 °C.

**Figure 11.**Photo of the GF25 plant and a snapshot of a representative simulation showing the injection of droplets through all four nozzles into the bubbling fluidized bed. (

**a**) Photo of the Glatt GF25 pilot-scale granulator plant. (

**b**) Simulation snapshot of the GF25 simulations, showing the temperatures of the particles, and the positions of the droplets, indicated in white.

**Figure 12.**Mean particle temperature and per-parcel water mass over time for the GF25 pilot-scale plant in three nozzle configurations and the GF3 lab-scale plant operated at a spray rate ${\dot{M}}_{\mathrm{spray}}=\left(8\phantom{\rule{0.166667em}{0ex}}\times \phantom{\rule{0.166667em}{0ex}}\right)15\mathrm{g}{min}^{-1}$.

**Figure 13.**Distribution of particle surface liquid/water mass and particle temperature across the apparatus length for the same net amount of liquid (${\dot{M}}_{\mathrm{spray}}=8\phantom{\rule{0.166667em}{0ex}}\times \phantom{\rule{0.166667em}{0ex}}15\mathrm{g}{\mathrm{min}}^{-1}$) injected through one, two and four nozzles.

**Figure 14.**Particle and gas phase temperatures in the GF25 plant (spray rate ${\dot{M}}_{\mathrm{spray}}=8\phantom{\rule{3.33333pt}{0ex}}\phantom{\rule{0.166667em}{0ex}}\times \phantom{\rule{0.166667em}{0ex}}\phantom{\rule{3.33333pt}{0ex}}20\mathrm{g}{\mathrm{min}}^{-1}$) after a 60 $\mathrm{s}$ process when introducing the same amount of liquid through one (on the very left), two (the outermost) or all four nozzles. Only wetted particles are shown and colored according to their temperature.

**Figure 15.**Tracked quantity distributions and their statistical moments for the lab-scale plant (GF3) and pilot-scale plant (GF25). (

**a**) Statistical moments of tracked quantities at a spray rate of ${\dot{M}}_{\mathrm{spray}}=8\phantom{\rule{3.33333pt}{0ex}}\phantom{\rule{0.166667em}{0ex}}\times \phantom{\rule{0.166667em}{0ex}}\phantom{\rule{3.33333pt}{0ex}}15\mathrm{g}{\mathrm{min}}^{-1}$. (

**b**) Statistical moments of tracked quantities at a spray rate of ${\dot{M}}_{\mathrm{spray}}=8\phantom{\rule{3.33333pt}{0ex}}\phantom{\rule{0.166667em}{0ex}}\times \phantom{\rule{0.166667em}{0ex}}\phantom{\rule{3.33333pt}{0ex}}20\mathrm{g}{\mathrm{min}}^{-1}$.

**Figure 16.**Influence of spray rate ${\dot{M}}_{\mathrm{spray}}$ on the tracked quantity distributions for the laboratory-scale case at ${\dot{V}}_{\mathrm{air}}=105{\mathrm{m}}^{3}{\mathrm{h}}^{-1}$, ${T}_{\mathrm{air},\mathrm{in}}=85{}^{\circ}\mathrm{C}$, ${p}_{\mathrm{spray}}=1.8\mathrm{bar}$, ${T}_{\mathrm{spray}}=70{}^{\circ}\mathrm{C}$ unless otherwise indicated.

**Figure 17.**Influence of air temperature ${T}_{\mathrm{air},\mathrm{in}}$ on the tracked quantity distributions for the laboratory-scale case at ${\dot{V}}_{\mathrm{air}}=105{\mathrm{m}}^{3}{\mathrm{h}}^{-1}$, ${p}_{\mathrm{spray}}=1.8\mathrm{bar}$, ${\dot{M}}_{\mathrm{spray}}=15\mathrm{g}{min}^{-1}$, ${T}_{\mathrm{spray}}=70{}^{\circ}\mathrm{C}$ unless otherwise indicated.

**Figure 18.**Parity plots for the mapping between tracked quantities and arithmetic mean surface roughness ${S}_{\mathrm{a}}$.

**Figure 19.**Parity plot comparing the measured surface roughness data for pilot-scale (GF25) and lab-scale (GF3) granulation experiments and the predicted roughness using the tracked quantity approach.

Parameter | Symbol | Unit | Values | ||
---|---|---|---|---|---|

Low | Mid | High | |||

Fluidization air flow rate | ${\dot{V}}_{\mathrm{g}}$ | ${\mathrm{m}}^{3}{\mathrm{h}}^{-1}$ | 80 | 105 | 130 |

Fluidization air temperature | ${T}_{\mathrm{g}}$ | ${}^{\circ}\mathrm{C}$ | 50 | 85 | 120 |

Spray air pressure | ${p}_{\mathrm{noz}}$ | $\mathrm{bar}$ | 0.5 | 1.8 | 3.0 |

Spray solution flow rate | ${\dot{M}}_{\mathrm{noz},\mathrm{l}}$ | $\mathrm{g}{\mathrm{min}}^{-1}$ | 10 | 15 | 20 |

Spray air temperature | ${T}_{\mathrm{noz},\mathrm{g}}$ | ${}^{\circ}\mathrm{C}$ | 20 | 70 | 120 |

**Table 2.**Experimental plan used for the sodium benzoate on Cellets experiments performed in the lab-scale granulator.

Fluidization Air | Spray Air | ||||
---|---|---|---|---|---|

ID | Flow Rate [Nm^{3} h^{−1}] | Temperature [°C] | Pressure [bar] | Solution Flow Rate [g min^{−1}] | Temperature [°C] |

1 | 105 | 85 | 1.8 | 10 | 70 |

2 | 130 | 50 | 3.0 | 10 | 120 |

3 | 80 | 50 | 0.5 | 20 | 20 |

4 | 105 | 85 | 1.8 | 15 | 70 |

5 | 105 | 85 | 1.8 | 15 | 70 |

6 | 105 | 85 | 1.8 | 15 | 70 |

7 | 80 | 50 | 3.0 | 10 | 20 |

8 | 105 | 85 | 1.8 | 15 | 120 |

9 | 130 | 120 | 3.0 | 10 | 20 |

10 | 130 | 85 | 1.8 | 15 | 70 |

12 | 80 | 50 | 0.5 | 10 | 120 |

13 | 130 | 120 | 0.5 | 10 | 120 |

14 | 130 | 50 | 3.0 | 20 | 20 |

15 | 130 | 120 | 0.5 | 20 | 20 |

16 | 105 | 85 | 1.8 | 15 | 20 |

17 | 130 | 50 | 0.5 | 20 | 120 |

18 | 80 | 120 | 3.0 | 20 | 20 |

19 | 105 | 85 | 1.8 | 20 | 70 |

20 | 80 | 120 | 0.5 | 20 | 120 |

21 | 130 | 120 | 3.0 | 20 | 120 |

22 | 80 | 120 | 0.5 | 10 | 20 |

23 | 105 | 85 | 3.0 | 15 | 70 |

24 | 130 | 50 | 0.5 | 10 | 20 |

25 | 105 | 120 | 1.8 | 15 | 70 |

26 | 80 | 120 | 3.0 | 10 | 120 |

28 | 105 | 50 | 1.8 | 15 | 70 |

30 | 80 | 85 | 1.8 | 15 | 70 |

31 | 80 | 50 | 3.0 | 20 | 120 |

32 | 105 | 85 | 0.5 | 15 | 70 |

Symbol | Unit | Glatt GF3 | Glatt GF25 | |
---|---|---|---|---|

Geometry | ||||

Base Dimensions | $\mathrm{m}$ | $\phantom{\rule{4pt}{0ex}}\varphi 0.2$ | $0.25\times 1$ | |

Base Area | ${A}_{\mathrm{in}}$ | ${\mathrm{m}}^{2}$ | 0.0314 | 0.25 |

Fluidization Air | ||||

Flow Rate | ${\dot{V}}_{\mathrm{air},\mathrm{in}}$ | ${\mathrm{Nm}}^{3}{\mathrm{h}}^{-1}$ | 105 | 840 |

Temperature | ${T}_{\mathrm{air},\mathrm{in}}$ | ${}^{\circ}\mathrm{C}$ | 85 | 85 |

Spray | ||||

Atomization Pressure | ${p}_{\mathrm{spray}}$ | $\mathrm{bar}$ | 1.8 | 1.8 |

Air Temperature | ${T}_{\mathrm{atom}.,\mathrm{in}}$ | ${}^{\circ}\mathrm{C}$ | 20 | 20 |

Solute Concentration | ${x}_{\mathrm{s},0}$ | $\mathrm{k}\mathrm{g}\mathrm{k}{\mathrm{g}}^{-1}$ | 0.3 | 0.3 |

Bed Mass | ${M}_{\mathrm{bed}}$ | $\mathrm{k}\mathrm{g}$ | 2 | 16 |

**Table 4.**Material properties, contact model parameters and numerical setup for the CFD-DEM models. Contact model properties were taken from [21].

Quantity | Symbol | Value |
---|---|---|

Numerics | ||

Time Step | ||

CFD | $\mathsf{\Delta}{t}_{\mathrm{CFD}}$ | $5\times {10}^{-4}$$\mathrm{s}$ |

DEM | $\mathsf{\Delta}{t}_{\mathrm{DEM}}$ | $1\times {10}^{-4}$$\mathrm{s}$ |

Coupling Interval | $\mathsf{\Delta}{t}_{\mathrm{couple}}$ | $5\times {10}^{-4}$$\mathrm{s}$ |

Scaling Factor (Coarse Graining) | ${\delta}_{\mathrm{CG}}$ | 4 |

Particle | ||

Diameter | ${d}_{\mathrm{p}}$ | $650\times {10}^{-6}$$\mathrm{m}$ |

Density | ${\rho}_{\mathrm{p}}$ | 1400 $\mathrm{k}\mathrm{g}$${\mathrm{m}}^{-3}$ |

Young’s Modulus | ||

Particle–Particle | ${Y}_{\mathrm{p}}$ | $1\times {10}^{6}$$\mathrm{Pa}$ |

Particle–Wall | ${Y}_{\mathrm{w}}$ | $2\times {10}^{6}$$\mathrm{Pa}$ |

Poisson Ratio | $\eta $ | 0.22 |

Restitution Coefficient | ||

Particle–Particle | ${e}_{\mathrm{pp}}$ | 0.051 |

Particle–Wall | ${e}_{\mathrm{pw}}$ | 0.051 |

Friction Coefficient | ||

Particle–Particle | ${k}_{\mathrm{fr},\mathrm{pp}}$ | 0.3 |

Particle–Wall | ${k}_{\mathrm{fr},\mathrm{pw}}$ | 0.3 |

Rolling Friction Coefficient | ||

Particle–Particle | ${k}_{\mathrm{rfr},\mathrm{pp}}$ | 0.083 |

Particle–Wall | ${k}_{\mathrm{rfr},\mathrm{pw}}$ | 0.028 |

Liquid | ||

Density | ${\rho}_{\mathrm{l}}$ | 1000$\mathrm{k}\mathrm{g}$${\mathrm{m}}^{-3}$ |

Heat Capacity | ${C}_{\mathrm{v},\mathrm{l}}$ | 4186 $\mathrm{J}$${\mathrm{k}\mathrm{g}}^{-1}$${\mathrm{K}}^{-1}$ |

Heat of Evaporation | $\mathsf{\Delta}{h}_{\mathrm{l}}^{\mathrm{LV}}$ | $2.55\times {10}^{6}$$\mathrm{J}$ ${\mathrm{k}\mathrm{g}}^{-1}$ |

Droplets per Parcel | ${N}_{\mathrm{droplet}}$ | 4 |

Atomization Pressure | Atomization Air Flow Rate | Median Droplet Size |
---|---|---|

${\mathit{p}}_{\mathbf{spray}}$ | ${\dot{\mathit{M}}}_{\mathbf{air},\mathbf{noz}}$ | ${\mathit{d}}_{\mathbf{drop}}$ |

[bar] | kg h^{−1} | [$\mathsf{\mu}$m] |

0.5 | 2 | 42 |

1.8 | 4 | 32 |

3.0 | 5 | 22 |

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**MDPI and ACS Style**

Kieckhefen, P.; Pietsch-Braune, S.; Heinrich, S.
Product-Property Guided Scale-Up of a Fluidized Bed Spray Granulation Process Using the CFD-DEM Method. *Processes* **2022**, *10*, 1291.
https://doi.org/10.3390/pr10071291

**AMA Style**

Kieckhefen P, Pietsch-Braune S, Heinrich S.
Product-Property Guided Scale-Up of a Fluidized Bed Spray Granulation Process Using the CFD-DEM Method. *Processes*. 2022; 10(7):1291.
https://doi.org/10.3390/pr10071291

**Chicago/Turabian Style**

Kieckhefen, Paul, Swantje Pietsch-Braune, and Stefan Heinrich.
2022. "Product-Property Guided Scale-Up of a Fluidized Bed Spray Granulation Process Using the CFD-DEM Method" *Processes* 10, no. 7: 1291.
https://doi.org/10.3390/pr10071291