CFD in Drying Technology – Spray‐Dryer Simulation

Blei, Stefan; Sommerfeld, Martin

doi:10.1002/9783527631629.ch5

Cited by 17 publications

(3 citation statements)

References 59 publications

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“…The mean particle residence time is larger than the mean air residence time, which was calculated as 29 s at the given process conditions. The flow within a spray chamber can be described by different flow regimes with a fast main central gas jet and a slower flow close to the wall [9] . Due to an increasing nozzle pressure there is a higher acceleration of the feed by the pressurized gas and a decrease in spray cone angle.…”

Section: Fig 3: Mean Residence Time τ (Left) and Number Of Ideal Stimentioning

confidence: 99%

Determination and modelling of the particle size dependent residence time distribution in a pilot plant spray dryer

Ruprecht

Kohlus

2018

Proceedings of 21th International Drying Symposium

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The residence time distribution (RTD) in a pilot plant spray dryer was characterised for two kinds of air distributors (centrifugal and parallel flow) and for different atomizing air pressures. To determine the RTD - and the RTD of different particle size fractions - the particle concentration and size at the dryer outlet was measured continuously using a particle counter. Results were modelled using the Bodenstein number and the CSTR in series model. An increasing nozzle pressure leads to a decrease in mean residence time and a more narrow distribution. The influence of nozzle pressure is more pronounced than of air distributor and particle size fraction. Keywords: Residence time distribution; Particle size; Bodenstein number modelling; Nozzle influence; Mechanism of air distribution

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Section: Fig 3: Mean Residence Time τ (Left) and Number Of Ideal Stimentioning

confidence: 99%

Determination and modelling of the particle size dependent residence time distribution in a pilot plant spray dryer

Ruprecht

Kohlus

2018

Proceedings of 21th International Drying Symposium

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“…However, in those processes where inter-particle interactions affect the properties of the particle streams, and thereby affecting the continuous phase, this approach still does not lead to accurate solutions. The counter-current spray drying process is one of the cases where particle agglomeration strongly affects mass, heat and momentum transfer processes [2,4,23].…”

Section: Introductionmentioning

confidence: 99%

CFD Model of Particle Agglomeration in Spray Drying

2015

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A 3D CFD model of the agglomeration of droplets and particles in a counter-current spray drying process was developed and verified. An original discrete phase model was elaborated, with an agglomeration module taking into account hydrodynamic segregation of particles, droplet coalescence and droplet shrinkage, for accurate calculations of mass balance of the discrete phase. The characteristic drying curves were applied to the model of particle moisture evaporation, which included the coupling of particle agglomeration with heat, mass and momentum transfer between the discrete and continuous phases. Two agglomeration zones were observed in the tower: wet particle agglomeration in the atomization zone, and "dry agglomeration" above the air inlets, due to the intensive mixing of particle streams. A comparison of the calculated particle size distributions and experimental data obtained from particle dynamics analysis (PDA) measurements proves the accuracy of the developed methodology. The elaborated model allows the final PSD of the powder in the spray towers to be predicted.

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“…[5][6][7] The predictive approach, which currently gives the highest level of details, is by using the computational fluid dynamics (CFD) technique. [8][9][10] This technique is useful in mathematically capturing spray dryers with non-conventional geometries and may be used to predict phenomena such as agglomeration, deposition or product quality changes within the drying chamber.…”

Section: Introductionmentioning

confidence: 99%

A practical CFD modeling approach to estimate outlet boundary conditions of industrial multistage spray dryers: Inert particle flow field investigation

et al. 2018

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Industrial multistage spray drying systems often have limited in situ process measurements to provide sufficient information for computational fluid dynamics (CFD) simulations of the primary drying chamber. In this case study on the spray dryer at Davis Dairy Plant (South Dakota State University), uncertainties were encountered in specifying the outlet boundary conditions of the spray drying chamber with two outlets: the side outlet and the bottom outlet leading to the second stage external vibrating bed. Using the available data on the vacuum pressure of the chamber, a numerical framework was introduced to approximate suitable outlet boundary conditions for the drying chamber. The procedure involved analyzing the ratio of the airflow rate between the two outlets and using a pseudo-tracer inert particle injection analysis. The goal of this approach was to determine a suitable range of outlet vacuum pressure that will lead to realistic particle movement behaviors during the actual plant operation. The protocol developed here will be a useful tool for CFD modeling of large scale multistage spray drying systems.

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CFD in Drying Technology – Spray‐Dryer Simulation

Cited by 17 publications

References 59 publications

Determination and modelling of the particle size dependent residence time distribution in a pilot plant spray dryer

Determination and modelling of the particle size dependent residence time distribution in a pilot plant spray dryer

CFD Model of Particle Agglomeration in Spray Drying

A practical CFD modeling approach to estimate outlet boundary conditions of industrial multistage spray dryers: Inert particle flow field investigation

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