Dispersion of water into oil in a rotor–stator mixer. Part 1: Drop breakup in dilute systems

Rueger, Paul E.; Calabrese, Richard V.

doi:10.1016/j.cherd.2013.05.018

Cited by 40 publications

(25 citation statements)

References 19 publications

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“…RSMs are designed to deliver a narrow region of high intensity shear and/or turbulence in order to achieve efficient mixing and emulsification. Under turbulent conditions, this corresponds to delivering a high local energy density or dissipation rate of turbulent kinetic energy (TKE) [13,14,16,[40][41][42]. When comparing batch and continuous mode RSMs, one should therefore keep in mind that the proportion of the supplied energy which can be translated into intense turbulence differs between the two modes of operation.…”

Section: Pumping and Turbulent Dissipation Efficiencymentioning

confidence: 99%

“…Turbulent drop breakup is often explained in terms of Kolmogorov-Hinze theory [44][45][46][47][48], which suggests scaling relations between the largest drop diameter that can survive a given turbulent field and the dissipation rate of TKE of that field. Depending on the size of this limiting drop in relation to the size of the smallest turbulent structures (the Kolmogorov length-scale), different explicit scaling laws have been suggested, see References [7,41,47] for comprehensive reviews and some different explicit formulations.…”

Section: Implications For Emulsificationmentioning

confidence: 99%

“…However, as shown for impeller mixers [49] and high-pressure homogenizers [50], in order for this approach to be satisfactory, the dissipation rate of turbulent kinetic energy should be the local value in the most intense region (where breakup takes place). However, since the local dissipation rate of TKE is highly challenging to measure [49], practical application of Kolmogorov-Hinze theory to RSMs are often based on externally measurable quantities such as rotor speed [31,39,51], the total dissipation power (P diss ) [52,53], and the globally defined Reynolds and Weber numbers [41]. The global Weber number is defined as:…”

Section: Implications For Emulsificationmentioning

confidence: 99%

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Rotor-Stator Mixers: From Batch to Continuous Mode of Operation—A Review

Håkansson

2018

Processes

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Although continuous production processes are often desired, many processing industries still work in batch mode due to technical limitations. Transitioning to continuous production requires an in-depth understanding of how each unit operation is affected by the shift. This contribution reviews the scientific understanding of similarities and differences between emulsification in turbulent rotor-stator mixers (also known as high-speed mixers) operated in batch and continuous mode. Rotor-stator mixers are found in many chemical processing industries, and are considered the standard tool for mixing and emulsification of high viscosity products. Since the same rotor-stator heads are often used in both modes of operation, it is sometimes assumed that transitioning from batch to continuous rotor-stator mixers is straightforward. However, this is not always the case, as has been shown in comparative experimental studies. This review summarizes and critically compares the current understanding of differences between these two operating modes, focusing on shaft power draw, pumping power, efficiency in producing a narrow region of high intensity turbulence, and implications for product quality differences when transitioning from batch to continuous rotor-stator mixers.

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Section: Pumping and Turbulent Dissipation Efficiencymentioning

confidence: 99%

Section: Implications For Emulsificationmentioning

confidence: 99%

Section: Implications For Emulsificationmentioning

confidence: 99%

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Rotor-Stator Mixers: From Batch to Continuous Mode of Operation—A Review

Håkansson

2018

Processes

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“…The basic assumption of the theory is that in order to keep the droplet stable, the stress which makes the droplet breakup must keep balance with the stress which makes the droplet coalescence. Based on the different scaling laws, expressions of the stress balance have different forms, 33–35 as shown in Equation ():

\frac{τ_{italicunstable}}{τ_{italicstable}} ~ ({}, \begin{matrix} \frac{ρ_{c} u {(d)}^{2}}{σ / d} = ({, \begin{matrix} \frac{ρ_{c} ε^{2 / 3} d^{2 / 3}}{σ / d}, d_{\max} ≫ η \\ \frac{ρ_{c} ν_{c}^{- 1} ε d^{2}}{σ / d}, d_{\max} \approx η \end{matrix}) \\ \frac{μ {(ε / ν)}^{0.5}}{σ / d}, d_{\max} ≪ η \end{matrix})

where

η

is the so called “Kolmogoroff length,” which represents the size of smallest eddies, and can be calculated as 36 :

η \approx {({((), μ_{c} / ρ_{c})}^{3} / ε)}^{0.25}

when

d_{\max} ≫ η

, the droplet breakup is mainly caused by the inertial stress. When

d_{\max} ≪ η

, the viscous stress is the primary factor in droplet breakup.…”

Section: Resultsmentioning

confidence: 99%

Hydrodynamics and droplet size distribution of liquid–liquid flow in a packed bed reactor with orifice plates

Cai

Wang

et al. 2021

AIChE Journal

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A packed bed reactor with orifice plates (PBR@OP) was designed by adding orifice plates periodically in packed beds. Hydrodynamics and droplet size distribution in PBR@OP were experimentally investigated using fatty acid methyl esters (FAME)/water as the model liquid–liquid system. In PBR@OP, the flow pattern was close to plug flow. Droplets with Sauter mean diameter (d32) of 150–550 μm were generated. The pressure drop of orifice, flow velocity and plate spacing were key parameters to control the droplet size. The reactor performance was evaluated by analyzing a FAME epoxidation process. At the same d32 and residence time, the length and total pressure drop of PBR@OP were about 1/3 and 1/4 of those of PBR without orifice plates, respectively. Furthermore, a semi‐empirical correlation describing the d32 change in PBR@OP was developed, revealing a relative mean deviation of 8.64%. PBR@OP presents a cost‐effective option for the intensification of liquid–liquid medium rate reactions.

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“…The drop diameter calculated using Equation (9) was compared to that calculated using an empirical equation derived from direct measurement where the data was gathered from published work [11]. The method of developing measurement data-basedempirical equations has been presented by Rueger and Calabrese [12]. The data is better to be classified based on flow regimes, because the dispersion mechanism is different.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Application of Computational Fluid Dynamics for Modeling of Hydrodynamics and Mass Transfer of Laboratory Scale Crude Palm Oil Degumming Process

2018

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In this research, computational fluid dynamics was applied to model a laboratory reactor for the degumming of crude palm oil (CPO) involving immiscible liquid-liquid mixing and being controlled by mass transfer. The fluid mixing of CPO and phosphoric acid in the reactor was simulated using multiphase mixture model and standard κ-ε turbulence model in steady state mode. The simulation predicts the distributions of the drop diameter, the dispersed-phase relative velocity, the drop Reynolds number and the interfacial area density. The mass transfer coefficient from experimental work is correlated using the model as Sherwood number Shd = 0.02576 Red0.673Sc0.431 with R2 being 0.91. Finally, the volumetric mass transfer coefficient was calculated and compared to the experiment result.

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Dispersion of water into oil in a rotor–stator mixer. Part 1: Drop breakup in dilute systems

Cited by 40 publications

References 19 publications

Rotor-Stator Mixers: From Batch to Continuous Mode of Operation—A Review

Rotor-Stator Mixers: From Batch to Continuous Mode of Operation—A Review

Hydrodynamics and droplet size distribution of liquid–liquid flow in a packed bed reactor with orifice plates

Application of Computational Fluid Dynamics for Modeling of Hydrodynamics and Mass Transfer of Laboratory Scale Crude Palm Oil Degumming Process

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