Residence time distribution and micromixing efficiency of a dynamic inline rotor–stator mixer

Dolshanskiy, Wladislaw; Stepanyuk, Alexey; Arian, Elias; Pauer, Werner

doi:10.1016/j.cej.2022.138555

Cited by 13 publications

(7 citation statements)

References 68 publications

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“…Substituting eq into eq , the variance could be calculated as σ θ 2 = ∫ 0 ∞ n n θ n + 1 e − n θ ( n − 1 ) ! dθ − 1 = 1 n …”

Section: Rtd Analysesmentioning

confidence: 99%

“…13 The pulse input method of this technique has been extensively utilized in the RTD measurement due to less tracer and more direct access to the RTD curves. 14 The RTD of SDRs has been investigated experimentally in the past decades. Visscher et al 15 adopted ink as a tracer to investigate the RTD characteristics of the rotor−stator SDR.…”

Section: Introductionmentioning

confidence: 99%

“…RTD is generally measured by the stimulus-response technique . The pulse input method of this technique has been extensively utilized in the RTD measurement due to less tracer and more direct access to the RTD curves . The RTD of SDRs has been investigated experimentally in the past decades.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determination of Liquid Residence Time Characteristics in Spinning Disk Reactors via a Computational Approach

Li,

Xu,

et al. 2023

Ind. Eng. Chem. Res.

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Residence time distribution (RTD) is an essential hydrodynamic prerequisite for the appropriate modeling of reactors. Our previous study designed a spinning disk reactor (SDR) for intensifying photochemical processes with disk configurations of elliptic-cylindrical spoilers (ES) and cylindrical spoilers (CS). Herein, RTD of the tailored SDRs was numerically studied using a pulse input method after verification with experimentally measured RTD curves in the previous literature. Mean residence time (t m ) and dimensionless variance (σ θ 2 ) of the SDRs were in ranges of 0.29−1.43 s and 0.02−0.15, respectively. t m and σ θ 2 were higher in the SDR with ES and CS than in the SDR with a flat disk. The nonideal flow behavior was assessed through tanksin-series (TIS) and axial dispersion models. TIS (n) and Peclet numbers (Pe) were, respectively, in ranges of 10−70 and 10−150 for the SDR with spoilers, indicating a departure tendency from plug flow. Finally, based on the simulation results, correlations of n and Pe relative to physical properties and operating parameters were established with deviations less than ±20%.

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“…Substituting eq into eq , the variance could be calculated as σ θ 2 = ∫ 0 ∞ n n θ n + 1 e − n θ ( n − 1 ) ! dθ − 1 = 1 n …”

Section: Rtd Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Liquid Residence Time Characteristics in Spinning Disk Reactors via a Computational Approach

Li,

Xu,

et al. 2023

Ind. Eng. Chem. Res.

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“…3 Mixing quality can be reflected in two aspects: residence time distribution (RTD) and micromixing performance. 4 RTD reflects the macromixing characteristic and global flow behaviors. 5 A narrow RTD is always a prerequisite for obtaining satisfactory yield and selectivity.…”

Section: Introductionmentioning

confidence: 99%

“…Mixing plays a decisive role in many chemical processes such as nanoparticle synthesis, polymerization and organic synthesis . Mixing quality can be reflected in two aspects: residence time distribution (RTD) and micromixing performance . RTD reflects the macromixing characteristic and global flow behaviors .…”

Section: Introductionmentioning

confidence: 99%

Micromixing Performance and Residence Time Distribution in a Miniaturized Magnetic Reactor: Experimental Investigation and Machine Learning Modeling

Chen

Deng

Luo

2023

Ind. Eng. Chem. Res.

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Miniaturization of mixers is a hot topic in process intensification, but efficient mixing at operational conditions with relatively large viscosity μ and large flow rate ratio R between two mixed solutions is still a great challenge for micromixers. Herein, a miniaturized magnetic reactor (MMR) is developed, and its micro- and macromixing performances are characterized by the Villermaux/Dushman reaction and residence time distribution experiments, respectively. The results show that the miniaturization of the premixing unit and the stirred unit is vital. By arranging the stirred units in series, the MMR is effective in reducing axial dispersion, intensifying mixing at operational conditions with large μ and R and achieving efficient energy utilization without introducing extra energy input. The micromixing time t m in the optimized MMR ranges from 0.24 to 1.42 ms when 4.72 mPa·s < μ < 73.30 mPa·s and 8 < R < 20, indicating superior micromixing performance. Moreover, machine learning models are established and compared for predicting t m with excellent accuracy and guiding mixer design, and the SHAP method is adopted for model interpretation.

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Machine learning and metaheuristics in microfluidic transport characterization and optimization: CFD and experimental study integrated with predictive modelling

Kouhkord,

Amirmahani,

Hassani

et al. 2024

Can J Chem Eng

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This study presents a comprehensive numerical and experimental analysis on microfluidic cell lysis through computational fluid dynamics (CFD), data‐driven modelling, and multi‐objective optimization. The proposed intelligent framework integrates artificial intelligence and CFD for data generation and extraction, alongside machine learning analysis and experimental studies for transport phenomena characterization in the cell lysis process. The framework explores compound effects of various inflow Reynolds numbers and geometrical parameters, including obstacle configurations and microchannel thickness. It shows substantial effects on flow patterns and mixing in varied microfluidic designs. A surrogate model, developed via central composite design, exhibits high accuracy in assessing system functionality (). The height of the implemented baffles from its lower value to the upper bound resulted in more than 42% and 14% increase in the mixing index at low and high Reynolds numbers, respectively, with minimal impact on pressure drop. The framework introduces data‐driven modelling coupled with multi‐objective optimization by desirability function (DF), non‐dominated sorting genetic algorithm (NSGA‐II), and differential evolution (DE). In the optimization of microfluidic processes, machine learning algorithms outperform desirability‐based methods, and the DE algorithm surpasses the NSGA‐II. An optimum micromixing reducing the mixing length by over 50% and mixing index above 97% achieved, fabricated, and experimental investigations conducted to validate numerical process. Through the precise control of microfluidic variables and the exploitation of microtransfer phenomena, it is possible to enhance the efficiency and selectivity of cell lysis. This not only improves the accuracy of diagnostic information but also opens up new avenues for personalized medicine and therapeutic development.

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Residence time distribution and micromixing efficiency of a dynamic inline rotor–stator mixer

Cited by 13 publications

References 68 publications

Determination of Liquid Residence Time Characteristics in Spinning Disk Reactors via a Computational Approach

Determination of Liquid Residence Time Characteristics in Spinning Disk Reactors via a Computational Approach

Micromixing Performance and Residence Time Distribution in a Miniaturized Magnetic Reactor: Experimental Investigation and Machine Learning Modeling

Machine learning and metaheuristics in microfluidic transport characterization and optimization: CFD and experimental study integrated with predictive modelling

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