Measuring Volumetric Phase Fractions in a Gas‐Solid‐Liquid Stirred Tank Reactor Using an Impedance Probe

Brunazzi, Elisabetta; Festa, Umberto Di; Galletti, Chiara; Merello, Cesare; Paglianti, Alessandro; Pintus, Sandro

doi:10.1002/cjce.5450800407

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…Such techniques base on measuring electrical capacitance and/or conductance of mixtures depending on the electrical properties of the working fluids. Electrical impedance techniques can be used for both local [18][19][20][21] and integral measurements of the phase holdups [22]. Furthermore, they can be used for both small-scale vessels and large industrial stirred tank reactors and pipes.…”

Section: Measurement Science and Technologymentioning

confidence: 99%

Quantitative cross-sectional measurement of solid concentration distribution in slurries using a wire-mesh sensor

Santos

Schleicher

Reinecke

et al. 2015

Meas. Sci. Technol.

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Wire-mesh sensors have so far been widely applied in gas–liquid flows where resistance or capacitance distributions are measured and converted into gas or liquid holdup distributions. In this work we report on the qualification of the wire-mesh imaging technique for the measurement of cross-sectional solid concentrations in solid–liquid mixtures. As the dielectric constants of solid particles are different from those of gas, water or oil in the flow, measuring this property can be used as an indication of solid distribution. Experiments were performed in a stirred tank of 100 mm diameter equipped with a capacitance wire-mesh sensor. The wire-mesh sensor was operated at an acquisition speed of 4000 frames per second and has a spatial resolution of 6.25 mm. As solids we used silica sand particles (diameter ~250 μm) which were suspended in water in a volume concentration range of 1% to 35% to form slurries. By varying the stirring speed, different solid concentration distributions were produced and investigated. In order to convert the measured relative permittivity distribution into a solid concentration distribution, an empirical approach was employed.

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Section: Measurement Science and Technologymentioning

confidence: 99%

Quantitative cross-sectional measurement of solid concentration distribution in slurries using a wire-mesh sensor

Santos

Schleicher

Reinecke

et al. 2015

Meas. Sci. Technol.

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“…The ability of mass transfer in a gas–liquid system is indexed by the gas holdup and the bubble diameter; these two together determine the interfacial area. For Newtonian fluids, studies to obtain gas holdup , and the bubble diameter − and propose correlations for their prediction have been a subject of interest for several investigations. A few data and correlations have been reported for studies with non-Newtonian fluids .…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Impellers for Gas Dispersion in Power-Law Fluids

2021

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The 3D multiphase computational fluid dynamic (CFD) models have been developed for gas−liquid systems in a standard baffled stirred tank reactor (diameter 0.3 m) for non-Newtonian power-law fluids. The standard k-ε model has been used to model the turbulence, and drag has been modeled with the Grace drag model with Brucato modifications. CFD simulations have been carried out for three impellers, namely, the Rushton turbine, pitched blade turbine, and hydrofoil impeller. The effects of impeller design, power input per unit volume of the fluid (P/V) (1200− 5200 W/m 3 ), fluid rheology (K = 0−0.1561 Pa•s n , n = 0.687−1.000), and superficial gas velocity (7.1−28.3 mm/s) have been analyzed. The CFD model has been validated with the local distribution of axial, radial, and tangential velocities and overall gas holdup data reported in the literature. The CFD model has been used to predict the regime transition from flooding to loading and complete dispersion. A good agreement was observed for the prediction of regime transition for the Rushton turbine when compared with the experimental data. The average value of absolute error for the prediction of the gas holdup is 20%, which indicates that the developed CFD model can be successfully used to predict the gas holdup and regime transition in stirred tank reactors. Among the impellers studied, the hydrofoil impeller shows the highest gas holdup and uniform gas distribution in the vessel at low power consumption.

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“…AE have been used for gas-bubble sizing [12] and the interrelationship between mass transfer and the sound spectra emitted by sparger, impeller, and bubbles have been investigated. [13][14][15] Many techniques have been applied to identify flow regime transitions, for example, fibre optic probe, [16] resistivity probe, [17] impedance probes, [18] level probes, [19] ultrasonic [20,21] computer tomography coupled with computational fluid dynamics (CFD), [22] x-ray tomography, [23,24] particle image velocimetry (PIV) coupled with CFD, [25] γ-ray, [26] and electrical resistance tomography (ERT). [27,28] All of these methods have been successful in identifying flow regimes, but their implementation on the industrial scale is limited by practical factors such as cost, reliability, retrofitting, opacity of the system, intrusion, safety, installation time, cost, and response time.…”

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confidence: 99%

Flow regime identification in aerated stirred vessel using passive acoustic emission and machine learning

et al. 2023

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Smart at‐ or online process sensors, which employ machine learning (ML) to process data, have been the subject of extensive research in recent years, due to their potential for real‐time process control. In this paper, a passive acoustic emission process sensor has been used to detect gas–liquid regimes within a stirred, aerated vessel using novel ML approaches. Pressure fluctuations (acoustic emissions) in an air‐water system were recorded using a piezoelectric sensor installed on the external wall of three identical cylindrical tanks of diameter, T = 160 mm, filled to a volume of 5 L (height, H = 1.5 T). The tanks were made of either glass, steel, or aluminium, and each tank was equipped with a Rushton turbine of diameter, D = 0.35 T. The investigated flow regimes, flooding, loading, complete dispersion, and un‐gassed, were obtained by changing the air feed flow rates and by varying the impeller speed. The acoustic spectra obtained were processed to select an optimal number of features characterizing each of the regimes, and these were used to train three different ML algorithms. The pre‐processing includes a principal component analysis (PCA) step, which reduces the volume of data fed to the ML algorithms, saving computational time up to a factor of 5. The algorithms (decision tree, k‐nearest neighbour, and support vector machines) were challenged to use these features to identify the correct flow regime. Accurate predictions of the three gas–liquid regimes of interest have been achieved. The accuracy of the prediction ranges from 90% to 99%, and this difference is related to the material used for the vessel.

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Measuring Volumetric Phase Fractions in a Gas‐Solid‐Liquid Stirred Tank Reactor Using an Impedance Probe

Cited by 10 publications

References 14 publications

Quantitative cross-sectional measurement of solid concentration distribution in slurries using a wire-mesh sensor

Quantitative cross-sectional measurement of solid concentration distribution in slurries using a wire-mesh sensor

Comparison of Different Impellers for Gas Dispersion in Power-Law Fluids

Flow regime identification in aerated stirred vessel using passive acoustic emission and machine learning

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