<i>In situ</i> characterization of mixing and sedimentation dynamics in an impinging jet ballast tank via acoustic backscatter

Bux, Jaiyana; Paul, Neepa; Hunter, Timothy N.; Peakall, Jeff; Dodds, Jonathan M.; Biggs, Simon

doi:10.1002/aic.15683

Cited by 11 publications

(20 citation statements)

References 46 publications

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“…5). The intensity subsequently decreases monotonically with distance, consistent with attenuation-dominated systems [26][27][28][29], followed by an intense peak marking the column base at the furthermost measured transducer range.…”

Section: Comparison Of Backscatter and Attenuation Responses Of Variosupporting

confidence: 53%

“…Comparing the backscatter response between the 1 and 2 MHz frequencies in Fig 5 (I) indicates some similarities and differences to previous measurements on inorganic mineral systems comprising small particle sizes. [26,27]. Attenuation increases from 1 to 2 MHz as would be expected [28], due to its natural enhancement with decreasing wavelength.…”

Section: Comparison Of Backscatter and Attenuation Responses Of Variomentioning

confidence: 65%

“…The general backscatter intensity results of these colloidal organic particles are weak compared to those exhibited by near colloidal inorganic barytes [26] and titania suspension [27] systems. This behaviour is partly a function of particle size, where the pMMA particles are the smallest of the particle systems tested here, whereas the minerals are intermediate and the glass beads are the largest (refer to Table 1).…”

Section: Comparison Of Backscatter and Attenuation Responses Of Variomentioning

confidence: 78%

“…Hay [39] observed this behaviour when comparing backscatter strength directly with concentration. Previously, the current authors have measured the m of highly attenuating barium sulphate particles, which were near-colloidal in size (d50 = 7.8 µm), in concentrations up to 64 kgm -3 within a 0.6 m depth vessel [26].…”

Section: Theory and Calibration Proceduresmentioning

confidence: 96%

“…The current authors have previously undertaken similar analysis alongside phenomenological approaches to characterise concentrated, settling and turbulent dispersions of large glass beads, plastic particles and colloidal minerals in pipe flows, as well as small and large industrial scale tanks [26][27][28][29][30][31][32]. Collectively, this research provides pathways to enable online characterisation of many concentrated dispersion systems in industries ranging from cosmetic, pharmaceutical, food and paint products, to water treatment, minerals and nuclear waste processing.…”

Section: Introductionmentioning

confidence: 95%

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Measurement and density normalisation of acoustic attenuation and backscattering constants of arbitrary suspensions within the Rayleigh scattering regime

et al. 2019

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The scattering and attenuation of megahertz frequency acoustic backscatter in liquid suspensions, is examined for a range of fine organic and inorganic particles in the Rayleigh regime, 10 -4 < ka < 10 0 (where k is the wavenumber and a the particle radius) which are widely industrially relevant, but with limited existing data. In particular, colloidal latex, mineral titania and barytes sediments, as well as larger glass powders were investigated. A manipulation of the backscatter voltage equation was used to directly measure the sediment attenuation constants, . Decoupling of the combined backscattering-transducer constant, allowing explicit measurement of the backscattering constant, ks, was achieved through calibration of the transducer constant, kt. Additionally, the methodology was streamlined via averaging between a number of intermediate concentrations to reduce data variability. This approach enabled the form function, f, and the corresponding total normalized scattering cross-sections, , to be determined for all species. While f and are available in the literature for large glass and sand, this methodology allowed extension for the colloidal organic and inorganic particles. Specific gravity normalisation of f collapsed all data onto a single distribution, with the exception of titania, due to scattering complexities associated withagglomeration. There was some additional variation in , with measured values of the fine particles up to of magnitude greater than the density-normalised prediction at low ka . Mechanisms accounting for these variations from theory are however analysed, and include viscous attenuation effects, the polydispersity of the particle type and increasing influence of the solvent attenuation.Additionally, thermoacoustic losses appeared to dominate the attenuation behaviour of the organic latex particles. This study demonstrates that particles close to the colloidal regime can be measured successfully with acoustic backscatter, and highlights the great potential of this technique to be applied for in situ or online monitoring purposes in such systems.

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Section: Comparison Of Backscatter and Attenuation Responses Of Variosupporting

confidence: 53%

Section: Comparison Of Backscatter and Attenuation Responses Of Variomentioning

confidence: 65%

Section: Comparison Of Backscatter and Attenuation Responses Of Variomentioning

confidence: 78%

Section: Theory and Calibration Proceduresmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 95%

See 3 more Smart Citations

Measurement and density normalisation of acoustic attenuation and backscattering constants of arbitrary suspensions within the Rayleigh scattering regime

et al. 2019

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Extending acoustic in‐line pipe rheometry and friction factor modeling to low‐Reynolds‐number, non‐Newtonian slurries

et al. 2020

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The rheology of non‐Newtonian slurries is measured in a recirculating pipe loop using an acoustic velocimetry‐pressure drop technique at very low flow rates and variable solids loadings. The technique avoids (a) settling at low solids concentration, a shortcoming of bench rheometry, by using a vertical test section, and (b) physical sampling, providing greater safety. Speed of sound in the suspensions is also modeled. In‐line and off‐line data are used to assess the suitability of several non‐Newtonian models to describe observed flow behavior. Measured and predicted values of the friction factor are compared with the Madlener et al Herschel–Bulkley extended model found to be superior. The dependence of yield stress and viscosity on solids loading and particle size is investigated, showing complexities from aggregation on the particle size distribution require more interpretation than the choice of rheological or friction‐factor model.

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Using image‐based machine learning and numerical simulation to predict pesticide inline mixing uniformity

Dai

Song

et al. 2022

J Sci Food Agric

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BACKGROUND Accurate pesticide inline mixing uniformity (PIMU) evaluation for direct nozzle injection systems (DNIS) helps evaluate system performance and develop efficient inline mixers. Based on supervised machine learning (ML), inline mixing images and computational fluid dynamics (CFD) simulations are directly associated for realizing intelligent PIMU predictions. RESULTS Image sets can be reduced to less than 3% of the data size at the same time as retaining 98% of information using principal component analysis (PCA). The CFD results, as referenced values for ML, were justified by mixture sampling experiments. Enhanced images for the long‐mixing tube effectively trained models including generalized linear model (GLM), support vector regression (SVR), BP‐neural network (NNW), and classification and regression trees (CART). By testing the re‐collected images, the verification accuracy of GLM was less than 95% and it failed to recognize uniformity differences under varying working conditions, whereas NNW, CART and SVR realized it with an accuracy for NNW and CART higher than 97% and for SVR slightly lower than 97%. By testing images of the jet mixer, the prediction accuracy compared with the CFD results of NNW and CART was also higher than 97%, although that for SVR was relatively lower, and insignificant declines in accuracy were observed on comparing the results with mixture sampling experiments. CONCLUSION PCA facilitates evaluations of CFD‐referenced PIMU using image‐based ML. Models trained by enhanced image sets of the long‐mixing tube have satisfactory performance. NNW and CART performed slightly better than SVR, and they can be used as tools to improve the rationality when evaluating PIMU in DNIS. © 2022 Society of Chemical Industry.

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In situ characterization of mixing and sedimentation dynamics in an impinging jet ballast tank via acoustic backscatter

Cited by 11 publications

References 46 publications

Measurement and density normalisation of acoustic attenuation and backscattering constants of arbitrary suspensions within the Rayleigh scattering regime

Measurement and density normalisation of acoustic attenuation and backscattering constants of arbitrary suspensions within the Rayleigh scattering regime

Extending acoustic in‐line pipe rheometry and friction factor modeling to low‐Reynolds‐number, non‐Newtonian slurries

Using image‐based machine learning and numerical simulation to predict pesticide inline mixing uniformity

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