Prediction of Bubble Sizes in Bubble Columns with Machine Learning Methods

Biessey, Philip; Bayer, Hakan; Theßeling, Christin; Hilbrands, Eske; Grünewald, Marcus

doi:10.1002/cite.202100157

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…Cross-validation is a common way of testing potential k-values to determine the best choice of k for a given dataset (Biessey et al, 2021). The optimal k-value was determined through cross-validation using an Frontiers in Bioengineering and Biotechnology frontiersin.org adapted holdout technique.…”

Section: Knn Cross-validationmentioning

confidence: 99%

Predicting shock-induced cavitation using machine learning: implications for blast-injury models

Marsh,

Zinnel,

Bentil

2024

Front. Bioeng. Biotechnol.

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While cavitation has been suspected as a mechanism of blast-induced traumatic brain injury (bTBI) for a number of years, this phenomenon remains difficult to study due to the current inability to measure cavitation in vivo. Therefore, numerical simulations are often implemented to study cavitation in the brain and surrounding fluids after blast exposure. However, these simulations need to be validated with the results from cavitation experiments. Machine learning algorithms have not generally been applied to study blast injury or biological cavitation models. However, such algorithms have concrete measures for optimization using fewer parameters than those of finite element or fluid dynamics models. Thus, machine learning algorithms are a viable option for predicting cavitation behavior from experiments and numerical simulations. This paper compares the ability of two machine learning algorithms, k-nearest neighbor (kNN) and support vector machine (SVM), to predict shock-induced cavitation behavior. The machine learning models were trained and validated with experimental data from a three-dimensional shock tube model, and it has been shown that the algorithms could predict the number of cavitation bubbles produced at a given temperature with good accuracy. This study demonstrates the potential utility of machine learning in studying shock-induced cavitation for applications in blast injury research.

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Section: Knn Cross-validationmentioning

confidence: 99%

Predicting shock-induced cavitation using machine learning: implications for blast-injury models

Marsh,

Zinnel,

Bentil

2024

Front. Bioeng. Biotechnol.

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“…97 Classic ML methods were successfully applied to determine the size of bubbles in real column reactors. 98,99 The discussed approaches require a relatively large dataset of input images. To solve the small dataset problem, Fu and Liu used generative adversarial networks to create a BubGAN model.…”

Section: Cavitation Bubbles' Analysismentioning

confidence: 99%

“…97 Classic ML methods were successfully applied to determine the size of bubbles in real column reactors. 98,99…”

Section: Machine Learning For Bubblesmentioning

confidence: 99%

Machine learning for soft and liquid molecular materials

et al. 2023

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“…Theßeling et al 48 used LASSO and tree-based regression to predict bubble diameter. Biessey et al 49 have used a similar approach to determine bubble size in a bubble column.…”

Section: Previous Workmentioning

confidence: 99%

Correlating Interfacial Area and Volumetric Mass Transfer Coefficient in Bubble Column with the Help of Machine Learning Methods

Hazare

Vala²,

Patil

et al. 2022

Ind. Eng. Chem. Res.

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Several empirical correlations are available to estimate the volumetric mass transfer coefficient and effective interfacial area for bubble column reactors. But these empirical correlations are applicable over the range of experimental conditions. By considering the broad range of parameters in a database, data-driven machine-learning methods can be used to correlate the design parameters. In this work, a generalized machine learning-based methodology is presented to calculate the volumetric mass transfer coefficient and effective interfacial area with independent parameters. Machine learning methods such as support vector regression (SVR), random forest (RF), extra trees (EXT), and artificial neural networks (ANN) have been used. An extensive set of experimental data points (1245 data points for volumetric mass transfer coefficient and 526 for interfacial area) have been extracted from the literature. The predictors, column diameter, column height, sparger design, sparger location, percentage free area, superficial gas and liquid velocity, pressure, temperature, density of gas and liquid, viscosity of gas and liquid, and diffusion coefficient, have been used to calculate design parameters. The performance of the machine learning methods has been compared using statistical parameters: mean absolute percentage error (MAPE), mean square error (MSE), and determination coefficient/ prediction accuracy (R-square). Statistical analysis shows that the predictive ability of machine learning methods is better than that of traditional regression methods. The extra tree predictions were accurate compared to other methods, and the statistical parameters by extra trees were found to be the best among the methods. For the volumetric mass transfer coefficient, R-square = 0.98, MSE = 2 × 10 −4 , and MAPE = 15.67, and for the effective interfacial area, R-square = 0.98 and MAPE = 12.04.

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Prediction of Bubble Sizes in Bubble Columns with Machine Learning Methods

Cited by 10 publications

References 29 publications

Predicting shock-induced cavitation using machine learning: implications for blast-injury models

Predicting shock-induced cavitation using machine learning: implications for blast-injury models

Machine learning for soft and liquid molecular materials

Correlating Interfacial Area and Volumetric Mass Transfer Coefficient in Bubble Column with the Help of Machine Learning Methods

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