Predictive analysis of gas hold-up in bubble column using machine learning methods

Hazare, Sumit R.; Patil, Chinmay S.; Vala, Shivam V.; Joshi, Anirudha J.; Joshi, Jyeshtharaj B.; Vitankar, Vivek; Patwardhan, Ashwin W.

doi:10.1016/j.cherd.2022.06.007

Cited by 10 publications

(10 citation statements)

References 112 publications

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“…The predictor with the higher impact becomes the most important feature and the one with a lower impact becomes the least important feature. The feature importance methodology explained and followed by Hazare et al 50 has been used to find important features for volumetric mass transfer coefficient and interfacial area. For the feature importance study, training data sets were used.…”

Section: Methodsmentioning

confidence: 99%

“…The models optimize the tube such that the prediction error is minimal and relationship of target with predictors is maintained. Hazare et al 50 have explained a detailed stepwise procedure to predict overall gas hold-up in a bubble column. The same approach can correlate the volumetric mass transfer coefficient and interfacial area.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Methodsmentioning

confidence: 99%

Section: Support Vector Regression (Svr)mentioning

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.

Self Cite

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“…An interesting example of machine learning applied to capture the behavior of complex systems has been published by Hazare and co-workers, who predicted the gas holdup in bubble columns and reviewed other efforts in the same area. However, their work does not provide human interpretable insights into the phenomena being modeled.…”

Section: Literature Reviewmentioning

confidence: 99%

“…25 They have thoroughly reviewed, investigated, analyzed, and talked about significant developments in recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from many angles: (i) to increase the precision and effectiveness of typical CFD simulations, multiphase closure models of drag force, turbulence stresses, and heat/mass transfer; (ii) other CFD simulation techniques include image reconstruction, regime identification, key parameter predictions, and multiphase flow and transport field optimization; (iii) reaction kinetic modeling, which includes the prediction of reaction networks, kinetic parameters, and species production, is being used as well as optimization of reaction conditions. 25 An interesting example of machine learning applied to capture the behavior of complex systems has been published by Hazare and co-workers, 26 who predicted the gas holdup in bubble columns and reviewed other efforts in the same area. However, their work does not provide human interpretable insights into the phenomena being modeled.…”

Section: Literature On Machine Learning Applications In Multiphase Re...mentioning

confidence: 99%

Novel Correlation for Critical Speed for Solid Suspension in Stirred Tanks Developed Using Machine Learning Models Trained on Literature Data

Joshi

Dalvi

Vitankar³

et al. 2023

Ind. Eng. Chem. Res.

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Critical speed for solid suspension (N js ) in stirred tanks is an important design parameter in several chemical processes. There is a need to develop a generalized correlation that applies to a broad range of literature data. Also, the literature lacks a comparative study of different machine learning (ML) models for the prediction of N js . In this paper, 3240 data points have been extracted from 35 papers on solid suspension and N js has been modeled as the dependent variable, initially using ML models. Three ML models (random forest regression (RF), CatBoost regression (CatB), and artificial neural network regression (ANN)) are compared. The CatB model was the most effective, resulting in R 2 = 0.99 for the testing dataset, better than any empirical correlation published. Further, we have successfully used the CatB model to obtain the functional form of the behavior of various parameters. We then developed novel correlations (in closed form and with parametric uncertainty) for HE-3, PTD, and DT impellers with R 2 = 0.89, 0.84, and 0.86, respectively, where the correlation constants were tuned using experimental data published in the literature. Our closed-form correlations significantly outperform earlier correlations published in the literature. Such a methodology has not been reported in the literature we have surveyed, and we believe that it is novel and original.

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“…27 In the field of multiphase flow, machine learning is equally compelling. There is much research on gas hold-up prediction in a bubble column, and some of it is based on machine learning, Based on an ANN model, a useful prediction model had been developed by Hazare et al 28 He et al also achieved a reliable prediction of bubble departure frequency in subcooled flow boiling by X-G boost. 29 In the region of microflows, Su et al constructed a neural network (NN) to predict the inertial lift in microchannels.…”

Section: Introductionmentioning

confidence: 99%

A general neural network model co-driven by mechanism and data for the reliable design of gas–liquid T-junction microdevices

Chang,

Sheng,

Wang

et al. 2023

Lab Chip

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Predictive analysis of gas hold-up in bubble column using machine learning methods

Cited by 10 publications

References 112 publications

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

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

Novel Correlation for Critical Speed for Solid Suspension in Stirred Tanks Developed Using Machine Learning Models Trained on Literature Data

A general neural network model co-driven by mechanism and data for the reliable design of gas–liquid T-junction microdevices

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