Determination of bubble size distribution in a bubble column reactor using artificial neural network

Amiri, Sahar; Mehrnia, Mohammad Reza; Barzegari, Davood; Yazdani, Aryan

doi:10.1002/apj.615

Cited by 6 publications

(2 citation statements)

References 25 publications

(30 reference statements)

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“…Amiri et al used gas velocity, kinematic viscosity, density, and height as the input parameters to the artificial neural network model to predict bubble size distribution. Similarly, Amiri et al developed a neural network model to predict gas hold-up with the same input parameters as Amiri et al Chidambaram et al studied the trajectory of a bubble by integrating a neural network with image processing technology, and superficial fluid velocities, time, and nozzle diameter were the input parameters. Manjrekar and Dudukovic combined optical probe data with support vector machines to identify the flow regime.…”

Section: Previous Workmentioning

confidence: 99%

“…The parameters were studied using genetic-support vector regression to predict the volumetric mass transfer coefficient, overall gas hold-up, and effective interfacial area. Amiri et al 44 used gas velocity, kinematic viscosity, density, and height as the input parameters to the artificial neural network model to predict bubble size distribution. Similarly, Amiri et al 45 developed a neural network model to predict gas hold-up with the same input parameters as Amiri et al 44 Chidambaram et al 46 studied the trajectory of a bubble by integrating a neural network with image processing technology, and superficial fluid velocities, time, and nozzle diameter were the input parameters.…”

Section: Previous Workmentioning

confidence: 99%

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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: Previous Workmentioning

confidence: 99%

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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A data‐driven approach for flow corrosion characteristic parameters prediction in an air cooler

Jin

Xiang-Yao

Liu

et al. 2020

Asia-Pacific J Chem Eng

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A data‐driven soft measurement method based on a multiunit back propagation neural network (MBPNN) is presented in this study. This model aims to estimate the characteristic parameters that can reflect the flow corrosion of the reactor effluent air cooler (REAC). Flow corrosion failure during the hydrogenation process presents a serious concern to the petrochemical industry. In this paper, a safety evaluation of flow corrosion failure for a petrochemical diesel hydrogenation unit is first carried out. During the investigation, it is found that there is a risk of NH4Cl crystallization at around 187°C. Then, considering flow‐induced corrosion and ammonium salt deposition, main characteristic parameters are determined, including NH4Cl crystallization temperature (TC), air cooler tube bundle minimum and maximum flow rate (Vmin and Vmax), air cooler inlet liquid water content (CW), and NH4HS concentration (CA). Finally, the data‐driven model based on a multiunit back propagation neural network (MBPNN) is constructed. An improved particle swarm optimization (PSO) approach is employed to initialize the main parameters of the model. Compared with a multioutput back propagation neural network (BPNN) model and MBPNN model without an optimization algorithm, the presented data‐driven model is proved to have high accuracy, a fast convergence rate, and high reliability.

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An artificial neural network toward simulating the treatment of wastes in multiphase reactors by ozonation

Kalari

Christodoulis

Bali

et al. 2023

Chemical Engineering Journal

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Determination of bubble size distribution in a bubble column reactor using artificial neural network

Cited by 6 publications

References 25 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

A data‐driven approach for flow corrosion characteristic parameters prediction in an air cooler

An artificial neural network toward simulating the treatment of wastes in multiphase reactors by ozonation

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