The use of neural network to estimate mass transfer coefficient from the bottom of agitated vessel

El-Shazly, Y. M. S.

doi:10.1007/s00231-014-1430-1

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…ElShazly 95 predicted the mass transfer coefficient of the liquid phase in a stirring tank by constructing a model of ANN based on the Levenberg-Marquardt back-propagation optimization algorithm. The neuron number of each hidden layer in the neural network varied from 1 to 12.…”

Section: Application Of Ai In Mass Transfermentioning

confidence: 99%

Application of Artificial Intelligence in Computational Fluid Dynamics

Wang

2021

Ind. Eng. Chem. Res.

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This review discusses the recent application of artificial intelligence (AI) algorithms in five aspects of computational fluid dynamics: aerodynamic models, turbulence models, some specific flows, and mass and heat transfer. Currently, there are three main coupling models. The first is the data-driven model to obtain the input−output relationship without involving any physical mechanisms. The second is the physical model to optimize the existing models by AI algorithms. The third is the hybrid model involving both data and physical mechanisms. Among various AI algorithms, artificial neural network is usually applied to build data-driven models and has been successfully employed in the mentioned five fields. Other AI algorithms such as recursive neural network, support vector machine, and naive Bayes are mainly used for the physical models. Finally, the development tendency of coupling models and how to choose an appropriate model are given in the conclusions and prospects.

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Section: Application Of Ai In Mass Transfermentioning

confidence: 99%

Application of Artificial Intelligence in Computational Fluid Dynamics

Wang

2021

Ind. Eng. Chem. Res.

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“…This model showed an outstanding performance over the predictive proposed correlations in the literature. ElShazly [ 16 ] estimated the mass transfer coefficient from the bottom of the agitated vessel and compared it with the empirical correlation expressed by Sherwood, Schmidt, and Reynolds numbers from a dimensional analysis. The author designed a network with one hidden layer and with varying neurons from 1 to 12 to optimize the structure.…”

Section: Introductionmentioning

confidence: 99%

A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach

et al. 2022

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Water transport in a hollow-fiber membrane depends on mass convection around the tube, mass convection inside the tube, and water diffusion through the membrane tube. The performance of water transport is then explained by the overall mass transfer coefficient in hollow-fiber membranes. This study presents the prediction of overall mass transfer coefficients of water transport in a hollow-fiber membrane module by an artificial neural network (ANN) that is used for a humidifier of a vehicular fuel cell system. The input variables of ANN are collected from water transport experiments of the hollow-fiber membrane module that is composed of inlet flow rates, inlet relative humidity, system pressures, and operating temperatures. The experimental mass transfer coefficients are the targets of the training model, which are determined via the effectiveness analysis. When unknown data are applied to the ANN model, the correlation of the overall mass transfer coefficient predicts precise results with R = 0.99 (correlation coefficient). The ANN model shows good prediction capability of water transport in membrane humidifiers.

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