An artificial neural network for prediction of gas holdup in bubble columns with oily solutions

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

doi:10.1007/s00521-011-0566-x

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…The ANN was derived for the following ranges of the independent variables: 0.003 < U G < 0.18 m/s, 55 < σ < 74.2 mN m 2 , 0.115 < d o < 6 mm and 3.94 < K f < 76.9. Since there is no theoretical method to determine the optimal structure of the ANN models, trial-and-error methodology was adopted [24]. The number of neurons in the hidden layer was varied until a good prediction of trained data was achieved.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Volumetric gas-liquid mass transfer coefficient in an external-loop airlift reactor with inserted membrane

Kojić

Šijački

Lukić

et al. 2016

CI&CEQ

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The effects of the inserted membrane in the downcomer of an external-loop airlift reactor, the gas sparger type (single orifice and sinter plate) and added alcohol (ethanol, n-butanol, or n-hexanol) on the volumetric gas-liquid mass transfer coefficient (kLa) were studied. Due to the presence of the membrane in the downcomer, kLa did not change significantly; the differences were smaller than 10%. The highest values of the kLa were obtained using the sinter plate. It was found that the addition of small amounts of alcohol increased the mass transfer. Using our experimental results and the data of other authors, the feed-forward back propagation neural network for prediction of kLa in external-loop airlift reactors with alcohol solutions was proposed. [Projekat Ministarstva nauke Republike Srbije, br. 172025]

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Section: Artificial Neural Networkmentioning

confidence: 99%

“…These results suggested further development of the artificial neural network model (ANN) that would better predict the k L a values. The ANN appeared to be a good alternative to empirical correlations [23][24][25]. The ANNs are more powerful and can manipulate non-linear relationships more successfully than available literature correlations [25].…”

mentioning

confidence: 99%

Volumetric gas-liquid mass transfer coefficient in an external-loop airlift reactor with inserted membrane

Kojić

Šijački

Lukić

et al. 2016

CI&CEQ

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“…Bioethanol contains 34% oxygen 14. Furthermore, its low cetane number compared to that of diesel fuel increases the peak temperature in the cylinder (Amiri et al, 2011). For this reason, the concentration of NOx emissions increased with the use of all types of test fuels containing bioethanol.…”

Section: Resultsmentioning

confidence: 99%

Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

Aydoğan

2017

J. energy South. Afr.

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The changes in the performance, emission and combustion characteristics of bioethanol-safflower biodiesel and diesel fuel blends used in a common rail diesel engine were investigated in this experimental study. E20B20D60 (20% bioethanol, 20% biodiesel, 60% diesel fuel by volume), E30B20D50, E50B20D30 and diesel fuel (D) were used as fuel. Engine power, torque, brake specific fuel consumption, NOx and cylinder inner pressure values were measured during the experiment. With the help of the obtained experimental data, an artificial neural network was created in MATLAB 2013a software by using back-propagation algorithm. Using the experimental data, predictions were made in the created artificial neural network. As a result of the study, the correlation coefficient was found as 0.98. In conclusion, it was seen that artificial neural networks approach could be used for predicting performance and emission values in internal combustion engines.

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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%

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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An artificial neural network for prediction of gas holdup in bubble columns with oily solutions

Cited by 16 publications

References 23 publications

Volumetric gas-liquid mass transfer coefficient in an external-loop airlift reactor with inserted membrane

Volumetric gas-liquid mass transfer coefficient in an external-loop airlift reactor with inserted membrane

Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

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

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