Mass transfer analysis in ozone bubble columns using artificial neural networks

M. S. Baawain; M. Gamal El-Din; D. W. Smith

Mass transfer analysis in ozone bubble columns using artificial neural networks

M. S. Baawain, M. Gamal El-Din, D. W. Smith

Civil and Architectural Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The design of ozone bubble columns is associated with accurate determination of some nonlinear parameters. The overall mass transfer coefficient (k_La) is the most important parameter as it dictates the efficiency of the bubble column. A multi-layer perceptron (MLP) artificial neural network (ANN) was used to simulate and predict the k_La in different ozone bubble columns by utilising simple inputs such as bubble column's geometry and operating conditions. The developed ANN model predicted k_La values in the training and validation data sets with a coefficient of multiple determination (R²) values that exceeded 0.87 and 0.85, respectively, which imply good model predictions.

Original language	English
Title of host publication	Proceedings of the 8th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, AICivil-Comp 2005
Publisher	Civil-Comp Press
Volume	82
ISBN (Print)	1905088051, 9781905088058
Publication status	Published - 2005
Event	8th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, AICivil-Comp 2005 - Rome, Italy Duration: Aug 30 2005 → Sept 2 2005

Other

Other	8th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, AICivil-Comp 2005
Country/Territory	Italy
City	Rome
Period	8/30/05 → 9/2/05

Keywords

Artificial neural networks
Bubble columns
Modelling
Overall mass transfer coefficient
Ozone

ASJC Scopus subject areas

Environmental Engineering
Civil and Structural Engineering
Computational Theory and Mathematics
Artificial Intelligence

Cite this

Mass transfer analysis in ozone bubble columns using artificial neural networks. / Baawain, M. S.; El-Din, M. Gamal; Smith, D. W.

Proceedings of the 8th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, AICivil-Comp 2005. Vol. 82 Civil-Comp Press, 2005.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Baawain, MS, El-Din, MG & Smith, DW 2005, Mass transfer analysis in ozone bubble columns using artificial neural networks. in Proceedings of the 8th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, AICivil-Comp 2005. vol. 82, Civil-Comp Press, 8th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, AICivil-Comp 2005, Rome, Italy, 8/30/05.

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title = "Mass transfer analysis in ozone bubble columns using artificial neural networks",

abstract = "The design of ozone bubble columns is associated with accurate determination of some nonlinear parameters. The overall mass transfer coefficient (kLa) is the most important parameter as it dictates the efficiency of the bubble column. A multi-layer perceptron (MLP) artificial neural network (ANN) was used to simulate and predict the kLa in different ozone bubble columns by utilising simple inputs such as bubble column's geometry and operating conditions. The developed ANN model predicted kLa values in the training and validation data sets with a coefficient of multiple determination (R2) values that exceeded 0.87 and 0.85, respectively, which imply good model predictions.",

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AU - El-Din, M. Gamal

AU - Smith, D. W.

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N2 - The design of ozone bubble columns is associated with accurate determination of some nonlinear parameters. The overall mass transfer coefficient (kLa) is the most important parameter as it dictates the efficiency of the bubble column. A multi-layer perceptron (MLP) artificial neural network (ANN) was used to simulate and predict the kLa in different ozone bubble columns by utilising simple inputs such as bubble column's geometry and operating conditions. The developed ANN model predicted kLa values in the training and validation data sets with a coefficient of multiple determination (R2) values that exceeded 0.87 and 0.85, respectively, which imply good model predictions.

AB - The design of ozone bubble columns is associated with accurate determination of some nonlinear parameters. The overall mass transfer coefficient (kLa) is the most important parameter as it dictates the efficiency of the bubble column. A multi-layer perceptron (MLP) artificial neural network (ANN) was used to simulate and predict the kLa in different ozone bubble columns by utilising simple inputs such as bubble column's geometry and operating conditions. The developed ANN model predicted kLa values in the training and validation data sets with a coefficient of multiple determination (R2) values that exceeded 0.87 and 0.85, respectively, which imply good model predictions.

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KW - Bubble columns

KW - Modelling

KW - Overall mass transfer coefficient

KW - Ozone

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ER -

Mass transfer analysis in ozone bubble columns using artificial neural networks

Abstract

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Keywords

ASJC Scopus subject areas

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