Artificial neural networks modeling of ozone bubble columns: Mass transfer coefficient, gas hold-up, and bubble size

Mahad S. Baawain, Mohamed Gamal El-Din, Daniel W. Smith

Research output: Contribution to journalArticle

22 Citations (Scopus)


This study aims at applying artificial neural network (ANN) modeling approach in designing ozone bubble columns. Three multi-layer perceptron (MLP) ANN models were developed to predict the overall mass transfer coefficient (kLa, s-1), the gas hold-up (εG, dimensionless), and the Sauter mean bubble diameter (dS, m) in different ozone bubble columns using simple inputs such as bubble column's geometry and operating conditions. The obtained results showed excellent prediction of kLa, εG, and dS values as the coefficient of multiple determination (R2) values for all ANN models exceeded 0.98. The ANN models were then used to determine the local mass transfer coefficient (kL, m.s-1). A very good agreement between the modeled and the measured kL values was observed (R2 = 0.85).

Original languageEnglish
Pages (from-to)343-352
Number of pages10
JournalOzone: Science and Engineering
Issue number5
Publication statusPublished - Sep 2007



  • Artificial Neural Networks
  • Bubble Columns
  • Bubble Size
  • Gas Hold-up
  • Local Mass Transfer Coefficient
  • Modeling
  • Overall Mass Transfer Coefficient
  • Ozone
  • Sauter Mean Bubble Diameter

ASJC Scopus subject areas

  • Environmental Science(all)
  • Environmental Chemistry

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