Abstract
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 language | English |
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Pages (from-to) | 343-352 |
Number of pages | 10 |
Journal | Ozone: Science and Engineering |
Volume | 29 |
Issue number | 5 |
DOIs | |
Publication status | Published - Sept 2007 |
Keywords
- 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 Engineering
- Environmental Chemistry