Artificial Neural Networks Modeling of Ozone Bubble Columns: Mass Transfer Coefficient,Gas Hold-Up,and Bubble Size

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 (k_La, s^?1), the gas hold-up (? _G , dimensionless), and the Sauter mean bubble diameter (d_S , 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 k_La, ? _G , and d_S 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 (k_L , m.s^?1). A very good agreement between the modeled and the measured k_L values was observed (R² ?=?0.85).