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

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 (ε G , 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, ε G , 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).