Local gas holdup and mass transfer in a bubble column using an ultrasonic technique and a neural network

The gas holdup and the volumetric mass transfer coefficient are very important parameters for mass transfer operation in bubble columns. These parameters have been major subjects for many researches for a long time. In this study, a novel technique of ultrasonics and neural network has been proposed to determine the local gas holdup and the volumetric mass transfer coefficient in a two-dimensional bubble column simultaneously. The measurement parameters of the energy attenuation and the time shift difference of ultrasound are used to obtain the local gas holdup and the volumetric mass transfer coefficient in dispersed systems of air-water and air-glycerol solution using the neural network reconstruction. In the range studied, the representative results of the proposed technique show a good agreement with the experimental data.