Analysis of Hydrodynamics of External Loop Circulating Bubble Columns with Open Channel Gas Separators Using Neural Networks

Gas holdups and liquid circulation velocities in two external loop circulating bubble columns of the open channel gas separators using air–water and air–glycerol systems were extensively reported by Al-Masry (1999, 2004). The effects of changing the volume of the liquid in the gas–liquid separators on the columns hydrodynamics were analysed numerically using neural network with four inputs and three outputs. The inputs were superficial gas velocity UGR, volume ratio TVR, liquid viscosity μL and scale-up factor AD/AR, while the outputs were liquid circulation velocity ULR, riser gas holdup ɛGR and downcomer gas holdup ɛGD. The network was trained on 60% of the data, and then used to predict 40% of the data that have never been seen by the network. The training was successfully accomplished and results obtained with average normalized square error <0.01. Comparison of the neural network predictions of the hydrodynamics variables with predictions of Al-Masry (2004) gave much better improvement. The results show that neural networks, if properly designed, are very powerful predicting mathematical tools that can accurately approximate nonlinear input–output mappings.