Long-term prediction of nonlinear hydrodynamics in bubble columns by using artificial neural networks

Abstract

An artificial neural network (ANN) model was proposed for the long-term prediction of nonlinear dynamics underlying holdup fluctuations in bubble columns with three different diameters of 200, 400 and 800 mm. Local holdup fluctuations were measured with an optical probe in the bubble columns. The superficial gas velocity was varied in the range of 33–90 mm/s. The time intervals between successive bubbles were extracted from the time series of holdup fluctuations to represent hydrodynamic behaviors in the system and used as training and validation data sets. The effect of data preprocessing as well as the numbers of nodes in input and hidden layers on the ANN training behavior was systematically investigated. The prediction capability of the ANN was evaluated in terms of time-averaged characteristics, power spectra and Lyapunov exponents. It was observed that: the ANN model, which was trained with experimental time series and gas velocity, can be used for the long-term prediction of dynamic characteristics in bubble columns by using random data as the initial input. The results indicate that the trained ANN models have the potential of modeling nonlinear hydrodynamic behaviors in bubble columns.