Design and scale-up methodology for multi-phase reactors based on non-linear dynamics

Multi-phase reactors exhibit chaotic behaviors due to the highly turbulent motions of bubbles and phase interactions, leading to the formation of a co mplex spatio-temporal flow structure. The instantaneous motions of bubbles and wakes were studied by local measurements of bubble and particle-velocit y fluctuations, bubble-wake structure, bubble shape and orientation. The ch aotic dynamics of bubble and particle motions in multi-phase reactors were characterized in terms of the correlation dimension obtained by the determi nistic chaos analysis for the series of time intervals between successive b ubbles or particles by means of the embedding method. Three different metho ds, the deterministic chaos analysis, the short-term predictability analysi s and the rescaled range (R/S) analysis, were applied to the non-linear dyn amics of multi-phase reactors. The scale-up effect in the dynamic behavior of multi-phase reactors was investigated by using columns of different diam eters. In addition, the non-linear hydrodynamic motions of bubbles and part icles in multi-phase reactors have been modeled by means of an ANN (Artific ial Neural Network) trained with time series data of voidage fluctuations f or gas and solid phases. By successively adapting its output to input, the ANN can generate time-series data for any superficial gas velocity. The bif urcation diagrams of both bubble and particle motions generated by the trai ned ANN demonstrated that the ANN is capable of predicting and modeling the chaotic dynamics of multi-phase reactors. (C) 2000 Elsevier Science Ltd. A ll rights reserved.