ONLINE FLOW VISUALIZATION IN MULTIPHASE REACTORS USING NEURAL NETWORKS

The success of the radioactive particle tracking system (RPT) develope d at the Ecole Polytechnique (Montreal) and applied to the study of pa rticle motion in a variety of chemical reactors (three-phase fluidized bed, gas spouted bed and liquid fluidized bed) has motivated us to co ntinue improving this technique (in terms of accuracy and resolution) and to apply it to new reactor types. Our goals are: (i) to enhance th e original search location algorithm in Order to permit on-line flow v isualization and (ii) to extend RPT to very fast solids flows, such as those encountered in circulating fluidized beds (particle velocities higher than a few m s(-1)). The potential of neural networks for on-li ne and real-time visualization of particle movements in multiphase rea ctors will be illustrated. The original least-squares search location algorithm (Larachi et al., 1994) has been replaced with an enhanced al gorithm which uses a three-layer feedforward neural network. The resul ts obtained from the two algorithms for particle tracking in a three-p hase fluidized bed reactor are compared. The RPT system employs 8 NaI( TI) scintillation detectors to study the movement of solid particles i n chemical reactors. The performance of the system was investigated us ing particles containing the radioisotopes Sc-46 (gamma-ray energy 100 5 keV), Mo-99 (gamma-ray energy 140 keV) and Au-198 (gamma-ray energy 412 keV). The three-dimensional spatial resolution was measured in emp ty and water-filled tubes, simulating highly diluted and dense media. The best results were obtained using Au-198 With which the particle ca n be located to within 7 mm in water and 9 mm in air 100 times s(-1). Copyright (C) 1997 Elsevier Science Ltd