An analysis of a sequencing batch reactor (SBR) from a fluid mechanical point of view, used in the cultivation of granular activated sludge (GAS), is carried out by experimental, computational fluid dynamics (CFD) and artificial neuronal networks (ANN) methods. Due to the complexity of the three-phase problem, new hybrid methods are developed in order to shorten the calculation time and to improve the numerical prediction. In the numeroexperimental hybrid, experimentally obtained velocities from particle image velocimetry (PIV) method are implemented as initial conditions for the numerical simulation. This operation causes improvement of multiphase flow results and save CPU time of about 40% in comparison with the standard calculation. In the neuronumerical hybrid, numerically obtained results and process parameters are employed for training of an ANN. With the trained ANN, several geometrical and physical entities are calculated for a range of SBRs. In this case, the acceleration, in the prediction of several process parameters, reaches the factor 1.0E + 05. (c) 2007 Elsevier Ltd. All rights reserved.