Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS

This work presents a combination of Computational Fluid Dynamics (CFD) and Adaptive Network-based Fuzzy Inference System (ANFIS) developed for flow characterization inside a cylindrical bubble column reactor. An attempt has been made to predict the liquid flow pattern and gas dynamics for various ring sparger diameters (i.e., 0.07-0.16 m) and bubble column heights. Gas hold-up, Turbulent Kinetic Energy (TKE) and axial liquid velocity are the output parameters predicted by using the ANFIS method with respect to sparger diameter, axial coordination and radial coordination. Various architectures of the ANFIS method were constructed in order to achieve an accurate prediction model of the liquid flow behavior and gas dynamics inside the bubble column. ANFIS approaches were trained and tested by using CFD simulation results. The performance of the ANFIS approaches was examined by comparing the root mean square error and correlation coefficient values of the prediction models. The CFD simulation results are validated with existing experimental and numerical data and mathematical correlations. Both CFD simulation and ANFIS prediction results show that ring sparger diameter significantly changes the liquid flow pattern and gas dynamics, resulting in different amounts of the gas inside the column. Different ANFIS structures were selected for precise estimation of gas hold-up, TKE and axial liquid velocity. Eventually, the mathematical correlations of the proposed ANFIS approaches are presented with correlation coefficients of 0.9717, 0.9917 and 0.9877 for gas hold-up, turbulent kinetic energy and axial liquid velocity prediction models. Hence, the ANFIS approach is able to provide a prediction of the 3D bubble column hydrodynamics in a continuous domain.