Adaptive neuro-fuzzy prediction of flow pattern and gas hold-up in bubble column reactors - Jović, Srdjan (Engineering with computers, 2021)
The prediction of fluid dynamics in multiphase bubble column reactors is a subject of major concern to appropriately design and optimize them. This paper employs the combination of computational fluid dynamics (CFD) (i.e., Euler–Euler approach) and adaptive neuro-fuzzy inference system (ANFIS) to propose new a viewpoint for multiphase modeling, including the accuracy of soft computing technique in prediction of a 3D bubble column reactor. Existing experimental, numerical and correlations results in the literature have been used to validate the implementation of the Euler–Euler approach. The results of Euler–Euler approach for a 3D bubble column reactor has been used for input training data which are liquid velocity, turbulent kinetic energy and gas hold-up. The ANFIS results have been also compared with Eulerian results, using root-mean-square error (RMSE) and coefficient of determination and Pearson coefficient. The results show that, flow pattern and gas hold-up are mainly affected by bubble column height, meaning towards sparger region, gas hold-up has a higher value near the ring sparger. According to the results, a greater improvement in estimation has been achieved through the ANFIS. Overall, the results show that ANFIS is a robust method to predict bubble column hydrodynamics parameters (e.g., liquid flow pattern and gas hold-up) as input. In addition, the exactness of the proposed ANFIS model may be boosted by considering more meteorological parameters as input values.