Adaptive neuro-fuzzy prediction of flow pattern and gas hold-up in bubble column reactors

• Published in: Engineering with computers Vol. 37; no. 3; pp. 1723 - 1734
• Main Author: Jović, Srdjan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2021; Springer Nature B.V
• Subjects: Adaptive systems; Artificial neural networks; Bubble columns; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Computational fluid dynamics; Computer Science; Computer-Aided Engineering (CAD; Control; Design optimization; Flow distribution; Fluid dynamics; Fluid flow; Fuzzy logic; Hydrodynamics; Kinetic energy; Liquid flow; Math. Applications in Chemistry; Mathematical and Computational Engineering; Mathematical models; Meteorological parameters; Model accuracy; Multiphase; Original Article; Reactors; Robustness (mathematics); Root-mean-square errors; Soft computing; Systems Theory; CFD; Bubble column reactor; ANFIS; Soft computing; Multiphase; Estimation
• ISSN: 0177-0667; 1435-5663
• DOI: 10.1007/s00366-019-00905-y

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.