ANFIS algorithm for mapping computational data of water reservoir homogenization with air bubble flows

Air as an inert gas is usually applied for homogenization and mixing liquids. In the current research, we study a 3-D bubble column reactor (BCR) filled with water by using an Artificial intelligence algorithm (AI) and CFD. We used one of the adaptive networks and fuzzy inference systems (ANFIS) to study fluid flow and see its effect on the accuracy of the AI. Therefore, the Gaussian membership function was used to have a prediction in the 3-D BCR. Also, the grid partition system was used to cluster the data. The number of membership functions increases in the training process of the AI system, from 2 to 5. The influence of input numbers on AI data prediction is analyzed. The four inputs in the training process included air velocity and pressure, as well as the x-direction and z-direction. Finally, air vorticity was considered as the output parameter of the study in the predictions. Correlations were developed to predict the air vorticity in each node using x and z direction, air velocity, and pressure. The results showed the AI accuracy increased by the rise of membership and input numbers. The AI intelligence level was found by five memberships and four inputs. The AI and CFD were in suitable agreement (regression number around 1). The developed correlations could simplify the calculation of air vorticity instead of using the complicated and time-consuming CFD simulation. As far as the authors know, there are no studies that have developed correlations to find the air vorticity in bubble column reactors.