Fluid Velocity Prediction Inside Bubble Column Reactor Using ANFIS Algorithm Based on CFD Input Data

Since machine learning and smart methods can be used to study hydrodynamics in the bubble column reactor, it is possible to create highly intelligent bubble column reactors that have not been previously simulated and optimized them with computational fluid dynamics (CFD) methods. The previous studies considered the position of each node (in three directions) inside the bubble column reactor as the input in the artificial intelligence model. Machine learning methods have been used for processing big data related to the bubble column reactor. These big data are associated with the points inside the bubble column reactor, which represent the gas volume fraction and the fluid velocity in the x-direction. In this study, adaptive-network-based fuzzy inference system (ANFIS) was used to find out the relationship between the outputs of the bubble column reactor. The present study also intends to investigate the relationship between two outputs, namely the amount of gas in the bubble column reactor and the velocity of the fluid in the x-direction. Various parameters were investigated in this system, including the number of rules, the type of membership function, and the amount of input data. The mentioned parameters were regularly changed to find out the state where the system can achieve its intelligence. In this study, the best parameter that helped the system was the amount of data in the training process. The results showed that the lower the amount of data used in training, the better the prediction.