Simulation of a Bubble-Column Reactor by Three-Dimensional CFD: Multidimension- and Function-Adaptive Network-Based Fuzzy Inference System

Recently, novel approaches have been developed for simulating bubbly flow as well as distributed and constant phase evolution by means of a two-phase reactor. Among these approaches, the Eulerian-Eulerian method and soft computing approaches can be mentioned. Since complex numerical methods (for example, multidimensional Eulerian-Eulerian method) require several runs for fluid conditions optimization, a method which can decrease these runs can be very useful and practical. This method is provided by joining computational fluid dynamic (CFD) to the adaptive neuro-fuzzy inference system (ANFIS). In this technique, valuable information is provided for a careful analysis of fluid conditions. Also, it can facilitate a vast amount of data categorization in synthetic neural network nodes, which eliminates the need for a complex nonstructured CFD mesh. Moreover, a neural geometry can be provided, in which no limitation of mesh numbers in the fluid domain would exist. The key CFD parameters in the scale-up of the reactorstaken into consideration in the current research are gas and liquid circulations. These factors are applied as output factors for prediction tool in various dimensions in the ANFIS method. The results obtained in this study show appropriate conformity concerning ANFIS and CFD results depending on multiple dimensions. In this study, the grouping of CFD and multifunction the ANFIS method delivers the nondiscrete domain in different dimensions and presents an intelligent instrument for the local prediction of multiphase flow. The result shows that three inputs, which represent the dimension of the reactor, and learning stage of the ANFIS method provide a better understanding of flow characteristics in the two-phase reactor, while the two-dimensional ANFIS method even with multistructured functions cannot predict well the multiphase flow in the reactor.