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Intelligent algorithms are recently used in the optimization process inchemical engineering and application of multiphase flows such as bubbling flow.This overview of modeling can be a great replacement with complex numericalmethods or very time-consuming and disruptive measurement experimental process.In this study, we develop the adaptive network-based fuzzy inference system(ANFIS) method for mapping inputs and outputs together and understand thebehavior of the fluid flow from other output parameters of the bubble columnreactor. Neural cells can fully learn the process in their memory and after thetraining stage, the fuzzy structure predicts the multiphase flow data. Fourinputs such as x coordinate, y coordinate, z coordinate, and air superficialvelocity and one output such as pressure gradient are considered in thelearning process of the ANFIS method. During the learning process, thedifferent number of the membership function, type of membership functions andthe number of inputs are examined to achieve the intelligent algorithm withhigh accuracy. The results show that as the number of inputs increases theaccuracy of the ANFIS method rises up to R^2>0.99 almost for all cases, whilethe increment in the number of rules has a effect on the intelligence ofartificial algorithm. This finding shows that the density of neural objects orhigher input parameters enables the moded for better understanding. We alsoproposed a new evaluation of data in the bubble column reactor by mappinginputs and outputs and shuffle all parameters together to understand thebehaviour of the multiphase flow as a function of either inputs or outputs.This new process of mapping inputs and outputs data provides a framework tofully understand the flow in the fluid domain in a short time of fuzzystructure calculation.

Sensitivity study of ANFIS model parameters to predict the pressure  gradient with combined input and outputs hydrodynamics parameters in the  bubble column reactor