  Intelligent algorithms are recently used in the optimization process in
chemical engineering and application of multiphase flows such as bubbling flow.
This overview of modeling can be a great replacement with complex numerical
methods 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 the
behavior of the fluid flow from other output parameters of the bubble column
reactor. Neural cells can fully learn the process in their memory and after the
training stage, the fuzzy structure predicts the multiphase flow data. Four
inputs such as x coordinate, y coordinate, z coordinate, and air superficial
velocity and one output such as pressure gradient are considered in the
learning process of the ANFIS method. During the learning process, the
different number of the membership function, type of membership functions and
the number of inputs are examined to achieve the intelligent algorithm with
high accuracy. The results show that as the number of inputs increases the
accuracy of the ANFIS method rises up to R^2>0.99 almost for all cases, while
the increment in the number of rules has a effect on the intelligence of
artificial algorithm. This finding shows that the density of neural objects or
higher input parameters enables the moded for better understanding. We also
proposed a new evaluation of data in the bubble column reactor by mapping
inputs and outputs and shuffle all parameters together to understand the
behaviour of the multiphase flow as a function of either inputs or outputs.
This new process of mapping inputs and outputs data provides a framework to
fully understand the flow in the fluid domain in a short time of fuzzy
structure calculation.
