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In order to perceive the behavior presented by the multiphase chemicalreactors, the ant colony optimization algorithm was combined with computationalfluid dynamics (CFD) data. This intelligent algorithm creates a probabilistictechnique for computing flow and it can predict various levels ofthree-dimensional bubble column reactor (BCR). This artificial ant algorithm ismimicking real ant behavior. This method can anticipate the flowcharacteristics in the reactor using almost 30 % of the whole data in thedomain. Following discovering the suitable parameters, the method is used forpredicting the points not being simulated with CFD, which represent meshrefinement of Ant colony method. In addition, it is possible to anticipate thebubble-column reactors in the absence of numerical results or training of exactvalues of evaluated data. The major benefits include reduced computationalcosts and time savings. The results show a great agreement between ant colonyprediction and CFD outputs in different sections of the BCR. The combination ofant colony system and neural network framework can provide the smart structureto estimate biological and nature physics base phenomena. The ant colonyoptimization algorithm (ACO) framework based on ant behavior can solve alllocal mathematical answers throughout 3D bubble column reactor. The integrationof all local answers can provide the overall solution in the reactor fordifferent characteristics. This new overview of modelling can illustrate newsight into biological behavior in nature.

Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants