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Title: Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants
Authors: Shamshirband, S
Babanezhad, M
Mosavi, A
Nabipour, N
Hajnal, E
Nadai, L
Chau, KW 
Issue Date: 2020
Source: Engineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 367-378
Abstract: A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.
Keywords: Bubble column reactor
Ant colony optimization algorithm (ACO)
Flow pattern
Machine learning
Computational fluid dynamics (cfd)
Big data
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2020.1715842
Rights: © 2020 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
The following publication Shahaboddin Shamshirband, Meisam Babanezhad, Amir Mosavi, NarjesNabipour, Eva Hajnal, Laszlo Nadai & Kwok-Wing Chau (2020) Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biologicalants, Engineering Applications of Computational Fluid Mechanics, 14:1, 367-378 is available at
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