Preprint Article Version 2 This version is not peer-reviewed

Prediction of Flow Characteristics in the Bubble Column Reactor by the Artificial Pheromone-Based Communication of Biological Ants

Version 1 : Received: 30 April 2019 / Approved: 5 May 2019 / Online: 5 May 2019 (12:59:23 CEST)
Version 2 : Received: 9 January 2020 / Approved: 11 January 2020 / Online: 11 January 2020 (12:47:48 CET)

How to cite: Shamshirband, S.; Babanezhad, M.; Mosavi, A.; Nabipour, N.; Hajnal, E.; Nadai, L.; Chau, K. Prediction of Flow Characteristics in the Bubble Column Reactor by the Artificial Pheromone-Based Communication of Biological Ants. Preprints 2019, 2019050025 (doi: 10.20944/preprints201905.0025.v2). Shamshirband, S.; Babanezhad, M.; Mosavi, A.; Nabipour, N.; Hajnal, E.; Nadai, L.; Chau, K. Prediction of Flow Characteristics in the Bubble Column Reactor by the Artificial Pheromone-Based Communication of Biological Ants. Preprints 2019, 2019050025 (doi: 10.20944/preprints201905.0025.v2).

Abstract

In order to perceive the behavior presented by the multiphase chemical reactors, the ant colony optimization algorithm was combined with computational fluid dynamics (CFD) data. This intelligent algorithm creates a probabilistic technique for computing flow and it can predict various levels of three-dimensional bubble column reactor (BCR). This artificial ant algorithm is mimicking real ant behavior. This method can anticipate the flow characteristics in the reactor using almost 30 % of the whole data in the domain. Following discovering the suitable parameters, the method is used for predicting the points not being simulated with CFD, which represent mesh refinement of Ant colony method. In addition, it is possible to anticipate the bubble-column reactors in the absence of numerical results or training of exact values of evaluated data. The major benefits include reduced computational costs and time savings. The results show a great agreement between ant colony prediction and CFD outputs in different sections of the BCR. The combination of ant colony system and neural network framework can provide the smart structure to estimate biological and nature physics base phenomena. The ant colony optimization algorithm (ACO) framework based on ant behavior can solve all local mathematical answers throughout 3D bubble column reactor. The integration of all local answers can provide the overall solution in the reactor for different characteristics. This new overview of modelling can illustrate new sight into biological behavior in nature.

Subject Areas

bubble column reactor; ant colony optimization algorithm (ACO); flow pattern; machine learning; computational fluid dynamics (CFD); big data

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