• Media type: E-Article
  • Title: Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants
  • Contributor: Shamshirband, Shahaboddin [Author]; Babanezhad, Meisam [Author]; Mosavi, Amir [Author]; Nabipour, Narjes [Author]; Hajnal, Eva [Author]; Nadai, Laszlo [Author]; Chau, Kwok-Wing [Author]
  • imprint: Publication Server of Weimar Bauhaus-University / Online-Publikations-System der Bauhaus-Universität Weimar, 2020-02-03
  • Language: English
  • Keywords: big data ; Bubble column reactor ; flow pattern ; ant colony optimization algorithm (ACO) ; computational fluid dynamics (CFD) ; OA-Publikationsfonds2020 ; bk:54 ; Maschinelles Lernen ; Machine learning
  • Origination:
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  • Description: 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.
  • Access State: Open Access