Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81533
Title: Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning
Authors: Mosavi, A
Shamshirband, S
Salwana, E
Chau, KW 
Tah, JHM
Keywords: Adaptive neuro-fuzzy inference system (ANFIS)
Artificial intelligence
Big data
Computational fluid dynamics (CFD)
Computational fluid mechanics
Computational intelligence
Fluid dynamics
Forecasting
Hybrid model
Hydrodynamics
Machine learning
Optimization
Prediction
Soft computing
Issue Date: 2019
Publisher: Hong Kong Polytechnic University, Department of Civil and Structural Engineering
Source: Engineering applications of computational fluid mechanics, 2019, v. 13, no. 1, p. 482-492 How to cite?
Journal: Engineering applications of computational fluid mechanics 
Abstract: The combination of machine learning and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. Such numerical combination can develop a smart multiphase bubble column reactor with the ability of low-cost computational time when considering the big data. However, the accuracy of such models should be improved by optimizing the data parameters. This paper uses an adaptive-network-based fuzzy inference system (ANFIS) to train four big data inputs with a novel integration of computational fluid dynamics (CFD) model of gas. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to R = 0.99, and the number of rules during the learning process has a significant effect on the accuracy of this type of modeling. Furthermore, the proper selection of model’s parameters results in higher accuracy in the prediction of the flow characteristics in the column structure.
URI: http://hdl.handle.net/10397/81533
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2019.1613448
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Amir Mosavi, Shahaboddin Shamshirband, Ely Salwana, Kwok-wing Chau & Joseph H. M. Tah (2019) Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning, Engineering Applications of Computational Fluid Mechanics, 13:1, 482-492, is available at https://doi.org/10.1080/19942060.2019.1613448
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