Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning

Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning

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 comp...

Full description

Bibliographic Details
Main Authors: Amir Mosavi, Shahaboddin Shamshirband, Ely Salwana, Kwok-wing Chau, Joseph H. M. Tah
Format: Article
Language:English
Published: Taylor & Francis Group 2019-01-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
machine learning
computational fluid dynamics (cfd)
hybrid model
adaptive neuro-fuzzy inference system (anfis)
artificial intelligence
big data
prediction
forecasting
optimization
hydrodynamics
fluid dynamics
soft computing
computational intelligence
computational fluid mechanics
Online Access:http://dx.doi.org/10.1080/19942060.2019.1613448

Internet

http://dx.doi.org/10.1080/19942060.2019.1613448

Similar Items