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

Kwok-wing Chau¹  Amir Mosavi²  Joseph H. M. Tah³  Shahaboddin Shamshirband⁴  Ely Salwana⁵ 

[1]Hong Kong Polytechnic University

[2]Obuda University

[3]Oxford Brookes University

[4]Ton Duc Thang University

[5]Universiti Kebangsaan Malaysia

关键词: 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;   

DOI  :  10.1080/19942060.2019.1613448

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown