Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor

Abstract For understanding the complex behavior of fluids in a multiphase chemical bubble column reactor, a combination of the computational fluid dynamic (CFD) method and the adaptive network-based fuzzy inference system (ANFIS) method is used to predict bubble flow inside a reactor based on the fu...

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Autores principales:	Meisam Babanezhad, Azam Marjani, Saeed Shirazian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f12a5b5b2f044ac6bfbc693205cdfa3e
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spelling	oai:doaj.org-article:f12a5b5b2f044ac6bfbc693205cdfa3e2021-12-02T16:18:06ZMultidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor10.1038/s41598-020-78388-x2045-2322https://doaj.org/article/f12a5b5b2f044ac6bfbc693205cdfa3e2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78388-xhttps://doaj.org/toc/2045-2322Abstract For understanding the complex behavior of fluids in a multiphase chemical bubble column reactor, a combination of the computational fluid dynamic (CFD) method and the adaptive network-based fuzzy inference system (ANFIS) method is used to predict bubble flow inside a reactor based on the function of column height. In this study, the Euler–Euler model is employed as a CFD method. In the Eulerian method, continuity and momentum governing equations are mathematically computed for each phase, while the equations are connected together by source terms. After calculating the flow pattern and turbulence flow in the reactor, all data sets are used to prepare a fully artificial method for further prediction. This algorithm contains different learning dimensions such as learning in different directions of reactor or large amount of input parameters and data set representing “big data”. The ANFIS method was evaluated in three steps by using one, two, and three inputs in each one to predict the liquid velocity in the x-direction (Ux). The x, y, and z coordinates of the location of the node of the liquid were considered as the inputs. Different percentages of data and various iterations and membership functions were used for training in the ANFIS method. The ANFIS method showed the best prediction using three inputs. This combination also shows the ability of computer science and computational methods in learning physical and chemical phenomena.Meisam BabanezhadAzam MarjaniSaeed ShirazianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Meisam Babanezhad Azam Marjani Saeed Shirazian Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor
description	Abstract For understanding the complex behavior of fluids in a multiphase chemical bubble column reactor, a combination of the computational fluid dynamic (CFD) method and the adaptive network-based fuzzy inference system (ANFIS) method is used to predict bubble flow inside a reactor based on the function of column height. In this study, the Euler–Euler model is employed as a CFD method. In the Eulerian method, continuity and momentum governing equations are mathematically computed for each phase, while the equations are connected together by source terms. After calculating the flow pattern and turbulence flow in the reactor, all data sets are used to prepare a fully artificial method for further prediction. This algorithm contains different learning dimensions such as learning in different directions of reactor or large amount of input parameters and data set representing “big data”. The ANFIS method was evaluated in three steps by using one, two, and three inputs in each one to predict the liquid velocity in the x-direction (Ux). The x, y, and z coordinates of the location of the node of the liquid were considered as the inputs. Different percentages of data and various iterations and membership functions were used for training in the ANFIS method. The ANFIS method showed the best prediction using three inputs. This combination also shows the ability of computer science and computational methods in learning physical and chemical phenomena.
format	article
author	Meisam Babanezhad Azam Marjani Saeed Shirazian
author_facet	Meisam Babanezhad Azam Marjani Saeed Shirazian
author_sort	Meisam Babanezhad
title	Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor
title_short	Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor
title_full	Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor
title_fullStr	Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor
title_full_unstemmed	Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor
title_sort	multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/f12a5b5b2f044ac6bfbc693205cdfa3e
work_keys_str_mv	AT meisambabanezhad multidimensionalmachinelearningalgorithmstolearnliquidvelocityinsideacylindricalbubblecolumnreactor AT azammarjani multidimensionalmachinelearningalgorithmstolearnliquidvelocityinsideacylindricalbubblecolumnreactor AT saeedshirazian multidimensionalmachinelearningalgorithmstolearnliquidvelocityinsideacylindricalbubblecolumnreactor
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Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor

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