Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors

Abstract To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-p...

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Autores principales:	Rasool Pelalak, Ali Taghvaie Nakhjiri, Azam Marjani, Mashallah Rezakazemi, Saeed Shirazian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ccb41b33880f4959af24cb09d3c2adf7
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spelling	oai:doaj.org-article:ccb41b33880f4959af24cb09d3c2adf72021-12-02T15:23:28ZInfluence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors10.1038/s41598-021-81514-y2045-2322https://doaj.org/article/ccb41b33880f4959af24cb09d3c2adf72021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81514-yhttps://doaj.org/toc/2045-2322Abstract To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas–liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.Rasool PelalakAli Taghvaie NakhjiriAzam MarjaniMashallah RezakazemiSaeed ShirazianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rasool Pelalak Ali Taghvaie Nakhjiri Azam Marjani Mashallah Rezakazemi Saeed Shirazian Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors
description	Abstract To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas–liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.
format	article
author	Rasool Pelalak Ali Taghvaie Nakhjiri Azam Marjani Mashallah Rezakazemi Saeed Shirazian
author_facet	Rasool Pelalak Ali Taghvaie Nakhjiri Azam Marjani Mashallah Rezakazemi Saeed Shirazian
author_sort	Rasool Pelalak
title	Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors
title_short	Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors
title_full	Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors
title_fullStr	Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors
title_full_unstemmed	Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors
title_sort	influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ccb41b33880f4959af24cb09d3c2adf7
work_keys_str_mv	AT rasoolpelalak influenceofmachinelearningmembershipfunctionsanddegreeofmembershipfunctiononeachinputparameterforsimulationofreactors AT alitaghvaienakhjiri influenceofmachinelearningmembershipfunctionsanddegreeofmembershipfunctiononeachinputparameterforsimulationofreactors AT azammarjani influenceofmachinelearningmembershipfunctionsanddegreeofmembershipfunctiononeachinputparameterforsimulationofreactors AT mashallahrezakazemi influenceofmachinelearningmembershipfunctionsanddegreeofmembershipfunctiononeachinputparameterforsimulationofreactors AT saeedshirazian influenceofmachinelearningmembershipfunctionsanddegreeofmembershipfunctiononeachinputparameterforsimulationofreactors
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Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors

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