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Functional input and membership characteristics in the accuracy of machine learning approach for estimation of multiphase flow

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dc.contributor.author Babanezhad, Meisam
dc.contributor.author Nakhjiri, Ali Taghvaie
dc.contributor.author Rezakazemi, Mashallah
dc.contributor.author Marjani, Azam
dc.contributor.author Shirazian, Saeed
dc.date.accessioned 2020-10-29T09:38:09Z
dc.date.available 2020-10-29T09:38:09Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/10344/9376
dc.description peer-reviewed en_US
dc.description.abstract In the current study, Artificial Intelligence (AI) approach was used for the learning of a physical system. We applied four inputs and one output in the learning process of AI. In the learning process, the inputs are space locations of a BCR (bubble column reactor), which are x, y, and z coordinate as well as the amount of gas fraction in BCR. The liquid velocity is also considered as output. A variety of functions were used in learning, such as gbellmf and gaussmf functions, to examine which functions can give the best learning. At the end of the study, all of the results were compared to CFD (computational fluid dynamics). A three-dimensional (3D) BCR was used in this research, and we studied simulation by CFD as well as AI. The data from CFD in a 3D BCR was studied in the AI domain. In AI, we tuned for various parameters to achieve the best intelligence in the system. For instance, different inputs, different membership functions, different numbers of membership functions were used in the learning process. Moreover, the meshless prediction was used, meaning that some data in the BCR have not participated in the learning, and they were predicted in the prediction process, which gives us a special capability to compare the results with the CFD outcomes. The findings showed us that AI can predict the CFD results, and a great agreement was achieved between CFD computing nodes and AI elements. This novel methodology can suggest a meshless and multifunctional AI model to simulate the turbulence flow in the BCR. For further evaluation, the ANFIS method is compared with ACOFIS and PSOFIS methods with regards to model’s accuracy. The results show that ANFIS method contains higher accuracy and prediction capability compared with ACOFIS and PSOFIS methods. en_US
dc.language.iso eng en_US
dc.publisher Nature en_US
dc.relation.ispartofseries Scientific reports;10, 17793
dc.subject Artificial Intelligence en_US
dc.subject gbellmf en_US
dc.title Functional input and membership characteristics in the accuracy of machine learning approach for estimation of multiphase flow en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1038/s41598-020-74858-4
dc.contributor.sponsor Government of the Russian Federation en_US
dc.contributor.sponsor Ministry of Science and Higher Education of Russia en_US
dc.relation.projectid FENU-2020-0019 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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