Physics-informed deep learning for modelling particle aggregation and breakage processes

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dc.contributor.authorChen, Xizhong
dc.contributor.authorWang, Li Ge
dc.contributor.authorMeng, Fanlin
dc.contributor.authorLuo, Zheng-hong
dc.contributor.funderUniversity College Corken
dc.contributor.funderInnovate UKen
dc.date.accessioned2021-07-14T11:13:25Z
dc.date.available2021-07-14T11:13:25Z
dc.date.issued2021-07-09
dc.date.updated2021-07-14T11:06:59Z
dc.description.abstractParticle aggregation and breakage phenomena are widely found in various industries such as chemical, agricultural and pharmaceutical processes. In this study, a physics-informed neural network is developed for solving both the forward and inverse problems of particle aggregation and breakage processes. In this method, the population balance equation is directly embedded in the loss function of a neural network so that the network can be trained efficiently and fulfil physical constraints. For the forward problems, solutions of population balance equations are obtained through the optimization of the neural network where the predictions well match the analytical solutions. In the inverse modelling, the data-driven discovery of model parameters of population balance equations is investigated. The sensitivity regarding the selection of different neural network structures is also investigated. The developed population balance equations embedded with neural network approach is promising for solving inverse problems of particle aggregation and breakage processes with noisy observation data.en
dc.description.sponsorshipInnovate UK (Knowledge Transfer Partnership between University of Sheffield and Process Systems Enterprise)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationChen, X., Wang, L. G., Meng, F. and Luo, Z.-h. (2021) 'Physics-informed deep learning for modelling particle aggregation and breakage processes', Chemical Engineering Journal. doi: 10.1016/j.cej.2021.131220en
dc.identifier.doi10.1016/j.cej.2021.131220en
dc.identifier.issn1385-8947
dc.identifier.journaltitleChemical Engineering Journalen
dc.identifier.urihttps://hdl.handle.net/10468/11567
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.rights© 2020, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectAggregationen
dc.subjectBreakageen
dc.subjectInverse problemen
dc.subjectParameter estimationen
dc.subjectPhysics-informed neural networken
dc.subjectPopulation balance equationen
dc.titlePhysics-informed deep learning for modelling particle aggregation and breakage processesen
dc.typeArticle (peer-reviewed)en
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