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dc.contributor.authorCámara Nebreda, José María 
dc.contributor.authorDiez Blanco, Victorino 
dc.contributor.authorRamos Rodríguez, Cipriano 
dc.date.accessioned2023-02-09T10:48:02Z
dc.date.available2023-02-09T10:48:02Z
dc.date.issued2023-02
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/10259/7435
dc.description.abstractAnaerobic membrane bioreactors have become an environmentally friendly solution for wastewater treatment. The lack of sufficiently accurate mathematical procedures to model their behaviour and the fouling process of the membranes, poses a challenge when trying to optimise their energy consumption and maintenance costs. An accurate model of the fouling process of the membranes is critical to make the most of this technology. This is a perfect scenario in which to introduce neural networks (NN) as an alternative to mathematical modelling. However, the duration of the experiments and the difficulties in measuring some relevant variables, make it hard to collect high quality datasets to train the NN. Our goal is to obtain a good prediction of the fouling status of the membranes to enable an adjustment of operation conditions and maintenance procedures ahead in time. To do so we must obtain high quality datasets to train our neural networks. The combination of static and dynamic networks enables us to leverage the best prediction capabilities of each one. This combination requires a preprocessing of the datasets that separates trends from oscillations. The outputs obtained need to be put together to build up the predicted evolution of fouling. Accurate predictions are then extended from 25 to up to 75 filtration cycles. To maintain and even extend accuracy after sudden changes in operating conditions, retraining the NN every 25 cycles is proposed. AI based real time predictions open a new scope for decision making, and optimisation in the field of anaerobic membrane reactors.en
dc.description.sponsorshipThis work is part of the project TED2021-132393B-I00, funded by the MCIN/AEI/10.13039/501100011033 and the European Union ‘‘NextGenerationEU’’/PRTR.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieren
dc.relation.ispartofEngineering Applications of Artificial Intelligence. 2023, V. 118, 105643en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAnMBRen
dc.subjectFiltrationen
dc.subjectNeural networksen
dc.subjectFeed forwarden
dc.subjectLSTMen
dc.subjectPreprocessingen
dc.subjectRetrainingen
dc.subjectPredictionen
dc.subject.otherElectrotecniaes
dc.subject.otherElectrical engineeringen
dc.subject.otherIngeniería químicaes
dc.subject.otherChemical engineeringen
dc.titleNeural network modelling and prediction of an Anaerobic Filter Membrane Bioreactoren
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.engappai.2022.105643es
dc.identifier.doi10.1016/j.engappai.2022.105643
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-132393B-I00/ES/Biofactoría inTegrada para la producción de biogás y biocompuestos, absorción de CO2 y depuración de efluentes Agroindustriales/es
dc.journal.titleEngineering Applications of Artificial Intelligenceen
dc.volume.number118es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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