2026-06-30T01:11:24Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/74352024-05-14T10:54:24Zcom_10259_4244com_10259_5086com_10259_2604com_10259_4402col_10259_4245col_10259_6209

Cámara Nebreda, José María Diez Blanco, Victorino Ramos Rodríguez, Cipriano 2023-02-09T10:48:02Z 2023-02-09T10:48:02Z 2023-02 0952-1976 http://hdl.handle.net/10259/7435 10.1016/j.engappai.2022.105643 Anaerobic 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. This work is part of the project TED2021-132393B-I00, funded by the MCIN/AEI/10.13039/501100011033 and the European Union ‘‘NextGenerationEU’’/PRTR. application/pdf eng Elsevier Engineering Applications of Artificial Intelligence. 2023, V. 118, 105643 https://doi.org/10.1016/j.engappai.2022.105643 info: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/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess AnMBR Filtration Neural networks Feed forward LSTM Preprocessing Retraining Prediction Electrotecnia Ingeniería química Electrical engineering Chemical engineering Neural network modelling and prediction of an Anaerobic Filter Membrane Bioreactor info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Engineering Applications of Artificial Intelligence 118