2026-06-30T05:20:19Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/74352024-05-14T10:54:24Zcom_10259_4244com_10259_5086com_10259_2604com_10259_4402col_10259_4245col_10259_6209
Repositorio Institucional de la Universidad de Burgos
author
Cámara Nebreda, José María
author
Diez Blanco, Victorino
author
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.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
AnMBR
Filtration
Neural networks
Feed forward
LSTM
Preprocessing
Retraining
Prediction
Neural network modelling and prediction of an Anaerobic Filter Membrane Bioreactor
info:eu-repo/semantics/article
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
URL
https://riubu.ubu.es/bitstream/10259/7435/1/Camara-eaai_2023.pdf
File
MD5
4fad2c76d8d663e886a279edc781cb3a
4091722
application/pdf
Camara-eaai_2023.pdf