Towards good modelling practice for parallel hybrid models for wastewater treatment processes.
WRRF
biological wastewater treatment
convolutional neural network
data-driven model
long short-term memory
mechanistic model
Journal
Water science and technology : a journal of the International Association on Water Pollution Research
ISSN: 0273-1223
Titre abrégé: Water Sci Technol
Pays: England
ID NLM: 9879497
Informations de publication
Date de publication:
Jun 2024
Jun 2024
Historique:
received:
07
02
2024
accepted:
03
05
2024
medline:
15
6
2024
pubmed:
15
6
2024
entrez:
15
6
2024
Statut:
ppublish
Résumé
This study explores various approaches to formulating a parallel hybrid model (HM) for Water and Resource Recovery Facilities (WRRFs) merging a mechanistic and a data-driven model. In the study, the HM is constructed by training a neural network (NN) on the residual of the mechanistic model for effluent nitrate. In an initial experiment using the Benchmark Simulation Model no. 1, a parallel HM effectively addressed limitations in the mechanistic model's representation of autotrophic bacteria growth and the data-driven model's incapability to extrapolate. Next, different versions of a parallel HM of a large pilot-scale WRRF are constructed, using different calibration/training datasets and different versions of the mechanistic model to investigate the balance between the calibration effort for the mechanistic model and the compensation by the NN component. The HM can improve predictions compared to the mechanistic model. Training the NN on an independent validation dataset produced better results than on the calibration dataset. Interestingly, the best performance is achieved for the HM based on a mechanistic model using default (uncalibrated) parameters. Both long short-term memory (LSTM) and convolutional neural network (CNN) are tested as data-driven components, with a CNN HM (root-mean-squared error (RMSE) = 1.58 mg NO
Identifiants
pubmed: 38877625
pii: wst_2024_159
doi: 10.2166/wst.2024.159
doi:
Substances chimiques
Wastewater
0
Nitrates
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2971-2990Subventions
Organisme : Natural Sciences and Engineering Research Council of Canada
ID : RGPIN-2021-04347
Organisme : Onderzoeksprogramma Artifciële Intelligentie (AI) Vlaanderen
Informations de copyright
© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
Déclaration de conflit d'intérêts
The authors declare there is no conflict.
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