Data-driven modelling of N2O production in wastewater processes using neural ordinary differential equations
File(s)
Published version
Author(s)
Huang, Xiangjun
Mousavi, Alireza
Kandris, Kyriakos
Katsou, Evina
Type
Journal Article
Abstract
Modelling nitrous oxide (N2O) production in wastewater treatment processes presents greater challenges than for other components, owing to its multiple production pathways and pronounced spatiotemporal variations. This study proposes a novel data-driven approach employing neural ordinary differential equations (NODEs) to capture the intrinsic dynamics of N2O production in typical activated sludge processes. The NODE models are trained directly on state trajectory data, which incorporate continuous influent variations and operational adjustments as external forcings to the system dynamics. To address these external influences, we extend standard training procedures. In addition, a normalisation technique and an incremental strategy are introduced to enhance the computational efficiency of NODE implementation in stiff wastewater systems. This methodology is validated using simulated data from the benchmark simulation model no. 1 (BSM1) plant, adapted to integrate the activated sludge model for greenhouse gases no. 1 (ASMG1). Results demonstrate the efficacy of NODE-based approach in accurately capturing the complex dynamics governing N2O production, highlighting its potential for controlling and mitigating greenhouse gases emissions in wastewater treatment.
Date Issued
2026-03-01
Date Acceptance
2025-12-17
Citation
Water Science and Technology, 2026, 93 (5), pp.602-614
ISSN
0273-1223
Publisher
IWA Publishing
Start Page
602
End Page
614
Journal / Book Title
Water Science and Technology
Volume
93
Issue
5
Copyright Statement
© 2026 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/).
License URL
Identifier
10.2166/wst.2026.231
Subjects
activated sludge
data-driven
modelling
neural ordinary differential equations
nitrous oxide
Publication Status
Published
Article Number
5
Date Publish Online
2026-02-17