Integrating data-driven models and process expertise in soft-sensor design for a wastewater treatment digital twin application
Loading...
Access rights
openAccess
CC BY
CC BY
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
20
Series
Water Science and Technology, Volume 92, issue 9, pp. 1308-1327
Abstract
Digital twin models offer great potential for process improvements in wastewater treatment plants (WWTPs). Such models require a constant real-time input data feed from the physical process. Collecting these data is challenging, especially in the harsh conditions in the headworks of the process. In this study, data-driven models and process and sewer system expertise were combined to design soft-sensors for primary effluent COD and NH4-N prediction. Ordinary least squares regression and the seasonal autoregressive integrated moving average model with exogenous variables were tested using flow rate and suspended solids concentration as model input. An excellent NH4-N prediction was achieved, and the prediction accuracy was further improved by implementing process-insight-driven weights. The tested models were able to achieve either good COD estimation accuracy or effectively capture the variability in the target data. However, achieving both simultaneously remained challenging, with or without weights. Simulation tests using the calibrated process model demonstrated that the developed soft-sensors were able to provide real-time predictions leading to goodness-of-fit in simulations comparable to or better than that achieved using laboratory data influent quality.Description
Other note
Citation
Haimi, H, Awaitey, A, Kiran, A, Larsson, T, Blomberg, K, Elvander, F, Petaja, E, Mulas, M, Sahlstedt, K & Mikola, A 2025, 'Integrating data-driven models and process expertise in soft-sensor design for a wastewater treatment digital twin application', Water Science and Technology, vol. 92, no. 9, pp. 1308-1327. https://doi.org/10.2166/wst.2025.154