Machine learning in wastewater treatment: insights from modelling a pilot denitrification reactor

• URL: http://arxiv.org/abs/2412.14030v1
• Date: Wed, 18 Dec 2024 16:49:23 GMT
• Title: Machine learning in wastewater treatment: insights from modelling a pilot denitrification reactor
• Authors: Eivind Bøhn, Sølve Eidnes, Kjell Rune Jonassen,
• Abstract summary: We use data from a pilot reactor at the Veas treatment facility in Norway to explore how machine learning can be used to optimize biological nitrate. Rather than focusing solely on predictive accuracy, our approach prioritizes understanding the foundational requirements for effective data-driven modelling.
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• Abstract: Wastewater treatment plants are increasingly recognized as promising candidates for machine learning applications, due to their societal importance and high availability of data. However, their varied designs, operational conditions, and influent characteristics hinder straightforward automation. In this study, we use data from a pilot reactor at the Veas treatment facility in Norway to explore how machine learning can be used to optimize biological nitrate ($\mathrm{NO_3^-}$) reduction to molecular nitrogen ($\mathrm{N_2}$) in the biogeochemical process known as \textit{denitrification}. Rather than focusing solely on predictive accuracy, our approach prioritizes understanding the foundational requirements for effective data-driven modelling of wastewater treatment. Specifically, we aim to identify which process parameters are most critical, the necessary data quantity and quality, how to structure data effectively, and what properties are required by the models. We find that nonlinear models perform best on the training and validation data sets, indicating nonlinear relationships to be learned, but linear models transfer better to the unseen test data, which comes later in time. The variable measuring the water temperature has a particularly detrimental effect on the models, owing to a significant change in distributions between training and test data. We therefore conclude that multiple years of data is necessary to learn robust machine learning models. By addressing foundational elements, particularly in the context of the climatic variability faced by northern regions, this work lays the groundwork for a more structured and tailored approach to machine learning for wastewater treatment. We share publicly both the data and code used to produce the results in the paper.

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