Wastewater Treatment Plant Data for Nutrient Removal System

Published: 12 June 2024| Version 2 | DOI: 10.17632/34rpmsxc4z.2
Esmaeel Mohammadi,


The dataset is collected from Denmark's Agtrup (BlueKolding) wastewater treatment plant, specifically designed to enhance phosphorus removal via chemical and biological methods. This rich dataset is assembled through a high-frequency Supervisory Control and Data Acquisition (SCADA) system data collection process, which captures a wide range of variables related to the operational dynamics of nutrient removal. It comprises time-series data featuring measurements sampled to a frequency of two minutes across various control, process, and environmental variables. The comprehensive dataset aims to foster significant advancements in wastewater management by supporting the development of sophisticated predictive models and optimizing operational strategies. By providing detailed insights into the interactions and efficiencies of chemical and biological phosphorus removal processes, the dataset serves as a vital resource for environmental researchers and engineers focused on improving the sustainability and effectiveness of wastewater treatment operations. The ultimate goal of this dataset is to facilitate the creation of digital twins and the application of machine learning techniques, such as deep reinforcement learning, to predict and enhance system performance under varying operational conditions.


Steps to reproduce

1- Download the data 2- Choose the variables 3- Design the model


Veolia Water Solutions and Technologies, Aalborg Universitet


Machine Learning, Reinforcement Learning, Phosphorus, Time Series, Biological Nutrient Removal, Dynamic Simulation, Wastewater, Deep Learning, Long Short-Term Memory Network, Digital Twin


H2020 Marie Skłodowska-Curie Actions