Wastewater Treatment Plant Data for Nutrient Removal System
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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.
本数据集采集自丹麦阿特鲁普（BlueKolding，布鲁科灵）污水处理厂，专为通过化学与生物联用工艺强化磷去除效果而构建。该数据集依托高频监控与数据采集（Supervisory Control and Data Acquisition，SCADA）系统开展数据采集工作，覆盖与营养物去除运行动态紧密相关的多类变量。数据集包含时序数据，针对各类控制、工艺及环境变量以2分钟的采样频率进行测量记录。本综合性数据集旨在通过支持先进预测模型的开发与运行策略优化，推动污水处理管理领域的实质性进展。通过揭示化学生物除磷工艺的交互机制与运行效率细节，本数据集可为致力于提升污水处理运行可持续性与处理效能的环境研究人员与工程师提供关键资源支撑。本数据集的终极目标是助力数字孪生构建与机器学习技术（如深度强化学习）的应用落地，以在多变运行工况下预测并优化系统性能。
