Wastewater Treatment Plant Data for Nutrient Removal System
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911948177670144 |
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| author | Mohammadi, Esmaeel Rani, Anju Stokholm-Bjerregaard, Mikkel Ortiz-Arroyo, Daniel Durdevic, Petar |
| author_facet | Mohammadi, Esmaeel Rani, Anju Stokholm-Bjerregaard, Mikkel Ortiz-Arroyo, Daniel Durdevic, Petar |
| contents | This paper introduces the Agtrup (BlueKolding) dataset, collected from Denmark's Agtrup 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05346 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wastewater Treatment Plant Data for Nutrient Removal System Mohammadi, Esmaeel Rani, Anju Stokholm-Bjerregaard, Mikkel Ortiz-Arroyo, Daniel Durdevic, Petar Systems and Control Computational Engineering, Finance, and Science Signal Processing This paper introduces the Agtrup (BlueKolding) dataset, collected from Denmark's Agtrup 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. |
| title | Wastewater Treatment Plant Data for Nutrient Removal System |
| topic | Systems and Control Computational Engineering, Finance, and Science Signal Processing |
| url | https://arxiv.org/abs/2407.05346 |