Kanalmonitoring mit KI - vom Potenzial zur Umsetzung
Michaela Rohrbach et al.
Available online
In the research project LiveSewer, real-time wastewater data are processed and analyzed using AI-supported pattern recognition (machine learning).
The aim of automated data evaluation based on machine learning algorithms is to recognize patterns for certain material conditions in the wastewater system in order to develop previously impossible operating and monitoring applications. This is being investigated and demonstrated in the LiveSewer project using three different examples of use. In this project, ISOE conducts application-oriented stakeholder workshops with decision-makers from politics and administration as well as potential users such as municipalities and other authorities. The aim is to increase the understanding of AI-based wastewater monitoring. At the same time, this is an opportunity to find out about the stakeholders’ expectations regarding AI and what other areas of application they see for this measuring system. The transdisciplinary approach promotes the transferability potential of this innovation to other areas of application.
The Covid-19 pandemic has shown that analyzing wastewater can provide in-depth information about material flows in the wastewater system and regarding the situation in the catchment area. However, while the detection of coronaviruses was usually carried out centrally via manual sampling at wastewater treatment plants, innovative online measuring systems open up significantly broader perspectives for the continuous and comprehensive monitoring of various material flows. Combined with automated, AI-supported data evaluation, previously impossible solutions can be developed for the management of wastewater systems and associated external applications ranging from the efficient use of operating chemicals and continuous discharge monitoring to water reuse and water protection.
The project “LiveSewer” is funded by the Federal Ministry of Education and Research (BMBF) as part of the Digital GreenTech funding programme.
Michaela Rohrbach et al.
Available online
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