The requirements for the purification performance of sewage treatment plants result directly from the requirements of water protection.
The high level of wastewater treatment has contributed significantly to improving water quality by reducing carbon, nitrogen and phosphorus pollution, but the complexity of contaminants (e.g microplastics
) and the limitations of treatment performance are constantly increasing.
In addition to water protection, issues such as energy efficiency and the consideration of wastewater as a valuable resource source
(N-E-W approach: nutrient - energy - water recycling from wastewater
) are just as important today as minimising the ecological footprint by further removing anthropogenic pollution with potentially adverse effects in the water body.
A digital process control system of a wastewater treatment plant provides per sé a high data quality in monitoring (hydrometry, operational management, etc.)
The problem that unstructured data collections can no longer be tamed with conventional IT infrastructure can be solved by using Big Data (Data Science / Data Analytics).
This involves collecting the large amounts of data and organising and analysing them using methods of artificial intelligence, machine learning and practical expert knowledge. Values arise from data.
In combination with low cost sensors and real-time monitoring, this provides the playground for AI experts and solution researchers like us.
We use this data to determine solution strategies and optimise technological implementation processes. Wastewater treatment reaches unprecedented levels of efficiency.