1. Home
  2. /
  3. Research
  4. /
  5. Digitization

Digitization as an instrument to improve the security of supply and disposal in water purification

Digitization is more than fast Internet email communication and data collection for the annual report. If we want to advance digitization in the context of the sustainability goals of the United Nations in such a way that future-oriented models and solutions emerge, then we have to overcome short-term thinking and acting - Creating future-oriented ability to act is a process and the right time to start is now.

The use of data is one thing - interpretation and the derivation of actions are the other

We can use this data to determine solution strategies and optimize implementation processes. Through this targeted and intelligent use and evaluation of data , as well as efficient control technology, the wastewater is cleaner and treated in accordance with the precautionary principle. In addition, resources (energy) are conserved and wastewater is used as a resource.

The precautionary principle is the guiding principle for water management.

For years, the wastewater industry has been confronted with continuous changes in the concentrations of micropollutants in water. If you take the Water Framework Directive, the overriding goal of water quality management is to ensure good water quality in European surface and groundwater bodies (EU Directive 2000/60 / EC).

In many places, however, the pollution of the different bodies of water is so high that the required “good chemical status” of surface water is currently not achieved in more than half of the area. The influence of the oceans as sinks, for example, must also be considered here (EU Directive 2008/105 / EC and Directive 91/676 / EC) and included in the evaluations.

In addition, the precautionary principle is anchored in many international conventions such as the UN Framework Convention on Climate Change and the OSPAR Convention for the Protection of the Marine Environment of the North-East Atlantic. At the national (German) level, the federal government is following developments in the area of the precautionary principle as part of the “federal digitization strategy”.

For the area of water management, the areas and fields of activity described in the following graphic as well as levels of action regarding the achievement of the sustainability goals were described.

Our research approaches combine scientifically collected data with practicable recommendations for action, so that values generated from data and digital methods (machine learning, artificial intelligence (AI)) can be deployed and used in a targeted manner.

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 the water quality of the waters by reducing the water pollution with carbon, nitrogen and phosphorus, but the complexity of pollution and the limitations of the cleaning services are constantly increasing.

In addition to water protection, topics such as energy efficiency and the consideration of wastewater as a valuable resource source (NEW approach: nutrient - energy - water recycling from wastewater) are just as important as minimizing the ecological footprint by further removing anthropogenic pollution with potentially adverse effects from the water.

A process control system of a sewage treatment plant per se provides high data quality in monitoring (hydrometry, operational management, etc.). Complemented by big data using low-cost sensors, real-time monitoring, networking, and surveillance, they provide the playground for AI experts and solution researchers like us.

The problem that unstructured collections of data can no longer be tamed with conventional IT infrastructure can be solved by using big data (data science / data analytics). For this purpose, the large amounts of data are collected and sorted and analyzed using methods of artificial intelligence, machine learning and practical expert knowledge. Values emerge from data.