Water treatment is crucial for a well-functioning and modern urban society, supplying drinking water to the population and ensuring that wastewater is properly treated before being released back to the natural environment.
The water and wastewater treatment sectors are facing several and significant challenges related to the quality, environmental and safety requirements. In addition, capital, operations and management costs of water and wastewater treatment are increasing. Many of the receiving waters for treated wastewater, e.g. the Oslo fjord, are widely used for recreational activities and have an ecosystem which is vulnerable to contamination. The influent to the wastewater facilities is steadily increasing due to e.g. demographic changes and climate change, and is approaching the maximum capacity of the existing systems. Furthermore, many facilities are old and are in need of significant repairs and capital upgrades. In addition, the treatment processes are highly complex, consists of several steps and are affected by plant control inputs such as chemicals and energy.
This results in increasing needs for optimal operation of the treatment processes. For the treatment sector, the challenge is to exploit and utilize existing infrastructure, and to establish new infrastructure in a sustainable manner. In this capacity, ML is one of several levers and will be the preferred methodology in this project. ML methods represent the state-of-the-art in data-driven modelling and have a huge potential for modelling and control of complex processes with a solid data foundation. To increase accuracy and autonomy in process control, INVAPRO will develop ML algorithms for the water and wastewater treatment processes that provide new domain knowledge and insight, and that can be used directly in daily operations.
In addition, INVAPRO aims to facilitate innovation within both the water and wastewater utilities and the supplier industries by developing not only technologies, but also enable new services, business models and ways of collaboration. This sector-wide and multi-faceted view of innovation aspect is expected to be one of the major contributions of the project.
