Securing enough and safe water is a crucial challenge for water management today. This situation calls for innovative water management solutions and alternative water resources. The EU-funded WATER-MINING project will demonstrate innovative next-generation water resource solutions in various demo cases in accordance with relevant legislation, such as the Water Framework Directive, Circular Economy and EU Green Deal packages. The project investigates both water management services and the improvement of renewable resources such as mining water.
Mining water and resources from desalination, urban and industrial wastewater streams
In the EU’s Horizon2020 program, the WATER-MINING project aims to increase the sustainability of water by stimulating reuse. A total of 6 case studies throughout Europe, demonstrate how the desalination process can be more energy-efficient and less polluting; by making the extraction process of important products (e.g. phosphate) from urban wastewater residues more sustainable; and by implementing Zero-Liquid-Discharge loop systems in the production of pollution-free industrial wastewater.
The project will examine different designs proposed for urban wastewater treatment and seawater desalination and innovative service-based business models aiming to improve the engagement of private and public stakeholders.
ICT solutions offer information to visualize, monitor and improve the recovery processes
KWR leads the development and implementation of ICT solutions to help the case studies reach their goals (e.g. energy reduction or increased mineral purity). These tools consist of an interactive dashboard (NESSIE from NTUA)and augmented reality (AR) applications developed by ICCS. Both will be developed to visualize the envisioned increase of circularity, energy efficiency and/or resource recovery. The visualization will help plant decision makers and process operators optimize their process. Moreover, it will facilitate the engagement with policy makers, citizens and other stakeholders.
The dashboard, will present real time sensor data enriched with model data as well as model predictions. These models developed in the project can be either process models or machine learn models and can detect anomalies or act as a soft sensor (a soft sensor determines the value of a parameter that cannot be measured directly by utilizing a model with actual measurements as input). Moreover, these models allow predictions, which allows an optimal control of the process. The technique of Reinforced Learning (RL), allows optimal control and is based on different parameters, which may even be competing: for example, balancing an optimal mineral yield with a minimal energy consumption. The dashboard and its connections to the different models will be applied in the different use cases and developed in close collaboration with end-users.
The AR applications will visualize different types of virtual data by enhancing the physical surroundings with virtual elements. They will drastically increase the learning value of the showcases by making visible to the users the ‘hidden’ or ‘intangible’ elements of the water cycle and demonstrated solutions. The virtual elements will consist of 3D and 2D elements (such as graphs, objects and images), videos and surveys.