Imagine treating water right where it's used instead of pumping it miles away to a giant plant. This project creates a toolkit of 'mini-plants' like green walls and floating gardens that clean water on the spot. It also builds a smart digital map to help cities decide exactly which tool to use for their specific neighborhood.
Cities face water shortages and floods due to climate change, while centralized sewage systems are expensive and inflexible. There is a lack of data-driven tools to help managers choose the right local water treatment options.
An AI-driven Decision Support System (DSS) featuring a technology catalogue, GIS tools, and 6 physical water treatment solutions like green walls and biofilters.
If you are a developer dealing with strict urban runoff laws — this project developed green roofs and cisterns that capture stormwater. This allows you to reuse water on-site and reduce sewage fees.
If you are a utility provider dealing with aging pipes and flood risks — this project developed an AI-driven decision platform. It helps you pick from 6 different decentralized technologies to manage water locally.
If you are a consultant dealing with emerging pollutants in rivers — this project developed biofilters and phototrophic bacteria systems. You can use these to offer clients a data-backed way to remove pollution near the source.
Based on available project data, specific pricing is not provided, but the project will use Life Cycle Costing (LCC) and cost-benefit analysis to determine economic viability.
The project demonstrates 6 different technologies across diverse geographic contexts, suggesting a scalable approach to decentralized management.
Based on available project data, there is no specific mention of licensing terms or patent strategies in the objective.
The project proposes a governance model and identifies administrative barriers to help authorities adapt policies for water reuse.
The project runs from 2025-09-01 to 2029-08-31, meaning full results and the AI platform will be ready by late 2029.
The consortium is well-balanced for technology transfer, featuring 16 partners from 8 countries. With a 25% industry ratio (4 industrial partners, including 2 SMEs), there is a clear bridge between the 8 universities and the commercial market, ensuring that the 6 developed technologies are grounded in business reality.
Contact Universidad de Cantabria in Spain
Contact us to track the AI Decision Support System development for early access.
