SMART-Plant supporting decision-making to reduce carbon footprint

The implementation of innovative resource recovery technologies at industrial scale level is always a big decision making challenge. To ease this task, the SMART-Plant H2020 project is testing a Decision Support System (DSS).

The GENOCOV group from Universitat Autonoma of Barcelona is working on a Decision Support System (DSS) that finds an optimal configuration of a WWTP based on seven innovative resource recovery unit processes tested during the SMART-Plant H2020 project (No.690323). For each configuration the DSS performs a plant design based on local long term inflow conditions, sewer characteristics and effluent limitations and finally sorts those configurations in relation to economic, effluent quality and environmental impact multi-criteria. The DSS will contribute to lower the risk aversion of water utilities in moving from conventional technologies to techs with embedded additional benefits from recovery of resources. Wellness Smart Cities is working with Brunel University London for the development of the SMART-Plant online energy consumption and operational carbon footprint web-application for the continuous evaluation of the energy and direct greenhouse gas emissions of the SMART-Plant processes. The application integrates sustainability indicators together with metrics conventionally monitored in wastewater processes. Brunel University is developing structured approaches (data mining techniques for pattern recognition, dependencies identification) to analyse long-term laboratory and sensor data. SMART-Plant’s methodological framework will guide towards the identification of operating parameters that optimize the carbon and energy footprint of biological processes in new and existing wastewater treatment plants.


Universitat Autonoma of Barcelona: Živko Južnič-Zonta; Juan Antonio Baeza;

Brunel University London: Vasileia Vasilaki, Evina Katsou

Wellness Smart Cities: José María Suazo Iglesias

Coordinator - Marche Polytechnic University: Francesco Fatone


16 April 2019

This project has received funding from the European Union’s Horizon 2020 Programme for research, technological development and demonstration under grant agreement No. 689239