EVOLVE: Electric Vehicles Point Location Optimisation via Vehicular Communications

EVOLVE aims to exploit the scientific excellence and expertise of key academic and industrial players into a joint collaborative effort to design, develop and test various technologies that consider the holistic view of Electric Vehicles (EV) charging taking into account the view of technical and business stakeholders. EVOLVE will pursue innovation for advancing the technologies in EV charging ecosystem by orchestrating and managing the underlying networking and computational resources, design innovative algorithms using Artificial Intelligence (AI) and Machine Learning ML), developing communication protocols, prediction and optimising load in smart grids, developing software systems and user interfaces (UIs). EVOLVE aims to transcend analytical models and simulation-based validation and aims to deliver five proof-of-concept (PoC) demonstrations. EVOLVE project will provide a platform to foster a close collaboration between academia and industry partners providing each with a unique experience to create new knowledge, share know-how and skills development.

DiTaS: A framework for agnostic compositional and cognitive digital twin services

The requirements for continuous analysis and prediction of the real-time behavior of assets and their possible future state have led to the emergence of the well-known digital twins. Digital twins play a key role in the digital transformation for the industry 4.0 and beyond. However, the efforts made so far have pursued vertical and tailored solutions, but not interoperable and plug&play services. The DiTaS project shifts towards interoperable, composable, and cognitive digital twins as services. DiTaS will provide an open digital twin framework for the definition of interoperable, agnostic, and cognitive digital twins services in the Industry 4.0.

ZeroVision: Enabling Zero impact wastewater treatment through Computer Vision and Federated AI

ZeroVision will design and deliver a next-generation AI-based ecosystem of technologies, integrated into a single process control platform to enable intelligent, highly efficient, sustainable, and resilient operations in wastewater management through Computer Vision and Federated AI.

GEDIER: Application of Digital Twins to more sustainable irrigated farms

Every object in a farm (crop, soil, equipment) can be virtualized (digital twin) and remotely controlled by IoT systems. A Digital Twin is the digital equivalent of a real-life object and reproduces its behavior throughout its life cycle in a virtual space. Using a Farm Digital Twin for irrigated farms, (IFDT), it is possible to characterize and improve the processes necessary for production to achieve higher productivity and sustainability. The aim of GEDIER is to develop an agricultural production model that takes advantage of disruptive digital technologies and moves towards an agriculture with greater protection of natural resources (water, soil, fertilizers, and inputs in general) and increases people’s quality of life using digital twins.

TRIATHLON: TRIhAlomeTHanes controL thrOugh AI-based techNologies

DALISE: Design/development of a Data Lake and set of services for experimentation and innovation in energy flexibility environments (DALISE: Data Lake and innovation services for energy).

OPTIMA-DONES: Cyber-physical system for proactive supervision and maintenance of critical systems of IFMIF-DONES

GEDERA: Intelligent management of flexible power demand in coupled hybrid networks

The main objective of the GEDERA project is to develop and validate the usefulness of a multi-agent tool (users and systems) to streamline the integration of renewables and flexible resources in the energy system, from the point of view of digitization, monitoring, control and participation in new markets in relation to the figure of the aggregator and local energy and flexibility markets.

Development of an intelligent early warning system to control abstractions and groundwater contamination in urban water supply during periods of drought.

During droughts, both the quantity and quality of available water decline, often requiring reliance on underground aquifers. To prevent overexploitation and contamination, advanced technologies are needed. This project aims to develop an Early Warning System (iSAT) using low-cost sensors and artificial intelligence algorithms to continuously monitor water quality and availability. The system will send alerts to managers in case of health risks, enhancing groundwater management. The goal is to experimentally validate the prototype under real conditions and reach a TRL7 technology readiness level.

Digital twins based on predictive systems for the management of irrigation water in irrigation communities (DIGAGRO).

Faced with increasing water scarcity in Spain, particularly in Andalusia, the agricultural sector has shown strong adaptability through efficient water use. The DIGAGRO project proposes developing advanced digital twins powered by artificial intelligence to optimize irrigation management during drought conditions. Led by the University of Málaga and the University of Córdoba, this initiative will enable water consumption forecasting, anomaly detection, long-term demand analysis, and scenario simulation. Its goal is to support decision-making in irrigation communities through innovative and predictive tools.

SIEITI: Industrial Pipeline Interior Condition Information System – Collaboration with INNTELIA Soluciones Tecnológicas

Cepsa. Research and innovation work related to the Hydrocracker (HCK) unit located at the Cepsa Energy Park in La Rábida.