Project Listing

Microbial Electrochemical Technologies (METs) will be developed as sustainable and efficient methods to i) transform wastewater residual components into high value-added bioproducts, and/or ii) degrade recalcitrant pollutants.

Recent advances in signal processing algorithms and digital signal processors have allowed to implement many digital applications. These advances can be applied to energy management systems. These systems need to process enormous amounts of information in order to identify various problems and make management decisions more accurate.

In different research areas in the Energy Sector there are problems to solve that many times involve complex algorithms. Some of these algorithms can benefit from the fact that there can be a great reduction in their processing times when using the High Performance computing power of GPUS (Graphic Proccessing Units).

Energy management system that integrates Smart Grids with Home Area Networks (HAN) that use smart appliance, home automation and Internet of Thing devices must address security and privacy features. That system must enable energy efficiency, energy service demand and save energy through monitoring and controlling energy in real time.

The systems based in spectral images has been proved as a very powerful tool in many different applications such as biotechnology, high precision agriculture, remote sensing, satellite image systems, artificial vision systems, among others. This research line is devoted to the study and development of systems for sensing spectral images based on smart sensors. The main goal is to design sensing systems appropriate for each application but with the reduction of the energy consumption in the sensing process in mind and with computing systems that can analyze the data in all spectral bands efficiently

The social adoption of smart energy requires not only an adequate technological deployment but must be accompanied by communication and awareness processes that contribute to its acceptance by citizens.
From the perspective of Research and Responsible Innovation, the communication and the associated training processes on Intelligent Energy require professionals and specialized researchers who can contribute to accelerate this acceptance. Within the actions of the Scientific Communication Observatory, the project is oriented towards the challenge of changes and social innovations to improve the develop plans for the communication, training and scientific dissemination of efficient research projects about Smart Energy, align them with the RRI guidelines, to broaden their social impact and contribute to increasing the SP on Science and Technology, measuring its effectiveness through a structure of indicators, and facilitating decision-making in the design of WP dissemination.

This research line tries to fill this gap, proposing the development of new approaches based on the combination of life cycle sustainability assessment (LCSA), including environmental, social and economic aspects; and MCDA tools. This combination aims to constitute a robust framework for assessing sustainability, monitoring the progress towards the SDGs and supporting an enhanced process of decision-making.
Four central activities are expected: 1) Development of an SDG-oriented LCSA approach; 2) Development of an MCDA framework; 3) Combination in a tool oriented towards decision-makers; 4) Application of the tool through a set of illustrative case studies.

The solar refinery is a novel concept in which the combustion gases generated in a thermal power plant, rich in CO2 and water vapour, are reused as raw material to produce fuels through processes activated by solar power. the aim of this research line is the development of materials for new thermochemical systems to produce syngas (CO+H2) by combined CO2/H2O splitting. The temperatures required for the process (1000-1500 °C) are reached by means of concentrating solar thermal energy systems.

This Group is focused on the legal aspects of the Energy Transition. The achievement of such challenging goal imposes an important reassessment of the legal and institutional framework. It is needed to update classic categories and to add new legal tools and regulations on matters which still are at a research and demonstration stage.

Scientific data mining is catching attention since it can find answers in data where traditional methods are incapable. Lately, computing learning techniques are applied to new virtual scenarios such as medical and biological data. By applying datelining techniques, this project aims to find a relationship between the material and decisions made during the production of the devices to get decision support to produce high-efficient synthetic photosynthesis devices.