Data mining techniques applied to synthetic photosynthesis
Mentor: Abraham Duarte Muñoz
Phone: (+34) 916655073
University: Universidad Rey Juan Carlos
Partner Host Institution: IMDEA Energy - Photoactivated Processes Unit - Researcher Víctor de la Peña ( - 2nd year of applied research compulsory. This research line has been developed together with IMDEA Energy. The second year of applied research in IMDEA Energy is compulsory under this line.
Keywords: datamining, scientific data, clustering, metaheuristics, smart catalysis, renewable fuels

Data mining techniques applied to synthetic photosynthesis

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. With the aim to fight global warming, carbon capture technologies have emerged and, with them, materials and production techniques to get devices that capture carbon dioxide from the air or water, fuelled with solar energy. The performance/efficiency of the artificial photosynthesis is still modest (below to 1%). Therefore is necesary to found new materilas that improve this photocatalytic performance. 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.

Departament: Computing Science, Computer Architecture, Programming Languages and Systems and Statistics and Operative Investigation
Research Group: Group for Research in Algorithms For Optimization (GRAFO)
More Information:
Relevants projects on the area:
Relevants publications on the area: 1.- Serrano-Luján, L., Cadenas, J. M., Faxas-Guzmán, J., & Urbina, A. (2016). Case of study: Photovoltaic faults recognition method based on data mining techniques. Journal of Renewable and Sustainable Energy, 8(4), 043506.
2.- Sánchez-Oro, J., A. Duarte, and S. Salcedo-Sanz, "Estimating the Spanish Energy Demand Using Variable Neighborhood Search", Advances in Artificial Intelligence: Lecture Notes in Artificial Intelligence, pp. 341–350, 2016