FUELPHORIA will set-up and test value chains for advanced biofuels and renewable non-biological fuels in Europe, establishing sustainable, competitive and secure value chains for advanced biofuels. The project contributes to the objectives of the European Commission’s 2022 REPower EU Plan “to scale-up renewables, achieve electrification, and replace fossil-based heat and fuel in industry, buildings, and the transport sector”. A versatile range of processes including but not limited to chemical, biological, and photo-biological processes will serve as the scaffold to convert different feedstocks into an array of renewable fuels of quality specifications defined by end-users in the transport (e.g. maritime and road) and in the power (e.g. gas or oil-fired thermal plants) production sector. Four demonstrators in Spain, Belgium, and Greece will serve as the testing grounds for the nine value chains solving any technical issues related to the conversion of feedstock into renewable fuels and preparing their market entry, by incorporating innovative business models. At the end of the project, a set of policy recommendations for EU policymakers will be developed with the objective of showcasing how the results of the project might contribute to a sustainable transition of the EU energy systems.

MKLab is responsible for creating an algae monitoring and operation system. The monitoring system will be implemented by capturing the spectra of the ponds and by utilizing input from satellites, cameras installed in the ponds, and Unmanned Aerial Vehicles (UAVs). The images and the novel annotated datasets will serve as a scaffold for collecting information about the algae growth from early steps. Generative Artificial Networks (GANs) will be used to make estimations about the biodiesel production optimizing the overall production process, and by creating early warnings in case of a low biodiesel production forecasting. Finally, the images and the input variables will be used to create a visualization platform to improve the overall monitoring process.