If you are a fuel producer dealing with high carbon emissions in flight — this project developed thermochemical routes that convert forestry residues into biofuels. This allows for the creation of drop-in fuels for hard-to-decarbonize sectors.
Scaling Up Sustainable Biofuel Production from Agricultural and Forestry Waste
Imagine turning forest scraps and leftover straw into high-quality fuel for planes and ships. Instead of just guessing where to build factories, this work uses AI to find the best locations and the cheapest waste materials. It's like creating a recipe book and a map for building industrial-scale green fuel plants.
What needed solving
Producing biofuels at an industrial scale is often too expensive or technically risky. Companies struggle to find the right biomass sources and the most cost-effective locations for their plants.
What was built
A complete pyrolysis biomass-to-fuel chain and an AI-driven predictive tool for biomass demand and plant location.
Who needs this
Who can put this to work
If you are a supplier dealing with strict shipping emission regulations — this project developed a way to upgrade pyrolysis oil and gasification products. This provides a renewable alternative to petroleum for heavy-duty sea transport.
If you are a waste manager dealing with large volumes of barley straw — this project developed AI-driven predictive models to optimize feedstock selection. This turns low-cost residual biomass into a valuable industrial feedstock.
Quick answers
How does this affect the cost of biofuel production?
The project focuses on using low-cost residual biomass, such as barley straw and forestry residues, to improve economic viability. It uses AI-driven models to forecast demand and enhance cost-efficiency.
Is this technology ready for industrial scale?
The project aims to bring these technologies to TRL5 and develops scale-up strategies based on advanced modeling and pilot-scale demonstrations. It identifies technical constraints and operational difficulties for moving from pilot to larger-scale plants.
What are the IP and licensing options for the AI tools?
Based on available project data, the project developed a user-adjustable AI tool for biomass demand and plant location, which is currently undergoing final validation.
How does it comply with EU regulations?
The AI-driven predictive models are specifically aligned with EU biofuel policies to ensure the developed value chains are sustainable and compliant.
Can these fuels be mixed with existing petroleum products?
Yes, the project investigates the blending of pyrolysis oil and gasification-based advanced biofuels through a petroleum company partner.
Who built it
The consortium is highly industry-oriented with a 50% industry ratio, consisting of 6 partners across 5 countries. With 3 industrial partners (including 2 SMEs) and 3 research entities, the project balances academic rigor with commercial application, specifically involving a petroleum company to test fuel blending.
Contact the National Centre for Research and Technological Development (REC) in Greece.
Talk to the team behind this work.
Contact us to access the AI-driven biomass location tools and scale-up guidelines.