3rd Edition of Global Conference on Biofuels and Bioenergy
September 17-19, 2026 | Rome, Italy
AI & Machine Learning Applications in Bioenergy
The adoption of AI & machine learning applications in bioenergy is transforming efficiency and decision-making in renewable fuel production. Machine learning algorithms optimize biomass conversion processes, predict energy yields, and improve feedstock logistics. AI-driven automation is enhancing reactor control, enzyme engineering, and microbial strain selection for biofuel synthesis. Predictive modeling is also aiding in policy development and market forecasting for bio-based energy. Real-time monitoring and process automation powered by AI are reducing operational costs while improving system adaptability. As big data analytics and deep learning advance, AI-driven solutions are expected to revolutionize bioenergy supply chains and production strategies.
Dai Yeun Jeong
Asia Climate Change Education Center, Korea, Republic of
Suzana Borschiver
Federal University of Rio de Janeiro, UFRJ, Brazil
Enrico Paris
CREA-IT, Italy
Maren Pauly
SGS Beta, United States
David Galan Madruga
Carlos III Health Institute, Spain
Manuel Alcazar Ortega
Valencia Polytechnic University, Spain
Stanislaw Dzwigaj
Sorbonne University, FranceSubmit your abstract Today
Title : A strategic technological roadmap for the future of biodiesel: Catalytic innovation and process intensification.
Suzana Borschiver, Federal University of Rio de Janeiro, UFRJ, Brazil
Title : Hydrogen production from contaminated residual biomass: An integrated gasification and SEWGS process study
Enrico Paris, CREA-IT, Italy
Title : Carbon-14 analysis: An accurate verification method for verifying the biogenic carbon content of biomass-derived fuels
Maren Pauly, SGS Beta, United States
Title : Application of vanadium and tantalum single-site zeolite catalysts in heterogeneous catalysis
Stanislaw Dzwigaj, Sorbonne University, France
Title : Robust MPPT-based design and simulation of integrated solar PV–hydrogen production systems
Elkhatib Kamal, Ecole Centrale de Nantes, France
Title : Aqueous/ EtOH: p-TsOH deep eutectic solvent assisted low-temperature deconstruction of miscanthus x giganteus biomass for production of bioethanol
Tirath Raj, University of Illinois Urbana Champaign, United States