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.
Title : Quality variation in market biofuels and the effect on tailpipe emissions
Nick Molden, Emissions Analytics, United Kingdom
Title : Revolutionizing bioplastics with yeast cell factories
Susan Newman, Integrated Lipid Biofuels, United States
Title : Combustion performances of advanced cooking stoves using woody and herbaceous pellets as fuel
Magnus Stahl, Karlstad University, Sweden
Title : Green hydrogen: Driving sustainable aviation's future
Sanjeev Gajjela, Tomato Sustainables LTD, United Kingdom
Title : Energy transition and neo-industrialization in Brazil - Windows of opportunities
Suzana Borschiver, Federal University of Rio de Janeiro, UFRJ, Brazil
Title : Ultra modern patented technology to convert agriwaste/MSW/ slaughter house effluent/lake waste/high cod distillery spent wash to 99% pure renewable hythane (hydrogen+methane)
Atul Saxena, Growdiesel Ventures Limited, India