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 : Revolutionizing bioplastics with yeast cell factories
Susan Newman, Integrated Lipid Biofuels, United States
Title : Quality variation in market biofuels and the effect on tailpipe emissions
Nick Molden, Emissions Analytics, United Kingdom
Title : Combustion performances of advanced cooking stoves using woody and herbaceous pellets as fuel
Magnus Stahl, Karlstad University, Sweden
Title : Ultra modern patented technology to convert agriwaste MSW slaughter house effluent lake waste high cod distillery spent wash to 99pure renewable hythane (hydrogen and methane)
Atul Saxena, Growdiesel Ventures Limited, India
Title : Green hydrogen: Driving sustainable aviations 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