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.
Susan Newman
Integrated Lipid Biofuels, United States
Dai Yeun Jeong
Asia Climate Change Education Center, Korea, Republic of
Atul Saxena
Growdiesel Ventures Limited, IndiaSubmit your abstract Today
Title : Green management of enterprises as a response to climate change
Dai Yeun Jeong, Asia Climate Change Education Center, Korea, Republic of
Title : Two-stage fermentation for converting waste CO2 into omega-3 fatty acids and biodiesel
Preeti Mehta Kakkar, Amity University Noida, India
Title : Research on high-temperature hydrogen-producing fungi assisted by AI
Zhikang Yang, Fujian Normal University, China
Title : In-Situ extraction and (trans)esterification of high-free fatty acid rice bran oil using synthesized heterogeneous catalysts
Deepika Singh, Panjab University, India
Title : Low-frequency ultrasonication as a dual-purpose strategy for biomass and macromolecule enhancement in Chlorella sp.
Simran Maratha, Central University of Rajasthan, India