Feedstock Technologies Program

The U.S. Department of Energy's Bioenergy Technologies Office (BETO) hosted its 2023 Project Peer Review on April 3‒7, 2023. The presentations from the Feedstock Technologies session are available to view below. For more information, including a complete schedule, view the 2023 Project Peer Review Agenda.

Feedstock Technologies Program
Title	Speaker	Organization
Technology Area Introduction	Liz Burrows & Alex Jansen	BETO
Feedstock Supply Chain Analysis	David Thompson	Idaho National Laboratory
Supply Scenario Analysis	Matt Langholtz	Oak Ridge National Laboratory
Bioenergy Feedstock Library	Rachel Emerson	Idaho National Laboratory
Resource Mobilization	Pralhad Burli	Idaho National Laboratory
Triple bottom line sustainability indicators for spatially-explicit, multi-feedstock, multi-technology waste-to-energy supply chains	André Coleman	Pacific Northwest National Laboratory
Global impacts of enhancing domestic ecosystem carbon sinks	Patrick Lamers	National Renewable Energy Laboratory, Pacific Northwest National Laboratory
Benefits and Land Use Effects of US Energy Crop-based Carbon Banking	Debo Oladosu	Oak Ridge National Laboratory
Technology Area Daily Intro	Dana Mitchell	BETO
Roads to Removal	Roger Aines	Lawrence Livermore National Laboratory
Next-Generation Feedstocks for the Emerging Bioeconomy	DK Lee	University of Illinois at Urbana-Champaign
Sustainable Herbaceous Energy Crop Production in the Southeast United States	Ted Wilson	Texas A&M AgriLife Research
Cover crop valorization for biofuels and products	Bill Smith	Idaho National Laboratory
Maximizing the value of late year cover crops in the Pacific Northwest	Daniel Santosa	Pacific Northwest National Laboratory
National availability and costs of cover crops managed as biofuel feedstocks	Esther Parish	Oak Ridge National Laboratory
Municipal Solid Waste Preprocessing and Decontamination	Vicki Thompson	Idaho National Laboratory
Artificial Neural Network for MSW Characterization	Carson Potter	AMP Robotics
Decontamination of Non-recyclable MSW and Preprocessing for Conversion to Jet Fuel	Tim Saunders	GTI
Advanced Sensing for Characterization and Sorting of Non-Recyclable Plastics Using Sensor Fusion with Artificial Intelligence	Nalin Kumar	UHV Technologies
High Precision Sorting, Fractionation, and Formulation of Municipal Solid Waste for Biochemical Conversion	Maobing Tu	University of Cincinnati
AI-Enabled Hyperspectral Imaging Augmented with Multi-Sensory Information for Rapid/Real-time Analysis of Non-Recyclable Heterogenous MSW for Conversion to Energy	Lokendra Pal	North Carolina State University
Integrated LIBS-RAMAN-AI System for Real-Time, In-Situ Chemical Analysis of MSW Streams	Carlos Romero	Lehigh University
Technology Area Daily Intro	Chenlin Li & Mark Elless	BETO
Thermal Conditioning for Development of Co-products for Carbon Cycle Sequestration	Jordan Klinger	Idaho National Laboratory
Value-added biocomposite production using off-spec biomass from mechanical fractionation	Erin Webb	Oak Ridge National Laboratory, Idaho National Laboratory
Polymer products from Lignin through de-aromatization and COOH functionalization	Michael Kent	University of South Carolina
Value-added process intensification in the supply chain	Bradley Wahlen	Idaho National Laboratory
Biomass Size Reduction, Drying and Densification	Neal Yancey	Idaho National Laboratory
Advancing Forest Biorefineries Towards Commercial Applications through Fractionation of Biomass Wastes	Luke Williams	Idaho National Laboratory, National Renewable Energy Laboratory
Characterization of Mechanical Biomass Particle-Particle and Particle-Wall Interactions	Hojae Yi	Pennsylvania State University, University Park
Enhanced Feedstock Characterization and Modeling to Facilitate Optimal Preprocessing and Deconstruction of Corn Stover	David Hodge	Montana State University
SWIFT: Single-pass, Weather Independent Fractionation Technology for Improved Property Control of Corn Stover Feedstock	Kevin Shinners	University Of Wisconsin
Sulfur Profiling in Pine Residues and Its Impact on Thermochemical Conversion	Jian Shi	University of Kentucky
Modeling Feedstock Performance and Conversion Operations	Michael Ladisch	Purdue University
Machine learning based modeling framework to relate biomass tissue properties with handling and conversion performances	Sudhagar Mani	University of Georgia Research Foundation Inc.
Real time, Integrated Dynamic Control Optimization to Improve the Operational Reliability of a Biomass Dryer	Damon Hartley	Idaho National Laboratory