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




Technology Area Introduction

Liz Burrows & Alex Jansen


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


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


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


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