Predictive Models and High-Performance Computing as Tools to Accelerate the Scaling-Up of New Bio-Based Fuels Workshop: Summary Report

New report describes best practices for using mathematical models to accelerate the scale-up of biofuels.

Bioenergy Technologies Office

November 30, 2020
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Predictive Models and High-Performance Computing as Tools to Accelerate the Scaling-Up of New Bio-Based Fuels Workshop: Summary Report

The Predictive Models and High-Performance Computing as Tools to Accelerate the Scaling-Up of New Bio-Based Fuels Workshop, coordinated by the Bioenergy Technologies Office, gathered bioenergy stakeholders across diverse sectors and technology areas, including representatives from industry, academia, and government. One hundred seventy-five participants provided their input regarding best practices for utilizing mathematical modeling tools across multiple scales to reduce technology uncertainty and accelerate scaling-up of biorefinery/chemical production equipment and optimize operations.

This workshop summary report summarizes stakeholder input related to discussion objectives including:

  • Understanding how modeling tools can be effectively utilized in conjunction with operational data from bench, pilot, and demonstration facilities to augment and accelerate scale-up and integration efforts
  • Identifying synergies, challenges, and gaps in current modeling efforts and data collection methods across technologies
  • Utilizing artificial intelligence and machine-learning computational technologies together with well-instrumented systems to assess and validate models and develop transfer functions to represent scale-up accurately
  • Developing best practices and publicly available multiscale modeling tools to reliably represent integrated equipment and operations involved in biorefineries.
  • This report summarizes the results of a virtual workshop sponsored by the Bioenergy Technologies Office held on June 11–12, 2020. The workshop discussed best practices for utilizing mathematical modeling tools across multiple scales to reduce technology uncertainty and accelerate scaling-up of biorefinery/chemical production equipment and optimize operations.