Multiscale Computational Modeling Enables Bioenergy Technology Innovation and Scale-Up
The Chemical Catalysis for Bioenergy Consortium (ChemCatBio) brings together expertise across multiple U.S. Department of Energy (DOE) national laboratories to tackle challenges in bioenergy, ranging from the discovery of new catalytic materials to de-risking scale-up of reactor systems and processes. The end goal is developing commercially viable routes to sustainable aviation fuel (SAF). To enable this range of technology innovation and development, ChemCatBio employs computational science in collaboration with the Consortium for Computational Physics and Chemistry (CCPC), also funded by DOE’s Bioenergy Technologies Office. The CCPC provides theory-based insights on catalyst innovations as well as fundamental scientific understanding of critical mass and heat transfer and reaction chemistry. This collaboration has led to breakthroughs in catalyst design and process improvements.
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The CCPC uses a multiscale approach to provide insight across the breadth of ChemCatBio technologies, including modeling at atomic, meso-, and reactor scales. Atomic-scale modeling of reactions at catalyst surfaces can provide fundamental insight into catalyst performance and drive predictive synthetic innovations. The CCPC uses DOE’s most advanced high-performance computers to simulate the vast parameter space of catalysts, which can be coupled with artificial intelligence and machine learning to provide insights into promising catalysts for targeted conversion pathways.
Mesoscale modeling focuses on the mass and heat transfer for realistic catalyst particles, which is a critical component of ChemCatBio’s current thrust on engineered catalyst design and forming. The CCPC provides guidance to optimize these engineered forms (e.g., target pore distribution) and better understand performance characteristics (e.g., deactivation via coking). By leveraging the expertise of catalyst suppliers in engineered catalyst development, the CCPC is able to translate industry know-how into actionable guidance for ChemCatBio.
CCPC reactor modeling uses lab-scale data to predict conversion efficiencies of SAF blendstocks in realistic reactor geometries. By incorporating kinetic reaction rates, the model allows predictive simulations that provide foundational information for scaling up the catalytic process and optimizing reactor design and controls. By avoiding time and hardware expenses associated with chemical reactors, models yield results at much lower costs than Edisonian approaches for scale-up.
A variety of resources on computational capabilities and associated ChemCatBio tools can be found online:
We encourage you to connect with ChemCatBio and CCPC leadership (email Jim Parks at parksjeii@ornl.gov). By pooling our talents and capabilities, we can reduce the cost and time to move your catalyst-driven technology from discovery to scale-up.
Let's get started!
Jim Parks, CCPC Principal Investigator
Fred Baddour, Scientist, National Renewable Energy Laboratory
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