Presentation Profile
Which Recent Breakthroughs in Biofuel Have Been Strictly Dependent Upon Advancements in Digital Twin Informational Instrumentation
Currently Scheduled: 10/14/2026 - 1:00 PM - 2:00 PM
Room: Exhibit Hall Entrance
Main Author
Raj Shah - Koehler Instrument Company, Inc.
- Tivian Luu - Koehler Instrument Company, Inc.
Abstract:
Literature review in biofuel research has identified only a few advances in which digital twin instrumentation is essential rather than supplementary. Two cases meet this standard. First, Lawrence Berkeley National Laboratory is developing a biological digital twin to predict engineered microbial lipid production for jet fuel. The system combines mechanistic bioreactor models, machine learning, omics data, phenotypic imaging, and high-performance computing to resolve microbial population dynamics at production scale. Although still in development, this capability depends on the digital twin’s integrated architecture. Second, Nasef, Diaz, and Leal Quiros (2026) developed a digital twin for biomass pyrolysis in a top-lit updraft reactor. Their framework combines a validated finite-volume physics model with an XGBoost model to predict pyrolysis-front movement and peak solid temperature across multiple feedstocks. It enables real-time optimization that physics-only computational fluid dynamics models cannot practically achieve because of their computational cost. Beyond these cases, digital twins generally supplement advances in strain engineering, catalysis, and feedstock chemistry. Strict dependence therefore remains an emerging category concentrated in processes too complex for direct measurement or first-principles modeling to resolve in real time.
