Analysis, Characterization, Modeling, and Simulation Research
NREL's analysis, characterization, modeling, and simulation bioeconomy research and development capabilities support innovations and technologies with near- to long-term market opportunities.
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Our researchers develop and use analytical characterization tools, computational modeling, techno-economic and life cycle analyses, and machine learning (ML) and artificial intelligence (AI) approaches to translate successful laboratory-scale bioenergy pathways to commercial scale by leveraging:
- Analytical characterization of biomass feedstocks, intermediates, and products and other renewable resources
- Multiscale computational modeling and simulations (atomic scale to industrial scale) to increase the efficiency of feedstock conversion and design scale-up
- ML and AI approaches to generate new research hypotheses and improve technology pathways
- Advanced analyses and models to understand cost drivers and perform evaluations of the feasibility and efficiency of processes and technologies.
Biomass and Bioproduct Characterization
We have deep expertise in tools and techniques for compositional and structural characterization of feedstocks, biofuels, and bioproducts. This research capability provides insights on how to optimize the properties of lignocellulosic biomass and other feedstocks for conversion, utilization, and storage processes. Analytical characterization tools include:
- Mass spectrometry
- Nuclear magnetic resonance
- Liquid chromatography
- Gas chromatography-mass spectrometry
- Fourier-transform infrared spectroscopy.
Laboratory Analytical Procedures
NREL provides laboratory analytical procedures to provide validated methods for biofuels and pyrolysis bio-oils research:
Computational Modeling and Simulations
Computational modeling and simulations are powerful tools that inform design and enable prediction from the atomic to refinery scale. This research capability is applied to fundamental scientific questions—such as elucidating molecular-scale structure, property, and function relationships—and to more applied projects such as optimizing process scale-up.
Modeling and Simulation
Biofuels and Batteries Gain From the System Dynamics Behind the Research
How NREL scientists use modeling feedback loops to inform analysis and decisions across technologies.
Biorefinery Model Points to Better Method at Producing Co-Products
A next-generation biorefinery model holds the potential to produce biofuels and chemicals through a process that researchers say has proven to be more efficient in converting plant biomass.
From Organics to Fuels: Computational Models Can Accelerate and Scale Up Biomass Conversion
NREL computational models are solving key challenges for pathways to sustainable biofuels.
On the Ground in Colorado, NREL Is Simulating Sustainable Aviation Fuel Combustion During Flight
NREL models are helping the aviation industry fine-tune aviation fuel chemistry.
Deep Learning and Artificial Intelligence Approaches
We use ML and AI tools to accelerate data-mining from:
- Research databases (biological, chemical, polymer, physical)
- Multivariate spectroscopic, spectrometric, and imaging data
- Real-time measures of titers, rates, and yields in bioreactors.
Combining analytical and imaging tools, computer vision, and AI enables prediction of new products with new properties and process optimization for a range of energy technology applications.
Machine Learning Shortcuts Time Spent in Bench-Scale Research
Pick Your Polymer Properties and This NREL Tool Predicts How To Achieve Them With Biomass
Artificial intelligence speeds the discovery of more cost-effective, higher-performing polymers.
A Metabolic "Traffic Map": Machine Learning Lets Scientists Plot Complex Flow of Compounds Through Microbes
This research could help uncover the best routes for optimizing microbial-based biofuel production.
Machine Learning Method Could Speed the Search for New Battery Materials
New research demonstrates a machine learning technique that could more quickly surface batteries with the most desirable properties.
Economic, Life Cycle, and Market Analysis
NREL innovates using techno-economic and life cycle analyses and its Materials Flow through Industry modeling tool to quantify the economic impacts of bioenergy processes and technology pathways. The goal is to support the transition of bio-derived fuels, materials, and products from proof-of-concept to market by highlighting technical and cost drivers through rigorous process modeling.
Techno-Economic and Life Cycle Analyses Support Better Decision-Making
GCxN Company’s Feedstock Poised To Be More Economical and Versatile
NREL researchers are working with XanoFiber founder to replace corn stover in biofuel production with their purpose-grown energy crop.
New Device Architecture Enables Streamlined Production of Formic Acid From CO2 Using Renewable Electricity
An NREL research team developed a conversion pathway to produce formic acid from CO2 with high energy efficiency and durability while using renewable electricity.
NREL and Joby Aviation Partnership Spotlights Green Ride-Hailing Flight Services
NREL conducts life cycle assessment and blockchain research for burgeoning electric vertical take-off and landing aircraft industry.
NREL Develops Systematic Framework To Compare Performance of Plastics Recycling Approaches
Researchers provided a comparison of various technologies for closed-loop recycling, which allow for the reuse of plastic through mechanical or chemical processing.
Contact
Contact Michael Crowley to leverage these capabilities and expertise or learn more about partnering with us.
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