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Results for “Uruguay Fuel”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Preliminary Assessment of Alternate Chlorination Process

Recent advancements towards low-temperature chlorination with disulfur dichloride and thionyl chloride of Al-clad used nuclear fuel (UNF) have been summarized for work control technical expertise development at the Savannah River National Laboratory (SRNL). This technique aims to provide a more effective method for separating U from cladding or alloying elements. A preliminary assessment determined needs for unit operation developments in regard to reaction kinetics and reagent quality. Furthermore, an analysis of an Uruguay fuel plate (U-Al x fuel meat), with low-enriched uranium (LEU) and located at SRNL, was a potential candidate for future experiments with lightly irradiated (0.08% burnup) fuel to test for chlorination. Future proposed work would involve kinetic studies, reagent quality assessments, and the machining of the fuel plate down to an appropriate bench-scale size and testing the effectiveness of low-temperature chlorination on the fuel plate.

Chlorination↗
DOE OSTI2025

Influence of particle size on NIR spectroscopic characterization of sorghum biomass for the biofuel industry

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum (Sorghum bicolor), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

09 BIOMASS FUELS↗