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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Massachusetts and New England SAF Study [Slides]

The Massachusetts Port Authority established a project to assess feedstock availability to produce SAF near 10 New England airports. A techno-economic analysis was conducted to determine renewable fuels and SAF that could be produced from these feedstocks. The resource assessment found municipal solid waste, woody biomass, and food waste were most abundant. The techno-economic analysis found that potential fuel production varied widely based on location, feedstock, and technology production pathway. An assessment of infrastructure readiness to handle SAF was conducted. It was determined that SAF produced outside of New England should be blended in other regions with more capacity and flexibility and delivered to New England as Jet A is today. For SAF produced in New England, there are terminals that have existing infrastructure to receive, blend, and distribute SAF/Jet A blends.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

BETO 2021 Peer Review - Strategic Analysis Support WBS 4.1.1.30

Strategic Analysis Support. The objective of the NREL strategic support project is to provide sound, unbiased, and consistent analyses to inform the strategic direction of the DOE BETO office. This project addresses key technological questions, provides critical data needed to inform strategy, and highlights barriers, gaps and data needs in support of the DOE BETO's mission to improve the affordability of bio-based fuels and products. This task employs various quantitative (techno-economic analysis, TEA) and qualitative (gap analysis) approaches to allow for direct comparisons of biomass conversion technologies across a wide slate of processing platforms and products. Furthermore, this project develops and utilizes novel analyses beyond traditional biorefinery focused TEA/LCAs to identify both technical (e.g., in sustainable design) and non-technical (e.g., in value proposition) barriers, as well as to outline mitigation strategies and R&D needs for emerging technologies. Additionally, the project is tasked with evaluating drivers that support the growing bio-economy, which is achieved by the development and public release of tools to advance the understanding and facilitate comparisons of socio-economic impacts along the supply chain. Critical to the success of this project is the development of defensible methodologies, analyses, and tools that are publicly available to support stakeholders and bioeconomy growth. To develop such high-quality analyses, the biggest challenge to this project, as with most analysis focused projects, is the availability and reliability of the underlying data. Therefore, the project team works extensively with key stakeholders (e.g., policy makers, bioenergy technology developers, and investors) in developing and reviewing the results of these analyses to overcome this challenge. Any remaining uncertainties associated with the analysis efforts are clearly defined and quantified.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Scaling Spectroradiometric Monitoring to an Algal Factory Farm

This report briefly summarizes the spectroradiometric approach adapted and deployed as part of Task 4.2 within the BETO-funded D evelopment of I ntegrated S creening, C ultivar O ptimization, and V erification R esearch (DISCOVR) project, specifically task 4, “Increase crop protection and culture stability.” The goals of this subtask were to monitor ponds cultivating a variety of DISCOVR strains at the Arizona Center for Algae Technology and Innovation (AzCATI) with spectroradiometric technology to: • Predict biomass concentrations with high temporal frequency. • Optimize automated analysis methods that enabled feedback for pond automation strategies • Identify pond crash indicators from primary (pest/predator) and indirect (change in algal host) spectroscopic signals with potential for early treatment/intervention • Provide recommendations for technology deployment at scale. This report fulfills the deliverable requirement for the final bullet “provide recommendations for technology deployment at scale”.

09 BIOMASS FUELS↗
DOE OSTI2020

Algae-Based Beneficial Re-use of Carbon Emissions Using a Novel Photobioreactor: a Techno-Economic and Life Cycle Analysis

Despite the many advantages of microalgae, the feasibility of large-scale cultivation requires significant amounts of carbon dioxide (CO 2 ) to enable high growth rates. A synergistic union typically proposed for the supply of CO 2 is the coupling of algal cultivation with emissions from power plants. This study investigates the sustainability of a novel microalgae platform coupled with coal-based flue gas. The proposed system consists of a novel photobioreactor (PBR) for the production of biomass followed by a two-stage dewatering process. Here, a systems model, which quantifies the CO 2 and energy consumption of the proposed system, was developed and the minimum biomass selling price (MBSP) was determined by a techno-economic analysis (TEA). TEA results indicate that a facility with the capacity to capture 30% of the emissions from a 1 MW power plant requires a biomass production of 1280 metric tonnes per year, which when scaled to a nth of kind facility, can produce biomass at a MBSP of $2322 per tonne. The environmental impact of the proposed facility was determined by a life cycle assessment methodology and results indicate a carbon capture potential of 1.16 x 10 4 metric tons of CO 2 equivalent. In addition, an energy analysis indicates a desirable net energy ratio of 0.1, which is lower than conventional PBRs. Discussion focuses on the requirements to reduce biomass production cost, including research investment areas for increasing productivity while decreasing energy requirements.

09 BIOMASS FUELS↗
DOE OSTI2022

CFD-DEM Modeling of Autothermal Pyrolysis of Corn Stover with a Coupled Particle- and Reactor-Scale Framework

Autothermal operation of fast pyrolysis is an efficient process-intensification technique wherein exothermic oxidation reactions are used to overcome the heat-transfer bottleneck of conventional pyrolysis. The development of accurate, reliable modeling toolsets is imperative to generating a deeper understanding of biomass autothermal pyrolysis systems to support scale-up and industrial deployment. This modeling effort describes the development of single-particle and reactor models which incorporate detailed reaction schemes and simultaneous exothermic oxidation reactions. The particle-scale model was parameterized for corn stover feedstock with particle morphology, density, ash content, and biopolymer composition, all of which impact the emergent conversion characteristics during pyrolysis. Results were then used to parameterize a reactor-scale autothermal pyrolysis model, which was developed using a coarse-grained computational fluid dynamic-discrete element method. The simulation results compared well with experimental results, with the predicted bio-oil, light gas, and biochar yield within 3.0 wt% of the experimental yields. Further analyses were performed to test the influence of equivalence ratio, biomass injection position, and particle size distribution on autothermal pyrolysis. The analysis of the physio-chemical properties of the fluid and solid phase inside the reactor and at the reactor outlet help reveal important process interactions of autothermal pyrolysis.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2021

A simplified integrated framework for predicting the economic impacts of feedstock variations in a catalytic fast pyrolysis conversion process

Feedstock attributes of lignocellulosic biomass, such as particle size, compositional makeup, and moisture content, can vary substantially even within pre-processed materials and have a significant effect on conversion in fast pyrolysis-based processes. However, the economic impacts of these attributes are not well understood. To address this, biomass deconstruction phenomena captured with a versatile particle-scale simulation were linked to techno-economic impacts via reduced-order models. Parametric analysis of the particle-scale model, which was validated using literature data, was used in combination with multiple linear regression models to develop correlations between feedstock attributes and yields of pyrolysis oil, gas, and char. Yields were then correlated with the minimum fuel selling price (MFSP) using a techno-economic model, bridging the gap between physics-based biomass conversion simulations and predictions of MFSP for a catalytic fast-pyrolysis process. Empirical correlations derived from the literature regarding the impact of mineral matter (ash) on oil yield were also considered. The model correlations deployed in the integrated framework capture the impacts of variation in feedstock attributes on the MFSP. Variations in ash were shown to have the biggest impact, varying MFSP by -13%/+22% due to catalytic effects and lower relative amounts of convertible lignocellulosic material. It was also found that, if ash can be controlled to low levels, the increased extractives in forest residues can help compensate for some yield losses associated with increased ash. As a result, other inputs considered (particle size, moisture content, and reactor temperature) had relatively negligible effects on process economics within the ranges analyzed considering particle-scale effects alone.

BIOMASS FUELS↗
DOE OSTI2022

At-Line Sampling and Characterization of Pyrolytic Vapors from Biomass Feedstock Blends Using SPME-GC/MS-PCA: Influence of Char on Fast Pyrolysis

Solid-phase microextraction (SPME) coupled with GC-MS analysis was used for at-line sampling of pyrolytic vapors produced during fast pyrolysis of biomass. The pure and binary blends of switchgrass (SWG) and pine harvest residues (PT6) were used as feedstock. Sequential SPME sampling allowed for monitoring of changes in the pyrolysis vapors as char accumulated in the fluid bed. The concentration and composition of the vapors desorbed from the SPME fibers were investigated using GC-MS, and the data sets were then analyzed using principal component analysis (PCA) to compare the composition of the pyrolysis vapors over the course of the pyrolysis run. The chemical composition of both carbohydrate and lignin fragments varied as the char builds up in the reactor bed. Fragments derived from cellulose and xylan included anhydrosugars, furans and light oxygenated compounds. Lignin fragments included methoxyphenols, phenolic ketones and aldehydes, low molecular weight aromatics. The composition of the carbohydrate fragments changed more than the lignin fragments as the char build-up in the fluid bed. This combination of SPME-GC/MS-PCA were a novel, easy and effective method for measuring the composition and changes in the composition of pyrolysis vapors during the fast pyrolysis process. Here, this work also highlighted the effect of char build-up on the composition of the overall pyrolysis vapors.

09 BIOMASS FUELS↗
DOE OSTI2023

An algal-bacterial symbiotic system of carbon fixation using formate as a carbon source

The commercial implementation of algal cultures for efficient capture of CO 2 from fossil-derived flue gases is not a reality yet due to several major challenges, particularly low gas-liquid mass transport efficiency along with loss of productivity due to pests. This study has explored an algal-bacterial symbiotic system to utilize formate as a carbon source. The algal-bacterial assemblage, after a 400-day adaptive evolution using the formate medium, has demonstrated a new capability to assimilate both formate and CO 2 to promote biomass production. The formate tolerant culture system also addresses CO 2 mass transfer limitation under high light conditions. Continuous cultivation of the assemblage on formate led to a carbon capture efficiency of 90 % with a biomass concentration of 0.92 g L -1 and biomass productivity of 0.31 g L -1 day -1 , and the system did not require CO 2 aeration. In addition, 13 C isotope tracing, proteomics, and microbial community analysis reveal a uniquely evolved community for formate utilizations and robust biomass synthesis, in which Chlorella sorokiniana is a dominant phototroph companying with highly diverse bacterial subpopulations. In the presence of formate, evidence has shown that Chlorella is able to form mutualism with formate utilizing bacteria in semi-continuous cultures, leading to a highly effective photomixotrophic metabolism. Furthermore, this study demonstrates a new route of using electrochemical-derived formate to support mutualistic algae-bacteria biorefinery while the formate as an alternative carbon source could repel pests for outdoor algal cultivations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain

A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Material Characterization-Based Wear Mechanism Investigation for Biomass Hammer Mills

Biomass, as harvested, is composed of inorganic compounds both intrinsically and extrinsically and can be abrasive. The present study investigates the wear modes and mechanisms of two types of blades of hammer mills used in biomass size reduction (impacting the particle size and distribution) and densification (impacting the size, shape, and density). The dominant wear modes for the stage 1 steel blades are determined to be erosive and polishing wear. For the stage 2 blades with a carbide weld overlay, the main wear mechanisms are erosion and fracture. Partial replacement of Co by Fe in the outer layer of the carbide grits, likely induced by diffusion during high-temperature welding, has been correlated to the observed microcracking. Finally, the microcracking is believed to weaken the grit strength and fracture toughness to make the overlay prone to fracture and erosion due to repetitive contact with the inorganic contents in chopping biomass.

42 ENGINEERING↗
DOE OSTI2023

Effect of nitrogen management in cultivation on the stability and microbial community of post-harvest Monoraphidium sp. algae biomass

Seasonal variation in algae biomass productivity is a significant obstacle to the economical production of fuels and chemicals from algae biomass. Long-term storage is necessary to guarantee a constant supply to a conversion facility and harvested microalgae biomass (20% solids) is subject to degradation immediately upon harvesting as a result of aerobic instability. Ensiling has been successfully used to preserve microalgal biomass, but biomass attributes that contribute to successful storage are poorly understood. Storage studies of Monoraphidium sp. biomass indicate a strong correlation between nitrogen management in algae cultivation and stability of post-harvest algae biomass in 30 days of anaerobic storage. Algae cultivated with periodic nitrogen addition stored poorly (> 20% loss, dry basis), while algae biomass from cultures that were allowed to continue growing after nitrogen depletion was more stable in storage (8% loss, dry basis). A follow-up study aimed at exploring the relationship between nitrogen management in cultivation and stability of post-harvest algae biomass stored Monoraphidium biomass cultivated in nitrogen deplete and nitrogen replete conditions, as well as with blends of each in varying ratios. Monoraphidium biomass from nitrogen replete cultivations experienced the largest degradation (24%, dry basis), while nitrogen deplete biomass experienced the least (10%, dry basis). Dry matter loss experienced during storage of blends was positively correlated with the amount of nitrogen replete biomass that a blend contained. Further, the composition of the post-storage algae microbial community was also strongly affected by cultivation conditions, with Clostridia species being more prevalent in stored biomass obtained from nitrogen replete cultivations. Nitrogen management has long been known to influence algae biomass productivity and biochemical composition; here, we demonstrate that it also strongly influences the stability of post-harvest algae biomass in anaerobic storage.

09 BIOMASS FUELS↗
DOE OSTI2025

BiCRS LCA Tool [SWR-26-005]

This is a biomass carbon removal and storage (BiCRS) life cycle analysis (LCA) tool. This version can be used to estimate the impacts of biochar burial BiCRS pathways.

Ware, Anne "Liz" [National Renewable Energy Labora↗
DOE OSTI2026

Improved Microalgal Carbon Utilization Efficiency via Integrated CO 2 Electro-Conversion to Formate and Microalgal Sequestration

This project developed a process to convert industrial carbon dioxide (CO 2 ) emissions into high-value, sustainable products through genetically engineered algae cultivation. While traditional microalgae cultivation depends on sparging CO 2 gas through water, this method is often inefficient because much of the gas escapes into the atmosphere before the algae can consume it. To overcome this challenge, the project designed an integrated system that first uses a CO 2 to formic acid electrolyzer to convert CO 2 into water-soluble formic acid/formate, then introduces formic acid/formate into the algae pond for cultivation, which allows the algae to access and utilize nearly all of the provided carbon, greatly increasing the efficiency of carbon utilization. The project team has successfully scaled up the CO 2 to formic acid electrolyzer from lab-scale to 1000 cm² and demonstrated industrially relevant current densities with the scaled-up electrolyzers using a CO 2 source that simulates industrial CO 2 waste.

42 ENGINEERING↗
DOE OSTI2021

Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains.

09 BIOMASS FUELS↗
DOE OSTI2022

Determining the Adsorption Energetics of 2,3-Butanediol on RuO 2 (110): Coupling First-Principles Calculations With Global Optimizers

As climate change continues to pose a threat to the Earth due to the disrupted carbon cycles and fossil fuel resources remain finite, new sources of sustainable hydrocarbons must be explored. 2,3-butanediol is a potential source to produce butene because of its sustainability as a biomass-derived sugar. Butene is an attractive product because it can be used as a precursor to jet fuel, categorizing this work in the alcohol-to-jet pathway. While studies have explored the conversion of 2,3-butanediol to butene, little is understood about the fundamental reaction itself. We quantify the energetics for three pathways that were reported in the literature in the absence of a catalyst. One of these pathways forms a 1,3-butadiene intermediate, which is a highly exothermic process and thus is unlikely to occur since 2,3-butanediol likely gets thermodynamically trapped at this intermediate. We further determined the corresponding energetics of 2,3-butanediol adsorption on an ensemble of predetermined binding sites when it interacts with a defect-free stoichiometric RuO 2 (110) surface. Within this ensemble of adsorption sites, the most favorable site has 2,3-butanediol covering a Ru 5–coordinated cation. This approach is compared to that obtained using the global optimization algorithm as implemented in the Northwest Potential Energy Surface Search Engine. When using such a global optimization algorithm, we determined a more favorable ground-state structure that was missed during the manual adsorption site testing, with an adsorption energy of -2.61 eV as compared to -2.34 eV when using the ensemble-based approach. We hypothesize that the dehydration reaction requires a stronger chemical bond, which could necessitate the formation of oxygen vacancies. As such, this study has taken the first step toward the utilization of a global optimization algorithm for the rational design of Ru-based catalysts toward the formation of butene from sustainable resources.

09 BIOMASS FUELS↗
DOE OSTI2021

Reduced ecosystem resilience quantifies fine-scale heterogeneity in tropical forest mortality responses to drought

Sensitivity of forest mortality to drought in carbon-dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, in this study, the potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site-year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave-one- out cross-validation R 2 = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine-scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high-resolution remote sensing to “fingerprint” forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought.

59 BASIC BIOLOGICAL SCIENCES↗