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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Sustainable production of ammonia fertilizers from biomass

Nitrogen is the most limiting plant nutrient. Inexpensive natural gas has substantially reduced costs of ammonia fertilizer for intensive agriculture in the developed world, but its excessive use negatively impacts downstream ecosystems. By contrast, the availability of ammonia fertilizer is a major economic bottleneck for agriculture in developing countries. A dedicated lignocellulosic biomass crop could supply sufficient substrate to generate optimal nitrogen fertilizer on less than 5% of a grower’s food crop acreage. However, reimagining ammonia generation using biomass could significantly enhance sustainable agricultural productivity in developing countries when combined with emerging catalytic technologies.

09 BIOMASS FUELS↗
DOE OSTI2022

A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the initial inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass' characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory's biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs.

09 BIOMASS FUELS↗
DOE OSTI2022

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗
DOE OSTI2023

Applications of biomass-derived solvents in biomass pretreatment – Strategies, challenges, and prospects

We report biomass pretreatment is considered a key step in the 2nd generation biofuel production from lignocellulosic biomass. Research on conventional biomass pretreatment solvents has mainly been focused on carbohydrate conversion efficiency, while their hazardousness and/or carbon intensity were not comprehensively considered. Recent sustainability issues request further consideration for eco-friendly and sustainable alternatives like biomass-derived solvents. Carbohydrate and lignin-derived solvents have been proposed and investigated as green alternatives in many biomass processes. In this review, the applications of different types of biomass pretreatment solvents, including organic, ionic liquid, and deep eutectic solvents, are thoroughly discussed. The role of water as a co-solvent in these pretreatment processes is also reviewed. Finally, current research challenges and prospects of utilizing biomass-derived pretreatment solvents for pretreatment are discussed. Given bioethanol's market potential and increasing public awareness about environmental concerns, it will be a priority adopting sustainable and green biomass pretreatment solvents in biorefinery.

09 BIOMASS FUELS↗
DOE OSTI2020

Validation and application of a multiphase CFD model for hydrodynamics, temperature field and RTD simulation in a pilot-scale biomass pyrolysis vapor phase upgrading reactor

Accurate prediction of transport phenomena is critical for VPU reactor design, optimization, and scale-up. The current study focused on the validation and application of a multiphase CFD model within an open-source code MFiX for hydrodynamics, temperature field, and residence time distribution (RTD) simulation in a non-reacting circulating fluidized bed riser for biomass pyrolysis vapor phase upgrading (VPU). First, an Eulerian-Eulerian approach three-dimensional CFD model was employed to simulate the pilot-scale VPU riser on the supercomputer Joule. Excellent quantitative agreement between experimental and simulated results was achieved for pressure drops and temperature field in a range of operating conditions. Then the validated multiphase CFD model was applied to predict gas and solid residence time distributions (RTDs) since prediction and analysis of RTD is an important tool to study the complex multiphase flow behavior and mixing inside chemical reactors. The predictions show that solid mean residence time is 3.5 times the gas residence time; the solid RTD is more sensitive to the process gas flow rate than the solids circulation rate.

09 BIOMASS FUELS↗
DOE OSTI2020

Optimization of harvest and logistics for multiple lignocellulosic biomass feedstocks in the northeastern United States

A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $\$67.90$ to $\$86.97$ per dry Mg with an average of $\$79.58$ /dry Mg. The biomass delivered costs by county were from $\$67.90$ to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $\$85.30$ /dry Mg for forest residues, $\$84.47$ /dry Mg for hybrid willow, $\$99.68$ for switchgrass and $\$97.87$ per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Lastly, our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.

09 BIOMASS FUELS↗
DOE OSTI2022

Carbon fibers derived from liquefied and fractionated poplar lignins: The effect of molecular weight

Lignin recovered from poplar, a key woody biomass resource proposed for lignocellulosic refineries, was investigated for conversion into carbon fibers. Aqueous solutions of ethanol at selected temperatures and compositions, where the requisite solvent and liquefied-lignin phases form, were used to purify and fractionate hybrid poplar (HP) lignin using the Aqueous Lignin Purification with Hot Agents (ALPHA) process. Sugars (<0.1%) and ash (0.01 ± 0.01%) content of the highest molecular weight fraction (Mw = 52 kDa) approached the limits of detection. This 52 kDa fraction could be dry spun with excellent stability and was subsequently converted into carbon fibers having strength (1.1 ± 0.2 GPa) and modulus (78 ± 8 GPa) essentially double those previously achieved with neat poplar and other hardwood lignins. Of equal importance, stabilization times were decreased by a factor of five. Such stabilization and strength/modulus improvements are essential for reducing the overall manufacturing cost of carbon fibers being proposed for cost-sensitive applications.

09 BIOMASS FUELS↗
DOE OSTI2023

Data-driven acoustic measurement of moisture content in flowing biomass

Measuring the moisture content in flowing biomass is critical to processes such as liquid biofuel conversion, such as biogasoline, biodiesel, bio jet kerosene, etc. However, biomass tends to flow in aggregates, which results in significant inhomogeneities in the amount of biomass flowing in front of a sensor at a given time, and there can be significant overlap in the material properties of dry vs wet biomass, leading to poor signal-to-noise ratio. We present a technique for identifying biomass moisture content using a series of acoustic pitch-catch measurements to quantify the sound speed and acoustic amplitude through the biomass, in conjunction with classical machine learning techniques, including Naive Bayes, Random Forest, and K-Nearest Neighbors classification. We amplify the differences between the acoustic measurements in different moisture levels by collecting a series of pulse-echo measurements, which we sort in order of ascending sound speed. We test the accuracy of the technique on experimentally-prepared batches of corn stover biomass with specified moisture levels and measure the average error in the estimated moisture level as a function of the number of pitch-catch measurements used. We observe average estimation errors as low as 6.7% by increasing the number of measurements and optimizing the hyperparameters. This work presents a novel method determining moisture content in flowing biomass with inhomogeneous flow. Additionally, this technique has application in optimizing biomass conversion processes, as well as other fields including, paper production, natural fiber processing, and mineral extraction.

09 BIOMASS FUELS↗
DOE OSTI2024

Lignin valorization reshapes sustainable biomass refining

As the largest natural reservoir of aromatics, lignin offers significant potential for bioproduct manufacturing through advances in valorization technologies. However, the intrinsically complex structures of lignin pose significant challenges for its fractionization and downstream valorization. Overcoming challenges in lignin chemistry modification is crucial for achieving effective lignin valorization and establishing sustainable biorefinery industries. This review explores the potential of tailoring lignin reactivity to enable functional bioproduct manufacturing thereby contributing to profitable biorefining. The intrinsic characteristics of lignin are first summarized, highlighting their roles in both fractionization and valorization. The latest progress in lignin fractionation is then presented, emphasizing their potential to tailor lignin chemistry, reactivity, and processibility. Furthermore, advancements in lignin valorization are covered, recognizing that tailored lignin reactivity is key to defining bioproduct functionality. By examining these chemical mechanisms, this review sheds on the structure-function relationships between lignin and its derived products. To address the dilemma of lignin valorization and biorefineries, a promising synergistic biorefinery is proposed. This involves redesigning biomass fractionation strategies, tailoring lignin chemistry, and upgrading both carbohydrate and lignin streams across the entire biorefinery chain—from feedstock to application. Altogether, a deeper understanding of tailored lignin chemistry is crucial for decoding the reaction mechanisms in biomass processing. A synergistic biorefinery could harness lignin's intrinsic properties to improve product functionality and address key challenges, paving the way for cost-effective, sustainable biorefinery solutions.

09 BIOMASS FUELS↗
DOE OSTI2025

Complexity Reduction Methods for Large-Scale Spatially Explicit Biofuels Network Design

The size and complexity of energy system optimization models have increased significantly in recent years, driven by the availability of high-resolution spatial data. We present complexity reduction and solution methods that enable us to efficiently represent high-resolution spatial data in the network design of large-scale energy systems. We aim to reduce the size and enhance the computational efficiency of network design models without sacrificing solution accuracy. Specifically, we first present how to aggregate highly granular data into larger resolutions without averaging out their specific properties through a composite-curve-based approach and then develop a method to linearly represent these curves. Second, we utilize a general clustering method to determine groups of geographically proximate biomass fields and establish a single transportation arc for all of them, reducing the number of transportation-related variables while maintaining an accurate representation of the system. Finally, we introduce a two-step algorithm that decomposes large-scale network design problems into two smaller, more manageable subproblems. We demonstrate the application of our methods using a case study of switchgrass-to-biofuels network design in the eight states of the U.S. Midwest, using realistic and highly explicit spatial data.

09 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 OSTI2021

Deactivation by Potassium Accumulation on a Pt/TiO 2 Bifunctional Catalyst for Biomass Catalytic Fast Pyrolysis

Biomass-derived feedstocks bring significant challenges to the longevity of the catalysts used for their conversion, and alkali metals, for example, K, in the feedstock have been widely ascribed as one of the important factors causing catalyst deactivation. To address this challenge, it is critical to understand the mechanism of catalyst deactivation caused by K accumulation to guide the improvement of catalysts and processes and the development of catalyst regeneration strategies. In this work, we report a deep understanding of the impact of K on a bifunctional Pt/TiO 2 catalyst, which is an efficient catalyst for the ex situ catalytic fast pyrolysis of biomass. We simulated the K-poisoning of Pt/TiO 2 catalysts by purposely loading different amounts of K (100-6000 ppm by weight) on the catalysts. A series of characterization approaches, including scanning transmission electron microscopy, Fourier transform infrared spectroscopy, and chemical titration, were combined with a kinetic assessment of multiple probe reactions to elucidate the mechanism of Pt/TiO 2 deactivation by K accumulation. At low K loadings (<800 ppm), K preferentially poisons the strong Lewis acid sites, leading to significantly reduced activity for acid-catalyzed alcohol dehydration. However, acetone condensation is less sensitive to the poisoning of strong Lewis acid sites. Reactions that occur on Pt sites or at the metal-support interface, including alkene hydrogenation, m-cresol hydrodeoxygenation (HDO), and CO oxidation, were not impacted. At high K loadings (>800 ppm), K starts to accumulate on the Pt-TiO 2 interfacial area, poisoning the interfacial active sites for HDO and CO oxidation reactions. We further found that the poisoning of the Pt/TiO 2 catalyst by K is reversible, and water washing can successfully remove the accumulated K and recover the activities for both alcohol dehydration and m-cresol HDO reactions.

09 BIOMASS FUELS↗
DOE OSTI2020

Supply Cost and Life-Cycle Greenhouse Gas Footprint of Dry and Ensiled Biomass Sorghum for Biofuel Production

Here we discuss Biomass sorghum as a promising feedstock for cellulosic biorefineries because of its high yield and drought tolerance. However, the difficulty of effectively drying sorghum in some regions means that it may require different handling than previously studied grassy feedstocks. This study compares the delivered cost and life-cycle greenhouse gas (GHG) footprint of field drying and baling, module storage (wrapped, densely packed biomass), pelletizing, and ensiling. Ensiling has not been widely considered for use in bioenergy production. For farms within 66 km of the biorefinery, ensiled biomass is of the lowest cost and GHG strategy despite additional cost and energy demand for hauling wet biomass. Field drying and baling, if feasible, is the most cost-effective option for sorghum between 66 km and 104 km, beyond which pellets are preferable. A 2000 bone-dry-metric ton (bdt)/day biorefinery can source sorghum with 18 bdt/ha yield cultivated on 5% of surrounding land at costs ranging from $\$$122 (silage) to $\$$167 (pellets)/bdt and a life-cycle GHG footprint of 111 (silage) to 179 kg CO2e/bdt (pellets). With 28 bdt/ha biomass yield, 10% cultivation of surrounding land, and low fertilizer application, costs can range from $\$$66 (silage) to $\$$85 (pellets)/ bdt and GHG footprint of 43 (silage) to 96 kg CO2e (pellets)/bdt.

09 BIOMASS FUELS↗
DOE OSTI2025

Go with the Flow: Additives to Improve the Flowability of Crumbled Corn Stover

For this work, magnesium stearate (MgSt), a common flow additive and anticaking agent, was applied to corn stover feedstocks with three particle sizes. The material handling properties of dry, 15%, and 30% moisture content corn stover were measured with a Freeman FT-4 powder rheometer and mass flow hopper calculations. While hydrated biomass typically exhibits poor flowability, introducing MgSt led to a significant increase in flowability for dry and 15% moisture content feedstocks, with less impact at 30% moisture content. Inverse gas chromatography showed a decrease, up to 40%, in the surface energy of MgSt-coated corn stover. While MgSt may not be ideal for biofuel or chemical production, this study highlights the potential of flow additives to reduce adhesive and cohesive forces, improving biomass feedstock handling.

09 BIOMASS FUELS↗
DOE OSTI2026

Conserved Macromolecular Architecture of Poplar Secondary Cell Walls Revealed by ssNMR and Atomistic Modeling

The macromolecular architecture of plant secondary cell walls governs wood's mechanical and biochemical properties, yet its natural intra-species variability remains poorly characterized. Here, we combined 13C solid-state NMR (ssNMR), multivariate statistical analysis, and molecular modeling to profile nanoscale structure across 13 genetically diverse Populus trichocarpa genotypes grown in 13C-enriched atmospheres. SsNMR-derived phenotypes spanning composition, structure, mobility, and inter-polymer proximities reveal a conserved architecture, with a subtle yet coordinated variation organizing into dominant structural and secondary mobility axes. A representative atomistic model captures these features and reproduces experimental metrics. Molecular dynamics simulations support a weak but consistent positive correlation between cellulose abundance and crystalline-like order, with interior cellulose chains enriched in tg (trans-gauche) conformations without expanding crystalline cores. Together, experiment and simulation reveal a genetically buffered, broadly conserved nanoscale architecture across genotypes, where subtle fine-tuning of cellulose bundling and matrix packing balances mechanical performance with biological function.

09 BIOMASS FUELS↗
DOE OSTI2024

Advances in electrosynthesis for a greener chemical industry

As nations unite to curb anthropogenic greenhouse gas emissions, the decarbonization of the chemical industry has been propelled to the forefront of scientific research. Renewable electricity will play a central role in this effort. In addition to powering chemical plants and allowing for the sustainable production of heat to drive thermocatalytic processes, renewable electricity also provides the chemical industry with opportunities to engage in the sustainability revolution and broadly reduce its environmental footprint through breakthrough innovations in direct electrochemical transformations. The electrification of chemical synthesis—electrosynthesis—is a promising route to promote sustainability without compromising economic competitiveness. Electrosynthesis uses electrons both as an energy source to drive reactions and as a green reagent for chemical reductions and oxidations under ambient conditions. Therefore, it holds tremendous potential (pun intended) to increase selectivity to desired products, open green reaction pathways for challenging transformations (e.g., Birch reduction, epoxidations, coupling reactions), and reduce chemical waste.

08 HYDROGEN↗