Search NASA⌕ Search

SEARCH · Search NASA

Results for “Hydrothermal processing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Pretreatment of Biomass by Selected Type-III Deep Eutectic Solvents and Evaluation of the Pretreatment Effects on Hydrothermal Carbonization

Hydrothermal carbonization (HTC) is a novel thermochemical conversion that converts wet biomass into energy dense solid fuel. Residual moisture under subcritical conditions reacts with a lignin-cellulose-hemicellulose matrix with the major reactions being identified as dehydration and decarboxylation. Among other reaction parameters (e.g., temperature, time, pressure), biomass morphology often plays a key role in HTC. The hypothesis of this study was enhancing the porous structure of biomass without significantly affecting biopolymer composition would augment hydrothermal carbonization (HTC). To prove the hypothesis, two type-III deep eutectic solvents (DESs), namely choline chloride:urea (ChCl:Urea, 1:2 mol/mol) and methyltriphenylphosphonium bromide:ethylene glycol (MTPB:EG, 1:4 mol/mol), were studied to pretreat loblolly pine at room temperature and ambient pressure for 1 h. DES pretreatment shows swelling of the biomass, increasing the surface fiber-to-fiber gap length by 52% and 185% for ChCl:Urea and MPTB:EG pretreatments, respectively. The total pore volume remained intact (2.6 × 10 –3 cm 3 /g), although new small pores evolved, and existing pores were abated with DES pretreatment. Hydrochars prepared from DES pretreated loblolly pine showed a high O/C and H/C ratio resulting in a significant increase of energy content (up to 42%) and a decrease of mass yield (up to 50 wt %), indicating an enhancement of HTC severity due to the alteration of surface morphology by DES. Finally, a preliminary process economics revealed that integrated DES pretreatment-HTC would increase fixed capital investment but decrease the cost of operation and manufacturing than the standalone HTC process.

09 BIOMASS FUELS↗
DOE OSTI2025

Valorizing Hydrothermal liquefaction aqueous phase via nanofiltration: Enhancing biocrude production from algal biomass

The hydrothermal liquefaction aqueous phase (HTL-AP), a nutrient- and carbon-rich waste byproduct of the HTL process, can be recirculated to enhance biocrude production efficiency. To avoid dilution of the HTL feedstock and reduced fuel quality associated with recirculating untreated HTL-AP, this study investigated nanofiltration to concentrate organic compounds and separate nutrients from HTL-AP of three algal biomass sources: an algal wastewater treatment system and two from harmful algal blooms in lakes. Up to 99% of the organics were recovered in the concentrated HTL-AP (retentate), and significant increases in both permeate flux and permeation of nitrogenous compounds were achieved by increasing HTL-AP temperature to 45 °C and pH to 11. Recirculating the concentrated HTL-AP increased biocrude yield by 70%-116% and reduced biocrude nitrogen content by up to 12%. The carbon and energy capture in the biocrude also increased by 66% and 68%, respectively.

09 BIOMASS FUELS↗
DOE OSTI2022

Techno-economic analysis of cellulosic ethanol conversion to fuel and chemicals

In this paper, we describe multiple scenarios investigated to assess the techno-economic results of producing fuels from corn stover-derived ethanol and the impact of co-producing higher-value, ethanol-derived n-butanol and 1,3-butadiene to improve economic feasibility. An alternative approach that produces only chemicals from the lignocellulosic-derived ethanol while producing fuels from the residual cell mass, unconverted sugars and lignin using hydrothermal liquefaction and hydrotreating also is described. Our results indicate that the hydrothermal liquefaction pathway has cost advantages relative to pathways that burn lignin from the corn stover conversion process to make ethanol when the associated ethanol stream is used to make only chemicals.

09 BIOMASS FUELS↗
DOE OSTI2022

Data for Bioprocessing, Recovery, and Mass Balance of Vegetative Lipids from Metabolically Engineered ‘Oilcane’ Demonstrates Its Potential as an Alternative Feedstock for Drop-In Fuel Production

Plant oils are increasingly in demand as renewable feedstocks for biodiesel and biochemicals. Currently, oilseeds are the primary source of plant oils. Although the vegetative tissues of plants express lipid metabolism pathways, they do not hyper-accumulate lipids. Elevated synthesis, storage, and accumulation of lipids in vegetative tissues have been achieved by metabolic engineering of sugarcane to produce “oilcane.” This study evaluates the potential of oilcane as a renewable feedstock for the co-production of lipids and fermentable sugars. Oilcane was grown under favorable climatic and field conditions in Florida (FLOC) as well as during an abbreviated growing season, outside its typical growing region, in Illinois (ILOC). The potential lipid yield of 0.35 tons/ha was projected from the hyperaccumulation of fatty acids in the stored vegetative biomass of FLOC, which is approaching the lipid yield of soybean (0.44 tons/ha). Processing of the vegetative tissues of oilcane recovered 0.20 tons/ha, which represents the recovery of 55% of the total lipids from FLOC. Chemical-free hydrothermal bioprocessing of ILOC and FLOC bagasse and leaves at 180 °C for 10 min prevented the degeneration of in situ plant lipids. This allowed the recovery of lipids at the end of the bioprocess with a major fraction of lipids remaining in the biomass residues after pretreatment and saccharification. Improvements through refined biomass processing, crop management, and metabolic engineering are expected to boost lipid yields and make oilcane a prime feedstock for the production of biodiesel.

Conversion↗
DOE OSTI2021

Techno-economic and life-cycle assessment of fuel production from mixotrophic Galdieria sulphuraria microalgae on hydrolysate

Outdoor photoautotrophic algal growth is limited by light attenuation and attendant respiratory CO 2 losses during dark periods, limiting its productivity potential and carbon use efficiency. Developing a system that leverages mixotrophic growth (combining the benefits of both heterotrophic and photoautotrophic growth) has the potential to dramatically improve the total productivity and economics of the system. However, it is unknown if the productivity gains offset the added costs of outdoor mixotrophic cultivation using cellulosic hydrolysate as the feedstock. In this study, corn stover-derived cellulosic sugars were evaluated as the mixotrophic organic carbon source for the cultivation of Galdieria sulphuraria, which can metabolize both glucose and xylose from corn stover hydrolysate. A techno-economic analysis (TEA) and a life-cycle assessment (LCA) were conducted based on a detailed engineering process model for both glass helical photobioreactor and covered pond cultivation platforms, coupled with downstream conversion and upgrading to renewable diesel through hydrothermal liquefaction. Results show the minimum biomass selling price for cultivation in the photobioreactor design assuming a productivity of 1.575 kg m -3 day -1 and a substrate yield of 0.57 g g -1 is $2869 per dry metric ton. The costs are dramatically reduced in the covered pond design which assumes a productivity of 0.8 kg m -3 day -1 and a substrate yield of 0.7 g g -1 , 921 dollars per dry metric ton. Expanding the system boundary to include downstream processing results in a minimum fuel selling price of 8.24 dollars and 3.32 dollars dm 3 GE -1 for the photobioreactor and covered pond systems, respectively. Finally, life-cycle results demonstrate a global warming potential of 339 and 9.1 gCO 2 -eq. MJ -1 on a well-to-wheels basis and a net energy ratio of 2.21 and 0.25 MJ MJ -1 for the photobioreactor and covered pond systems, respectively. Discussion focuses on a required co-product selling price as a function of biomass diversion to meet economic parity with conventional fuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Wet Waste Hydrothermal Liquefaction and Biocrude Upgrading to Hydrocarbon Fuels: 2021 State of Technology

Each year the U.S. Department of Energy Bioenergy Technologies Office (BETO) assesses progress in their research and development efforts toward sustainable production of renewable fuels (DOE 2016) through the annual state of technology (SOT) assessment. The SOT assessment evaluates the impact of the year’s research progress on the modeled minimum fuel selling price (MFSP) for selected biofuel conversion pathways and measures the current state of the technology relative to defined goal case projections. Supply chain sustainability analysis to track and guide research toward improved greenhouse gas (GHG) emissions, energy usage, water usage and other environmental metrics for the pathway is performed by Argonne National Laboratory (Cai et al. 2018, 2020). Technical and cost targets for a projected goal case set for the year 2022 were previously established for the wet waste hydrothermal liquefaction (HTL) and biocrude upgrading pathway and summarized in a design report (Snowden-Swan et al. 2017). Process performance advancements made for HTL and biocrude hydrotreating have resulted yearly reductions in the modeled MFSP relative to the initial SOT (2018) (Snowden-Swan et al. 2020, 2021). This report summarizes the research and associated techno-economic analysis (TEA) for the pathway 2021 SOT. Methods and economic assumptions for the nth plant analysis used for the TEA are consistent with the design report (Snowden-Swan et al. 2017), with the exception of updates in the modeled cost year (2016) and income tax rate (21%).

09 BIOMASS FUELS↗
DOE OSTI2022

Process optimization of caustic scrubber and iodine-129 immobilization in sodalite-based waste forms

Caustic scrubbers are proposed to remove vaporized iodine from nuclear fuel reprocessing plants. Resulting caustic slurries of radioactive iodine ( 129 I) and other anions can be captured and immobilized into the cage structures of sodalite-type minerals. Here, mixed anion sodalites were hydrothermally synthesized from a simulated caustic scrubber solution containing hydroxide/water, carbonate, chlorine, bromine, iodine, nitrate, and nitrite ions with added kaolinite. Experiments were conducted with twenty-five different sets of process conditions. Resulting powders were characterized by quantitative X-ray diffraction (XRD) and multi-functional thermal analysis, including gravimetry, calorimetry, and mass spectrometry. Using statistical analysis, process variables were tailored to maximize conversion of kaolinite into cage silicates, immobilize caustic scrubber anions, and minimize amorphous content. Further, chemical analysis showed the incorporation of all the targeted ions into the solids. Detailed investigation of a batch characterized after washing (creating secondary waste) and one without washing but subsequent calcination is given. This work contributes to defining the constraints required for subsequent consolidation process steps of the waste form to avoid volatilization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Elucidating the role of reactive nitrogen intermediates in hetero-cyclization during hydrothermal liquefaction of food waste

Hydrothermal liquefaction (HTL) has emerged as a promising strategy for converting abundant, water-rich organic streams into an energy-dense, sustainable, biocrude. Despite major strides in improving oil yields and process intensification, a key underlying challenge persists in relating the composition of the feedstock to the ultimate fate of nitrogen, which is especially important for biocrude quality. To elucidate how nitrogen partitions into the respective gas, aqueous, oil and solid hydrothermal products, food waste was treated under HTL conditions (15 wt% solids, 575 K reaction temperature, and 1 hour reaction time) and elemental, chromatographic and mass spectroscopy analyses were performed on the products to establish dominant product classes and to close mass, carbon, and nitrogen balances. Liquid products (aqueous and biocrude) were found to contain nearly 60% of the nitrogen, with a majority of the biocrude-phase nitrogen in the form of amides. Ab initio thermodynamic and kinetic simulations on model reactants were used to evaluate potential reaction pathways involving reactive nitrogen intermediates. A subsequent kinetic model was evaluated at reaction temperatures ranging 300–1000 K and for a range of feedstock compositions to identify primary reaction pathways. The Maillard reaction is revealed to be the dominant pathway for converting reactive nitrogen intermediates into observed products, especially those in which nitrogen is present as a heterocyclic aromatic. Subsequent product analysis by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) confirms strong agreement among the experimental data, kinetic model and proposed Maillard pathways.

09 BIOMASS FUELS↗
DOE OSTI2021

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes. This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below).

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Subsurface microbial community structure shifts along the geological features of the Central American Volcanic Arc

Subduction of the Cocos and Nazca oceanic plates beneath the Caribbean plate drives the upward movement of deep fluids enriched in carbon, nitrogen, sulfur, and iron along the Central American Volcanic Arc (CAVA). These compounds fuel diverse subsurface microbial communities that in turn alter the distribution, redox state, and isotopic composition of these compounds. Microbial community structure and functions vary according to deep fluid delivery across the arc, but less is known about how microbial communities differ along the axis of a convergent margin as geological features (e.g., extent of volcanism and subduction geometry) shift. Here, we investigate changes in bacterial 16S rRNA gene amplicons and geochemical analysis of deeply-sourced seeps along the southern CAVA, where subduction of the Cocos Ridge alters the geological setting. We find shifts in community composition along the convergent margin, with communities in similar geological settings clustering together independently of the proximity of sample sites. Microbial community composition correlates with geological variables such as host rock type, maturity of hydrothermal fluid and slab depth along different segments of the CAVA. This reveals tight coupling between deep Earth processes and subsurface microbial activity, controlling community distribution, structure and composition along a convergent margin.

Science & Technology - Other Topics↗
DOE OSTI2021

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗
DOE OSTI2025

Harvesting Energy from Wastewater by Converting Sewage

This project aims were to develop and demonstrate a scalable, integrated process to convert sewage sludge into renewable natural gas (RNG), enabling wastewater treatment plants (WWTPs) to become net energy producers. The system proposal integrates autothermal hydrothermal liquefaction (AT-HTL), supercritical salt precipitation (SCSP), and hydrothermal gasification (HTG), collectively forming the Supercritical Sludge-to-Gas (SC-S2G) platform. Initially, batch hydrothermal liquefaction reactions were used to screen sewage sludge using AT-HTL (later termed RI-HTL) conversion to biocrude, aqueous and char phases compared to hydrothermal liquefaction (HTL). Significant improvement in biocrude yield using peroxide addition at O:C ratio of 0.05 and under conditions of 300°C for 10 minutes gave 57% biocrude yield and 85% fluid carbon yield (biocrude plus aqueous), while minimizing the loss of carbon to char solids (~7%). Hence, RI-HTL was shown to be effective for conversion of real sewage sludge. The corrosion of the alloy reactor tubes or vessels is an important factor when developing a process that includes an oxidant and a chemically complex feed like sewage sludge. We investigated the corrosion rates on metal alloys at 350°C for 240 hours. Corrosion rates of 0.21 and 0.26 mpy for 304L and 316L stainless steel were measured respectively. The corrosion information obtained in this investigation was utilized by PNNL for design, materials sourcing and construction of the pilot scale continuous flow system.

09 BIOMASS FUELS↗
DOE OSTI2021

Development of Algal Biomass Yield Improvements in an Integrated Process (Final Report)

This project built on the success of Global Algae Innovations Algae Biomass Yield Phase 1 project to accelerate the commercialization of algal biofuels through development of an integrated, economical, photosynthetic, open raceway system to produce algal oil. Two parallel pathways to a biofuel were investigated. In the algal crude oil pathway, highlighted in blue, the dewatered algal biomass slurry is used as a feed to hydrothermal liquefaction to produce an algae crude oil and recycle aqueous stream. In the algal lipid oil pathway, highlighted in green, the algal biomass slurry is dried, and the oil is extracted to produce an algal lipid oil biofuel intermediate and a high protein algae meal co-product. Upgrading to drop-in fuels has been demonstrated for the biofuel intermediates in both pathways. Since the algal lipid oil pathway requires lipid accumulation, the productivity is generally lower than the algal crude oil pathway, but the required productivity for economical algal biofuel production is also lower because the coproduct value is greater. The outdoor cultivation was performed at the Kauai Algae Farm. The project team has expertise across the full breadth strain development, advanced algal cultivation, open raceway contamination control, CO 2 supply, harvesting, dewatering, extraction, hydrothermal liquefaction, technoeconomic analysis and life cycle analysis: Hildebrand laboratory at Scripps Institution of Oceanography – diatom strain development. Mayfield laboratory at UCSD - green algae strain development. TSD Management Associates- CO 2 supply, algal harvesting and dewatering, and algal drying and extraction. Qualitas - large-scale test site for algal harvesting technology. Texas A&M University - algal drying and extraction technology. Pacific Northwest National Laboratory - hydrothermal liquefaction technology. GE Water and Power - membrane technology. National Renewable Energy Laboratory - algal techno-economic analysis. Phase 1 of the project resulted in tremendous productivity and pre-processing improvements in an integrated, large-scale, low-cost cultivation and pre-processing process that moved algal technology closer to economic viability for biofuels than ever before. Phase 2 accomplishments further facilitate development of a commercial algal biofuel industry by adding: several top performing strains and strain development tools including breeding; open-pond cultivation innovations that achieved a 30% improvement in overall productivity, an 83% reduction in cost, and a 95% reduction in energy use relative to conventional technology; further improvements in harvesting that led to the Zobi harvester ® as a universal, economical, low energy, commercially available microalgae harvesting solution; breakthroughs in drying and extraction including a new low energy drying process and several new low energy extraction unit operations that together with the Zobi harvester ® achieve a 90% reduction in cost and 97% reduction in energy use relative to conventional technology. These improvements are sufficient to enable economical production of algae biofuel and protein meal co-product if the process were scaled up to a commercial-scale 5,000-acre algae farm.

09 BIOMASS FUELS↗
DOE OSTI2025

Catalyst Deactivation Modes of PdO/γ-Al 2 O 3 Catalysts for Lean Methane Oxidation

PdO/γ-Al 2 O 3 catalysts are one of the most active catalytic components for the complete oxidation of methane. Under reaction conditions, especially in a wet feed, the catalysts suffer severe performance degradation. This study establishes a series of testing protocols to systematically investigate the causes of catalyst deactivation under methane oxidation reaction conditions. Four distinct catalyst deactivation modes are identified. Two of the deactivation modes are directly related to H 2 O, either from the feed gas or as a part of the reaction products, with one (Mode 2) being attributed to the formation of surface hydroxyl groups and the other (Mode 3) to the competitive adsorption of H 2 O on the catalysts. The impact of the two deactivation modes is acute and severe but reversible. In contrast, the other two deactivation modes are gradual and persistent but irreversible. Both modes are induced by CH 4 oxidation reaction, with the impact of a wet feed (Mode 4) being substantially more severe than that of a dry feed (Mode 1). The major cause of the irreversible catalyst deactivation is attributed to surface reconstruction of PdO nanoparticles, which behaves as a passivation layer lowering the number of coordinately unsaturated Pd sites for CH 4 activation. Although the passivation layer is relatively stable against thermal or hydrothermal treatment, it is not completely inert. Formation and partial regeneration of the passivation layer is a highly dynamic process and heavily depends on the reaction temperature: a lower reaction temperature (≤ 450 ℃) can lead to quicker catalyst deactivation; but a higher reaction temperature (between 500 – 550 ℃) can result in a greater extent of catalyst deactivation.

PdO/γ-Al2O3↗
DOE OSTI2022

Bioconversion of Thermochemical Intermediates

Thermochemical (TC) biofuels production via both pyrolysis and hydrothermal liquefaction produces aqueous waste streams, typically with organic compounds at concentrations of -50-100 g/L. These streams represent a wastewater treatment cost and carbon loss for the TC biorefinery, but the concentration range for these compounds is ideal for bioconversion. To that end, the Bioconversion of Thermochemical Intermediates (BTI) project is developing advanced analytics and engineered microbes to convert these waste streams to co-products, with the overall aim of improving the economics and carbon conversion efficiency of TC biorefining. To date, we have primarily focused on development of advanced analytical chemistry approaches to fully characterize TC aqueous streams and engineering of Pseudomonas putida for conversion of non-conventional substrates, including methylated phenolics, cyclic ketones, furans, and C1-C3 light oxygenates, into atom-efficient products. Two primary challenges are the rapid deployment of aqueous-compatible analytics to changing upstream conditions and dealing with the toxicity of the feed streams to engineered microbes. The project efforts have resulted in engineered strains of P. putida able to consume 90% of the organic compounds in aqueous waste streams from catalytic fast pyrolysis, more than 300-fold toxicity tolerance improvements in P. putida, and carbon closures exceeding 90% for TC wastewater streams across multiple processing technologies.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2021

Scalable Polymeric Few-Nanometer Organosilica Membranes with Hydrothermal Stability for Selective Hydrogen Separation

Nanoporous silica membranes exhibit excellent H 2 /CO 2 separation properties for sustainable H 2 production and CO 2 capture but are prepared via complicated thermal processes above 400 °C, which prevent their scalable production at a low cost. Here, we demonstrate the rapid fabrication (within 2 min) of ultrathin silica-like membranes (~3 nm) via an oxygen plasma treatment of polydimethylsiloxane-based thin-film composite membranes at 20 °C. The resulting organosilica membranes unexpectedly exhibit H2 permeance of 280-930 GPU (1 GPU = 3.347 x 10 -10 mol m -2 s -1 Pa -1 ) and H2/CO2 selectivity of 93-32 at 200 °C, far surpassing state-of-the-art membranes and Robeson’s upper bound for H 2 /CO 2 separation. When challenged with a 3 d simulated syngas test containing water vapor at 200 °C and a 340 d stability test, the membrane shows durable separation performance and excellent hydrothermal stability. The robust H 2 /CO 2 separation properties coupled with excellent scalability demonstrate the great potential of these organosilica membranes for economic H 2 production with minimal carbon emissions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Simulated moving bed-inspired method for continuous adsorptive denitrogenation of model fuel

Efficient, continuous routes for removing nitrogen-containing compounds from hydrothermal liquefaction-derived synthetic aviation fuel are needed to enable direct blending with conventional jet fuels. Here, we report a simulated moving bed-inspired process for adsorptive denitrogenation of a model fuel. Unlike conventional simulated moving bed systems, which are designed for sharp separations between similar solutes, this approach was run deliberately outside the classical separation region so that both pyridine and indole were removed together from the hydrocarbon stream. Alcohol solvents were used to regenerate the silica adsorbent, maintaining performance over extended operation and avoiding the downtime and energy demand associated with calcination. Under these conditions, the system demonstrates removal of more than 98% of nitrogen while cutting solvent use by 28% compared to batch operation. Classical modeling tools predicted column concentration profiles even in this nontraditional regime, suggesting a straightforward path to scaling. Together, these results motivate solvent-efficient, continuous denitrogenation strategies that could be integrated with biorefinery processes.

Adsorption↗