Search NASA⌕ Search

SEARCH · Search NASA

Results for “carbon inputs”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Genetic characterization of a Sorghum bicolor multiparent mapping population emphasizing carbon-partitioning dynamics

Sorghum bicolor, a photosynthetically efficient C4 grass, represents an important source of grain, forage, fermentable sugars, and cellulosic fibers that can be utilized in myriad applications ranging from bioenergy to bioindustrial feedstocks. Sorghum’s efficient fixation of carbon per unit time per unit area per unit input has led to its classification as a preferred biomass crop highlighted by its designation as an advanced biofuel by the U.S. Department of Energy. Due to its extensive genetic diversity and worldwide colonization, sorghum has considerable diversity for a range of phenotypes influencing productivity, composition, and sink/source dynamics. To dissect the genetic basis of these key traits, we present a sorghum carbon-partitioning nested association mapping (NAM) population generated by crossing 11 diverse founder lines with Grassl as the single recurrent female. By exploiting existing variation among cellulosic, forage, sweet, and grain sorghum carbon partitioning regimes, the sorghum carbon-partitioning NAM population will allow the identification of important biomass-associated traits, elucidate the genetic architecture underlying carbon partitioning and improve our understanding of the genetic determinants affecting unique phenotypes within Poaceae. We contrast this NAM population with an existing grain population generated using Tx430 as the recurrent female. Genotypic data are assessed for quality by examining variant density, nucleotide diversity, linkage decay, and are validated using pericarp and testa phenotypes to map known genes affecting these phenotypes. We release the 11-family NAM population along with corresponding genomic data for use in genetic, genomic, and agronomic studies with a focus on carbon-partitioning regimes.

multiparental populations↗
DOE OSTI2026

An Integrated Computational Materials Engineering (ICME) Approach to Design Nonlinear Transition Zones Between Dissimilar Metals

Current approaches to designing graded transition joints (GTJs) between dissimilar metals often rely on linear changes in both composition profiles and thickness of each sublayer. This increases fabrication cost and may not be optimal with respect to residual stress or the formation of undesirable phases. Here, in this study, GTJs between P91 ferritic/martensitic steel and 347H austenitic stainless steel were designed using Integrated Computational Materials Engineering (ICME) principles with nonlinear composition and length profiles. Guided by inputs from classical mechanics and CALPHAD predictions of carbon chemical potential, a novel transition zone consisting of five discrete compositions was proposed, with the thickness of each sublayer varying according to a brachistochrone-inspired distribution. In addition to carbon potential gradients, CALPHAD was used to predict coefficients of thermal expansion, which were incorporated into finite element models to evaluate stress evolution. The proposed nonlinear design resulted in a smoother carbon potential gradient, lower carbon depletion at the P91 interface, and a comparable residual stress under long-term thermal exposure, compared to a conventional linear design using ten sublayers with equal thickness. This work introduces a brachistochrone-inspired distribution for GTJ design, offering a general framework for optimizing graded interfaces between dissimilar metals.

Directed Energy Deposition↗
DOE OSTI2021

Quantifying the influence of different biocrust community states and their responses to warming temperatures on soil biogeochemistry in field and mesocosm studies

Biocrusts influence soil biogeochemistry by fixing carbon (C) and nitrogen (N) and through leachate inputs to soils. Functional rates can vary among biocrust community states and in response to edaphic properties, heterotrophic microbial activity, and global change. Using soils and biocrusts from the Colorado Plateau, Utah, USA, we aimed to quantify the influence of early-successional (ES) and late-successional (LS) biocrust community states on soil biogeochemistry. In a field setting, we found soil was less “fertile” under ES than LS biocrust, but ES biocrust had a relative influence 1.3 times greater than LS biocrust on soil fertility. Leachate collected from LS biocrust had, on average, 6 times more organic C and 1.7 times more dissolved N than ES biocrust, but concentrations of phosphorus (P) and inorganic N did not differ among the two biocrust types. Here, to disentangle influences of biocrusts and soil properties on biogeochemical pools, we constructed mesocosms from homogenized soil and left the surface bare or covered with ES or LS biocrust, before assignment to ambient or warmed (+5 °C) temperature treatments for 3 months. Multivariate biogeochemical properties differed among cover types, yet all exhibited losses of P, N, and organic C and nearly half of the biogeochemical variables considered did not differ among cover types. Mesocosms with LS biocrust retained more dissolved N, supported 8 additional, significant correlations among biogeochemical pools of the biocrust and mineral soil layer on average, and lost fewer of these correlations under warming. Overall, while soils under LS biocrusts were more fertile (i.e., had higher nutrient concentrations) than under ES, we did not find evidence implicating leachate as the primary driver of this difference. Biocrust influences on soil fertility were greater when mineral soil nutrients were in lower concentrations, highlighting the value of even incipient biocrusts for dryland functioning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model

This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.

biofuel sustainability↗
DOE OSTI2024

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

Abstract Atmospheric CO 2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO 2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Organic wastewater treatment by a single-atom catalyst and electrolytically produced H2O2

The presence of organic contaminants in wastewater poses considerable risks to the health of both humans and ecosystems. Although advanced oxidation processes that rely on highly reactive radicals to destroy organic contaminants are appealing treatment options, substantial energy and chemical inputs limit their practical applications. Here we demonstrate that Cu single atoms incorporated in graphitic carbon nitride can catalytically activate H 2 O 2 to generate hydroxyl radicals at pH 7.0 without energy input, and show robust stability within a filtration device. We further design an electrolysis reactor for the on-site generation of H 2 O 2 from air, water and renewable energy. Coupling the single-atom catalytic filter and the H 2 O 2 electrolytic generator in tandem delivers a wastewater treatment system. These findings provide a promising path toward reducing the energy and chemical demands of advanced oxidation processes, as well as enabling their implementation in remote areas and isolated communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

ATOMIC Simulations and Experimental Data for CaCO3 Mixtures

This data consists of simulations and experimental measurements of laser-induced breakdown spectroscopy (LIBS). The simulations are produced by ATOMIC, a general purpose plasma modeling and kinetics code that has been designed to compute emission (or absorption) spectra from plasmas [1] and are used to develop a statistical characterization of matrix effects. Our overall suite of simulations includes contains several sets of simulations: training and validation sets of simulations for three and four element mixtures of calcium, carbon, oxygen, and nitrogen (included to account for atmosphere) along with simulations of the individual elements. The 4-element simulations include the mixture of all four elements mentioned and for each of the four individual elements. The 3-element simulations include output for the mixture of calcium, carbon, oxygen and for these three individual elements. The training data were produced using a 600-run design, shown in Figure 1, that varies input parameters temperature (T), electron density (Ne), and proportion of the elements calcium, carbon, oxygen, and nitrogen (Ca; C; O; N) for the 4 element output. The 3-element output includes all parameters except for the proportion of nitrogen. The element proportions (all the variables but T and Ne) sum to one and are unused in the single-element simulations. The validation data was produced with a 80-run design shown in Figure 2. The training and validation simulation outputs for the 4-element simulations for the mixture and for the single element calcium are shown as sample simulations in Figures 3 and 4 respectively. The simulations produce spectra over a range of 190nm - 950nm that roughly mimics the range collected by the SciAps Z-300 LIBS instrument that was used for the experimental data. The measured spectra for a CaCO3 (which may include contribution from Earth's atmosphere) in the experiment is shown in in Figure 5. All files are kept in directories whose names indicate the elemental composition (CaCO3, Ca, C, O, or N), number of elements (3 or 4), and purpose (training, which is not labeled in the file name, or validation) with file names numbered to indicate the line in the design files used to produce the simulation. The designs are provided as text files with names indicating their purpose. The experimental data is provided as a CSV file. [1] J Colgan, EJ Judge, DP Kilcrease, and JE Barefield II. Ab-initio modeling of an iron laser-induced plasma: Comparison between theoretical and experimental atomic emission spectra. Spectrochimica Acta Part B: Atomic Spectroscopy, 97:65{73}, 2014.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Substitution of virgin carbon fiber with low-cost recycled fiber in automotive grade injection molding polyamide 66 for equivalent composite mechanical performance with improved sustainability

Recycled carbon fiber has historically proven challenging to integrate into composite manufacturing due in no small part to the low-density, randomly oriented, discontinuous fiber format that results from typical recycling. Discontinuous fiber requires the use of alternative technologies than those traditionally applied to continuous fiber (e.g., hand lay-up, winding). Extrusion compounding is one such applicable technology, but material transfer into the system requires alternative feeding equipment or the use of an altered procedure as trialed in this research. In this study, an injection molding compound for automotive applications was prepared with recycled carbon fiber and compared against an existing commercial compound. Input fibers and molded compound were evaluated for mechanical performance, while relevant variables such as compounded fiber alignment and aspect ratio were compared to the existing baseline material to confirm a like-for-like composite material structure. Overall, analysis indicates that recycled fiber performs similarly to virgin fiber reinforcement, demonstrating that recycled fiber may be a viable drop-in replacement for short-fiber discontinuous applications.

42 ENGINEERING↗
DOE OSTI2022

A hybrid inorganic–biological artificial photosynthesis system for energy-efficient food production

Artificial photosynthesis systems are proposed as an efficient alternative route to capture CO 2 to produce additional food for growing global demand. Here a two-step CO 2 electrolyser system was developed to produce a highly concentrated acetate stream with a 57% carbon selectivity (CO 2 to acetate), allowing its direct use for the heterotrophic cultivation of yeast, mushroom-producing fungus and a photosynthetic green alga, in the dark without inputs from biological photosynthesis. An evaluation of nine crop plants found that carbon from exogenously supplied acetate incorporates into biomass through major metabolic pathways. Coupling this approach to existing photovoltaic systems could increase solar-to-food energy conversion efficiency by about fourfold over biological photosynthesis, reducing the solar footprint required. This technology allows for a reimagination of how food can be produced in controlled environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Techno-economic prospects for producing Fischer-Tropsch jet fuel and electricity from lignite and woody biomass with CO 2 capture for EOR

This work explores the prospective techno-economic performance of facilities that produce low- and net-negative-carbon liquid transportation fuels and electricity with CO 2 capture for enhanced oil recovery. The lignite and biomass-to-jet fuel process is based on KBR’s TRIG gasifier, Rectisol (for sulfur removal and CO 2 capture), fixed-bed low temperature Fischer-Tropsch synthesis of liquid fuels, and Brayton/Rankine combined cycles to convert synthesis/refining off-gases and waste heat to electricity. This work leverages a recent, highly-detailed assessment of a prospective first-of-a-kind (FOAK) demonstration facility to develop highly detailed Aspen Plus process simulations for nine prospective N th -of-a-kind (NOAK) plant equipment configurations. Component-level capital costs from the FOAK study are scaled and adjusted to reflective prospective learning-by-doing to estimate capital costs for the NOAK designs. NOAK plant economic performance is found to be largely insensitive to variations in plant configurations and electricity output fraction, but biomass input fraction significantly affects profitability. Facilities that consume only carbon–neutral biomass, with no lignite co-feed, have significantly net-negative carbon emissions and the most favorable prospective economics when carbon emissions are priced. For these facilities, the crude oil price required for plant economic viability falls rapidly from $\$$100/bbl as carbon emission prices increase above $\$$120/tonne CO 2eq . In general, plants that co-fire lignite with biomass are less profitable (than 100% biomass plants) due to their higher net greenhouse gas emissions.

09 BIOMASS FUELS↗
DOE OSTI2022

Fast-decaying plant litter enhances soil carbon in temperate forests but not through microbial physiological traits

Abstract Conceptual and empirical advances in soil biogeochemistry have challenged long-held assumptions about the role of soil micro-organisms in soil organic carbon (SOC) dynamics; yet, rigorous tests of emerging concepts remain sparse. Recent hypotheses suggest that microbial necromass production links plant inputs to SOC accumulation, with high-quality (i.e., rapidly decomposing) plant litter promoting microbial carbon use efficiency, growth, and turnover leading to more mineral stabilization of necromass. We test this hypothesis experimentally and with observations across six eastern US forests, using stable isotopes to measure microbial traits and SOC dynamics. Here we show, in both studies, that microbial growth, efficiency, and turnover are negatively (not positively) related to mineral-associated SOC. In the experiment, stimulation of microbial growth by high-quality litter enhances SOC decomposition, offsetting the positive effect of litter quality on SOC stabilization. We suggest that microbial necromass production is not the primary driver of SOC persistence in temperate forests. Factors such as microbial necromass origin, alternative SOC formation pathways, priming effects, and soil abiotic properties can strongly decouple microbial growth, efficiency, and turnover from mineral-associated SOC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Atomic view of photosynthetic metabolite permeability pathways and confinement in synthetic carboxysome shells

Carboxysomes are protein microcompartments found in cyanobacteria, whose shell encapsulates rubisco at the heart of carbon fixation in the Calvin cycle. Carboxysomes are thought to locally concentrate CO 2 in the shell interior to improve rubisco efficiency through selective metabolite permeability, creating a concentrated catalytic center. However, permeability coefficients have not previously been determined for these gases, or for Calvin-cycle intermediates such as bicarbonate (HCO$^–_3$), 3-phosphoglycerate, or ribulose-1,5-bisphosphate. Starting from a high-resolution cryogenic electron microscopy structure of a synthetic β-carboxysome shell, we perform unbiased all-atom molecular dynamics to track metabolite permeability across the shell. The synthetic carboxysome shell structure, lacking the bacterial microcompartment trimer proteins and encapsulation peptides, is found to have similar permeability coefficients for multiple metabolites, and is not selectively permeable to HCO$^–_3$ relative to CO 2 . To resolve how these comparable permeabilities can be reconciled with the clear role of the carboxysome in the CO 2 -concentrating mechanism in cyanobacteria, complementary atomic-resolution Brownian Dynamics simulations estimate the mean first passage time for CO 2 assimilation in a crowded model carboxysome. Despite a relatively high CO 2 permeability of approximately 10 -2 cm/s across the carboxysome shell, the shell proteins reflect enough CO 2 back toward rubisco that 2,650 CO 2 molecules can be fixed by rubisco for every 1 CO 2 molecule that escapes under typical conditions. The permeabilities determined from all-atom molecular simulation are key inputs into flux modeling, and the insight gained into carbon fixation can facilitate the engineering of carboxysomes and other bacterial microcompartments for multiple applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Identifying mechanistic differences between co-fed CO 2 hydrogenation and reactive CO 2 capture using Ru and Pd dual function materials

Dual function materials (DFMs) enable reactive carbon capture (RCkeC), an intensified approach to carbon dioxide capture and utilization for cost and energy input reductions. Yet, there is a fundamental lack of understanding of mechanisms around CO 2 adsorption and subsequent conversion on these materials, hindering further development. Herein, we investigated several supported alkaline metal oxides for their CO 2 adsorption characteristics to find that Na/Al 2 O 3 had the highest CO 2 adsorption capacity, accompanied by a variety of CO 2 adsorption geometries as identified by in situ DRIFTS and computational modeling. The addition of catalytic metals (Ru, Pd) increased the adsorption capacity of Na/Al 2 O 3 without altering binding modes. In the subsequent reactive desorption step, acetate and formate intermediates were observed. Notably, this mechanistic investigation identified that the formation of acetate species was unique to RCC on a DFM, as these species were not observed in co-fed hydrogenation over the DFM or RCC over a Na-free catalyst.

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

Data for Impacts of Legacy and Contemporary Nitrogen Inputs on N2O and CO2 Emissions in Miscanthus and Maize Cultivated Soils

Nutrient inputs influence the sustainability of bioenergy crop production through contemporary (shortly after addition) and legacy effects (persisting over years) on microbial nitrogen (N) and carbon cycling, which contribute to greenhouse gas emissions. However, the relative importance of contemporary and legacy effects and how that could vary by crop functional types is poorly understood. Considering its rhizomatous roots and perennial growth, we hypothesized that Miscanthus × giganteu s ( M × g ) would be more sensitive to legacy N fertilization and the historical context of its environment than an annual crop like maize. To test this hypothesis, we examined the effects of legacy and contemporary N inputs on nitrous oxide (N2O) and carbon dioxide (CO2) emissions, as well as key N cycling genes in soils where M × g and maize were grown. A 150-day soil incubation experiment was conducted using soils from a long-term M × g and maize fertility experiment with three historic N fertilization rates (0, 112, and 336 kg N ha−1 year−1) and a contemporary amendment (60 mg N kg−1) with negative control (0 mg N kg−1). We observed significant increases in cumulative N2O emissions in M × g soils relative to maize soils, particularly at higher legacy fertilization rates, while contemporary N had no significant effect. Bacterial amoA gene abundance, which plays a significant role in nitrification in nutrient-rich soils, also increased with higher legacy fertilization rates in M × g soils but was unaffected by the contemporary N. In maize soils, legacy and contemporary N did not significantly affect N2O emissions, but cumulative CO2 emissions and amoA gene abundance significantly increased. The abundances of norB genes were not significantly influenced by either legacy fertilization or contemporary N amendments in either soil. Our findings demonstrate the greater importance of fertilization history over contemporary N in mediating soil N2O emissions, particularly for perennial bioenergy crops.

Carbon↗
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulated nitrogen deposition and precipitation events alter microbial carbon cycling during early stages of litter decomposition

Plant litter decomposition is a major nutrient input to terrestrial ecosystems that is primarily driven by microorganisms. Litter quality is considered a key drive of decomposition; however, human-induced global disturbance like nitrogen deposition and increasing extreme precipitation events will shift nutrient availability during litter decomposition. Little is known about how shifting nutrient availability will impact dissolved organic matter concentrations and microbially driven carbon cycling that are critical to soil organic matter formation. This study investigated the effect of simulated nitrogen deposition and repeated precipitation events on microbially driven carbon flow during short-term litter decomposition using a ‘common garden’ experiment with microcosms containing sand and blue grama grass litter inoculated with different microbial communities. Overall, nitrogen deposition decoupled respiration and dissolved organic carbon (DOC) by increasing respiration and not affecting DOC concentrations. Moreover, nitrogen deposition had no effect on microbial carbon use efficiency (CUE). Repeated simulated precipitation events significantly increased DOC concentrations, decreased microbial CUE, increased the microbial metabolic quotient (qCO2), and altered microbial composition and diversity. These findings highlight the complex interactions and responses of surface litter decomposers to shifting nutrient availability and contradicts previous findings that nitrogen deposition will increase soil carbon sequestration from a larger supply of DOC and reduced respiration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Integrating Tide‐Driven Wetland Soil Redox and Biogeochemical Interactions Into a Land Surface Model

Abstract Redox processes, aqueous and solid‐phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox‐sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗