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At least 739 records · Page 41

Transport Upscaling under Flow Heterogeneity and Matrix-Diffusion in Three-Dimensional Discrete Fracture Networks

For this work, we investigate the combined effects of network scale flow variability and retention due to matrix-diffusion on the scaling behavior of transport through fractured media. Two of the principal mechanisms controlling the transport of solutes through fractured low-permeability media are broad distributions of flow velocities and retention times in the solid matrix. We study the relative impact of these two processes under different initial conditions using a set of three-dimensional discrete fracture network simulations. We use these simulations to develop and calibrate an upscaled continuous time random walk (CTRW) approach for advective transport based on an Ornstein-Uhlenbeck model for the particle velocities that accounts for the fracture-matrix coupling using a compound Poisson process. This CTRW model can be conditioned on the initial solute distribution and allows to observe late-time scaling behavior at distances beyond what is feasible to observe using high-fidelity direct numerical simulations. We determine that the initial distribution of particles leads to marked differences in the persistent long-term scale behavior in the solute travel time distributions, even those undergoing retention due to matrix diffusion through implementation and analysis of the model.

54 ENVIRONMENTAL SCIENCES↗

Materials data analytics for 9% Cr family steel

A materials data analytics (MDA) methodology was developed in this study to evaluate publicly available information on 9% Cr family steel and to handle nonlinear relationships and the sparsity in materials data for this alloy class. The overarching goal is to accelerate the design process as well as to reduce the time and expense associated with qualification testing of new alloys for fossil energy applications. Data entries in the analyzed data set for 82 iron–base alloy compositions, several processing parameters, and results of tensile mechanical tests selected for this study were arranged in 34 columns by 915 rows. While detailed microstructural information was not available, it is assumed that the compositional space for the 9 to 12% Cr steels is limited such that all data entries have a tempered martensitic microstructure during service. Establishing a hierarchy of first–order trends in the publicly available data requires the MDA to filter out the biases. Complexity of the phase transformations and microstructure evolution in the multicomponent alloys (using 21 chemical elements) with major influence on mechanical properties, leads to inefficiency in direct application of unbiased linear regression across the entire data space. To address the nonlinearity, analyses of tensile data were performed in composition–based clusters. Clusters corresponding to moderately frequent patterns and maximized information gain were further refined by using p–norm distance measures, matching the alloy classification groups adopted by industry. Lastly, the evolutionary method of propagating an ensemble of competing cluster–based models proved to be a viable option in dealing with scarce, multidimensional data.

36 MATERIALS SCIENCE↗

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

Abstract The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity ( w’ ) ( gamma ) and the damping of the w’ skewness ( c8 ) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance ( c1 ) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

54 ENVIRONMENTAL SCIENCES↗

Abridged spectral matrix inversion: parametric fitting of X-ray fluorescence spectra following integrative data reduction

Recent improvements in both X-ray detectors and readout speeds have led to a substantial increase in the volume of X-ray fluorescence data being produced at synchrotron facilities. This in turn results in increased challenges associated with processing and fitting such data, both temporally and computationally. Herein an abridging approach is described that both reduces and partially integrates X-ray fluorescence (XRF) data sets to obtain a fivefold total improvement in processing time with negligible decrease in quality of fitting. The approach is demonstrated using linear least-squares matrix inversion on XRF data with strongly overlapping fluorescent peaks. This approach is applicable to any type of linear algebra based fitting algorithm to fit spectra containing overlapping signals wherein the spectra also contain unimportant (non-characteristic) regions which add little (or no) weight to fitted values, e.g. energy regions in XRF spectra that contain little or no peak information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

An Investigation of Hydrometeor Latent Cooling upon Convective Cold Pool Formation, Sustainment, and Properties

Downdrafts extending from convective clouds can produce cold pools that propagate outward, sometimes initiating new convection along their leading edges. Models operating at scales requiring convective parameterizations usually lack representation of this detail, and thus fail to predict this convective regeneration and longer episodes of convective activity. Developing such parameterizations requires an improved understanding of the physical drivers of cold pools, and detailed studies of the roles of all the contributing microphysical processes have been lacking. This study utilizes a set of 12 simulations conducted within a single convective environment, but with variability in the microphysical fields produced by varying parameters influencing warm-rain or ice processes. Time-integrated microphysical budgets quantify the contribution of each hydrometeor type to the total latent cooling occurring in the downdrafts that form and sustain the cold pool. The timing of the onset of the cold pool is earlier in cases with a stronger warm rain process, but both graupel and rain were equally as likely to be the dominant hydrometeor in the downdraft first forming the cold pool. Graupel sublimation is the dominant term in sustaining the cold pool in all simulations, but the evaporation of rain has the strongest correlation to the cold pool expansion rate, depth, and intensity. Reconciling the current results with past studies elucidates the importance of considering: graupel sublimation, the latent cooling only in downdrafts contributing to the cold pool, and latent cooling in those downdrafts at altitudes that may be significantly higher than the melting level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration?

Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single robust method exists for assessing the calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes in liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum ( γ ), the radar mean Doppler velocity ( W ), and the liquid water path (LWP). For each relation, we evaluate the potential for radar calibration. For γ and W , we use box model simulations to determine typical radar reflectivity values for reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz K a -band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that liquid cloud processes are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that, in 2016, the radar reflectivity at NSA was about 1±1  dB too low but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

54 ENVIRONMENTAL SCIENCES↗

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗

Method to Extract System-Independent Material Properties From Dual-Energy X-Ray CT

In 2016, we published a method for processing dual-energy computed tomography (DECT) data called system-independent rho-e/Z-e (ρ e /Z e ) or SIRZ. Using data from multiple DECT systems and spectra, SIRZ estimated the electron density ρ e and effective atomic number Z e (based on published X-ray cross sections), for a set of known specimens. However, the decomposition process required complex spectral modeling of the DECT system, which made SIRZ difficult to implement and automate. This paper describes the subsequent work on “SIRZ-2” to simplify the spectral modeling, automate the process, and improve its range and versatility. The SIRZ-2 basis functions are more accurate for the X-ray energy range (<;200 keV) and materials that are common to DECT applications in security and nondestructive characterization. To evaluate the SIRZ-2 performance, eight well-characterized specimens (with Z e of 6-20) were scanned with multiple spectral pairs (up to 200 keV) on seven different DECT systems, including a commercial airport luggage scanner. For these varied tests, the average SIRZ-2 relative errors for ρ e estimates were four times lower (0.7% compared to 3.0% for SIRZ), while average Z e relative errors were comparably low for both methods (<;1.5%). Average precision errors were also low (<;2.3% for both). SIRZ-2 produces accurate, repeatable, and system-independent material property estimates of (ρ e , Z e ) that can be directly compared across DECT systems and over time.

42 ENGINEERING↗

Blazar Jets Perturbed by Magneto-gravitational Stresses in Supermassive Binaries

We study particle acceleration and radiative processes in blazar jets under recurring conditions set by gravitational perturbations in supermassive binary systems. We consider the action from a companion orbiting a primary black hole of ∼10{sup 8} M{sub ⊙}, and perturbing its relativistic jet. We discuss how such conditions induce repetitive magneto-hydrodynamic stresses along the jet, and affect its inner electron acceleration and radiative processes. Specifically, we study how macroscopic perturbations related to increased jet “magnetization” end up in higher radiative outputs in the optical, X-ray, and gamma-ray bands. We first find an increase in magnetic field strength, as gauged in the optical band from the synchrotron emission of electrons accelerated in kinetic processes stimulated by reconnecting magnetic lines. The energetic electrons then proceed to up-scatter the synchrotron photons to GeV energies after the canonical synchrotron self-Compton radiation process. Our model implies a specific recurring pattern in the optical to gamma-ray emissions, made of high peaks and wide troughs. Progressing accelerations caused by spreading reconnections will produce an additional synchrotron keV component. Such outbursts provide a diagnostics for enhanced acceleration of electrons, which can up-scatter photons into the TeV range. We discuss how our model applies to the BL Lac object PG 1553+113, arguably the best candidate to now for high amplitude, recurring modulations in its gamma-ray emissions. We also consider other BL Lacs showing correlated keV–TeV radiations such as Mrk 421.

79 ASTRONOMY AND ASTROPHYSICS↗

Machine-learning-informed scattering correlation analysis of sheared colloids

We have carried out theoretical analysis, Monte Carlo simulations and machine-learning analysis to quantify microscopic rearrangements of dilute dispersions of spherical colloidal particles from coherent scattering intensity. Both monodisperse and polydisperse dispersions of colloids were created and underwent a rearrangement consisting of an affine simple shear and non-affine rearrangement using the Monte Carlo method. We calculated the coherent scattering intensity of the dispersions and the correlation function of intensity before and after the rearrangement and generated a large data set of angular correlation functions for varying system parameters, including number density, polydispersity, shear strain and non-affine rearrangement. Singular value decomposition of the data set shows the feasibility of machine-learning inversion from the correlation function for the polydispersity, shear strain and non-affine rearrangement using only three parameters. A Gaussian process regressor is then trained on the data set and can retrieve the affine shear strain, non-affine rearrangement and polydispersity with relative errors of 3%, 1% and 6%, respectively. Altogether, our model provides a framework for quantitative studies of both steady and non-steady microscopic dynamics of colloidal dispersions using coherent scattering methods.

Gaussian process regression↗

BEAM: A Computational Workflow System for Managing and Modeling Material Characterization Data in HPC Environments

Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now—with the rise of multimodal acquisition systems and the associated processing capability—the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalable data analysis and simulation via an intuitive, cross-platform client user interface. This framework delivers authenticated, “push-button” execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing the converged compute-and-data infrastructure at Oak Ridge National Laboratory's (ORNL) Compute and Data Environment for Science (CADES) and HPC environments like Titan at the Oak Ridge Leadership Computing Facility (OLCF). In this work we address the underlying HPC needs for characterization in the material science community, elaborate how BEAM's design and infrastructure tackle those needs, and present a small sub-set of user cases where scientists utilized BEAM across a broad range of analytical techniques and analysis modes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

SPRUCE Phospholipid Fatty Acid (PLFA) Abundances, August 2021-June 2022

This data set provides the results for phospholipid fatty acid analysis (PLFA) of peat samples collected from ambient plots in the SPRUCE experiment site, with experimental plot samples still being processed. The samples used to generate this data set were collected on 2021-08-23 and 2022-06-21. This data set includes abundances for groups of lipids indicating total biomass, fungi, Gram-positive bacteria, Gram-negative bacteria, actinomycetes, and anaerobic bacteria. This data set contains one file in comma separate (*.csv) format. Samples: On each sampling date, cores extending to 200 cm were collected and subsampled at 10 cm intervals from 0 and 100 cm and 25 cm intervals below 100 cm depth in Plot 7 and 21. During the lipid extraction procedure, samples were pooled into the following depth increments in order to obtain sufficient material to achieve adequate lipid yield: 0-10, 10-20, 20-30, 30-50, 50-100, and 100-150, and 150-200 cm.

actinomycetes↗

Description of FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

The urgency to deliver fusion power is growing now more than ever, with increasing pressure for both public programs and private companies to meet milestones timelines and overcome significant remaining technical challenges to ensure growth of a nascent fusion industry in time to meet rapidly growing clean energy demands. With incredible advancements in computation and years of investment in fusion model development and validation, integrated modeling is poised to fill a key role in accelerating the timeline to a fusion pilot plant (FPP). Future fusion pilot plants will operate in regimes far beyond current experience, and device design will rely on physics-based prediction and extrapolation. Many concepts will also rely on simulation to assess safety (shielding, tritium management, materials activation and lifetimes), economics and scalability before the decision to build. Importantly, integrated simulation can be used to reveal and solve the complexities of system integration that may otherwise not be apparent in physical components or models developed in isolation. New experimental test facilities that produce relevant conditions to validate and resolve key technical challenges for various subsystems (materials, blankets, fuel cycle, etc.) have been repeatedly called for by the fusion community but are not yet realized. Integrated modeling has an important role in identifying realistic load conditions (thermal, electromagnetic, plasma, neutron and photon loads, etc.) and defining the components and experiments for these test facilities in order to ensure meaningful validation that sufficiently reduces modeling uncertainties and technical risk for the full integrated reactor. The Fusion REactor Design and Assessment (FREDA) SciDAC project is building a component-based integrated modeling framework & data structure to enable self-consistent, multi-fidelity, iterative optimization workflows for the fusion reactor design process. FREDA aims to shorten the time to viable designs by providing a set of flexible workflows to support the various stages of the design process using an integrated model hierarchy, ranging from the simple analytic descriptions to the highest fidelity, theory-based plasma and engineering modeling developed by the fusion and fission communities. These tools are expected to be needed for timely support of FPP design in the milestone program and in the FIRE collaboratives. The plasma simulation backbone of FREDA is IPS-FASTRAN with newly developed coupled Core-Edge Pedestal-SOL (CESOL) workflows, which is being extended to the far-SOL region up to the plasma facing components. FREDA incorporates the FERMI engineering modeling suite and will enable self-consistent evaluation of the thermal shields, limiters, blanket, magnets, and other surrounding structures with predictions of temperatures, erosion, dpa, activation, tritium generation and transport, creep, corrosion, material degradation, etc. Parametric generation of 3D CAD enables rapid iteration of component geometry in response to plasma and loading specifications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

PFLOTRAN-E4D: A parallel open source PFLOTRAN module for simulating time-lapse electrical resistivity data

Time-lapse electrical resistivity tomography (ERT) is finding increased application for remotely monitoring processes occurring in the near subsurface in three-dimensions (i.e. 4D monitoring). However, there are few codes capable of simulating the evolution of subsurface resistivity and corresponding tomographic measurements arising from a particular process, particularly in parallel and with an open source license. Herein we describe and demonstrate an electrical resistivity tomography module for the PFLOTRAN subsurface simulation code, named PFLOTRAN-E4D. The PFLOTRAN-E4D module operates in parallel using a dedicated set of compute cores in a master-slave configuration. At each time step, the master processes receives subsurface states from PFLOTRAN, converts those states to bulk electrical conductivity, and instructs the slave processes to simulate a tomographic data set. The resulting multi-physics simulation capability enables accurate feasibility studies for ERT imaging, the identification of the ERT signatures that are unique to a given process, and facilitates the joint inversion of ERT data with hydrogeological data for subsurface characterization. PFLOTRAN-E4D is demonstrated herein using a field study of stage-driven groundwater/river water interaction ERT monitoring along the Columbia River, Washington, USA. Results demonstrate the complex nature of changes subsurface electrical conductivity, in both the saturated and unsaturated zones, arising from water table changes and from river water intrusion into the aquifer. The results also demonstrate the sensitivity of surface based ERT measurements to those changes over time. PFLOTRAN-E4D is available with the PFLOTRAN development version with an open-source license at https://bitbucket.org/pflotran/pflotran-dev .

Environmental Molecular Sciences Laboratory↗

Electrically-Assisted Tubing Processes for Enhancing Manufacturability of Oxide Dispersion Strengthened Structural Materials for Nuclear Reactor Applications

The overall goal of this project is to enhance the formability of Nanostructured Ferritic Alloys (NFAs) such that high-strength thin-walled claddings can be successfully and economically manufactured for use in future nuclear reactors. Specifically, four objectives were identified: Objective #1 – to achieve a good understanding of the mechanical behavior and resulting microstructure of 14YWT made from the powder form subjected to various Electrically- Assisted (EA) loading conditions. Objective #2 – to conduct EA-flat rolling of 14YWT completed and the prioritization/selection of two alternative designs of EA tube rolling processes. Objective #3 – to design and assemble a new EA tube rolling machine. Objective #4 – to conduct rolling using the new EA rolling machine. The above objectives were achieved and the findings can be further applied to establish other innovative fabrication processes. Specifically, the major accomplishments are summarized as: The use of an EA forming process was investigated to reduce the forming force of ODS ferritic steels. The effect of a continuous electric current on 14YWT during plastic deformation was evaluated. EA uniaxial tension tests show that the electric current is able to reduce the flow stress of 14YWT. 14YWT exhibits a ductility minimum at a current density of 34.5 A/mm2 which is consistent with fractography results. Hardness measurements and TEM results indicate that oxide particles are stable during the EA process. By following the tests, microstructure analysis was performed on the sample in its as-received state and after current treatment. The microstructures are stable when subjected to high current density. The related thermal model was developed to predict the Joule heating temperatures. The temperatures were compared with the experimental results. At last, a temperature-dependent hardening model was used to explore the mechanisms acting during EA deformation. Since the model can accurately predict the yield strength of 14YWT under EA conditions without the incorporation of non-thermal effects, it can be concluded that the reduction in yield strength is due to the Joule heating generated under continuous current loading conditions. An oven-heated pure thermal tension test was conducted to confirm this conclusion. The new EA tube rolling machine has been completely developed. The related work includes: (1) forming process design; (2) analysis of the effect of roll-tube contact geometry on rolling force; (3) analysis of machine performance under mechanical loading; (4) design of machine electrical setting path and insulation; (5) multi-pass process analysis; (6) design of power transmission system (axial/radial/torque feeding mechanisms), roll bearing system, cooling system, workpiece feeding mechanism, and other related mechanical connection support components; (7) design and optimization of roll profiles/dimensions; (8) analysis and simulation of the rolling process; (9) development of engineering drawings and fabrication; (10) assembly and calibration of the EA tube rolling machine; (11) design and integration of the electrical control system; (12) hardware integration for high power supply electrically-assisted rolling process; (13) induction heating system integration; (14) experimental tests for the new rolling process. Through the process from conceptual design to final machine calibration and experimental tests, there are many technical and physical works that have been implemented and improved for the best performance of the innovative prototype.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Impact of T - and ρ -dependent decay rates and new (n, γ ) cross-sections on the s process in low-mass asymptotic giant branch stars

Aims. We study the impact of nuclear input related to weak-decay rates and neutron-capture reactions on predictions for the slow neutron-capture process (s process) in asymptotic giant branch (AGB) stars. We provide the first database of surface abundances and stellar yields of the isotopes heavier than iron from the Monash models. Methods. We ran nucleosynthesis calculations with the Monash post-processing code for seven stellar structure evolution models of low-mass AGB stars with three different sets of nuclear inputs. The reference set has constant decay rates and represents the set used in the previous Monash publications. The second set contains the temperature and density dependence of β decays and electron captures based on the default rates of nuclear NETwork GENerator (NETGEN). In the third set, we further update 92 neutron-capture rates based on re-evaluated experimental cross sections from the ASTrophysical Rate and rAw data Library. We compare and discuss the predictions of the sets relative to each other in terms of isotopic surface abundances and total stellar yields. We also compare the results to isotopic ratios measured in presolar stardust silicon carbide (SiC) grains from AGB stars. Results. The new sets of models result in a ∼66% solar s-process contribution to the p-nucleus 152 Gd, confirming that this isotope is predominantly made by the s process. The nuclear input updates result in predictions for the 80 Kr/ 82 Kr ratio in the He intershell and surface 64 Ni/ 58 Ni, 94 Mo/ 96 Mo, and 137 Ba/ 136 Ba ratios that are more consistent with the corresponding ratios measured in stardust; however, the new predicted 138 Ba/ 136 Ba ratios are higher than the typical values of the SiC grains. The W isotopic anomalies are in agreement with data from the analyses of other meteoritic inclusions. We confirm that the production of 176 Lu and 205 Pb is affected by too large uncertainties in their decay rates from NETGEN.

79 ASTRONOMY AND ASTROPHYSICS↗

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed. Finally, the proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state-of-the-art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes.

42 ENGINEERING↗

Lignite-plus-Biomass to Synthetic Jet Fuel with CO2 Capture and Storage: Design, Cost, and Greenhouse Gas Emissions Analysis for a Near-Term First-of-a-Kind Demonstration Project and Prospective Future Commercial Plants

We report on a 30-month design study for a first-of-a-kind (FOAK) demonstration plant that would be built at a site near Meridian, Mississippi, to coprocess lignite coal and woody biomass into jet fuel. The design uses an oxygen-blown TRIG™ gasifier developed by KBR and Southern Company. Fischer-Tropsch conversion of the syngas produces synthetic paraffinic kerosene (SPK) as the primary product, plus naphtha. Other co-products include electricity sold to the grid and CO 2 sold for use in enhanced oil recovery (EOR). Previous studies have identified coprocessing of various coals and biomass with CO 2 capture as promising options for cost-competitive production of low net lifecycle greenhouse gas (GHG) emissions synthetic fuels. The effort reported here goes beyond earlier studies in the level of detail in process design and cost estimation with the aim of improving the understanding of the economic prospects for lignite and woody biomass coprocessing systems. Key objectives in the design of the FOAK lignite/biomass-to-jet (LBJ) plant were to achieve net lifecycle GHG emissions for the SPK that are less than for conventional petroleum-derived jet fuel and to make process design and equipment selections such that the plant could be built and operated in the near term, e.g., before 2025. The process design was developed by researchers at Princeton University and the University of Queensland and validated by engineers at the WorleyParsons Group (WP). Commercial vendors provided design and cost estimates for several major plant components. Bare-erected capital costs and operating and maintenance costs were estimated by WP. The primary objective in building and operating the FOAK plant would be to demonstrate the technical viability of the LBJ concept as an essential first step toward launching commercial-scale plants in the longer term. With this in mind, the project team developed a set of principles to help guide the process design: the total plant cost should be less than 2 billion USD (to limit investment risk); the level of input biomass should be both proportionally significant to reflect biomass/lignite co-gasification and sufficient to achieve GHG emissions goals; process design decisions and vendor/equipment selections should prioritize the likelihood of technical success over minimizing the cost of jet fuel production. The resulting FOAK plant design capacity is 1,551 metric t/d (45.5% moisture) lignite and 556 t/d (43.3% moisture) biomass, for a total input of 295 MW HHV , of which 25% is biomass. The design output is 1,252 actual barrels per day of liquids, of which 80% is SPK (62.3 MW LHV ) and 20% is naphtha (13.9 MW LHV ). It exports 15 MW e of electricity after satisfying a 38 MW e onsite auxiliary load. Additional products are 1,326 t/d of pressurized pipeline-quality CO 2 and 49 t/day of sulfuric acid (93 wt% H 2 SO 4 ). With thinnings from sustainably-managed southern pine plantations as the biomass, and with captured CO 2 stored underground via EOR, the net lifecycle emissions for the SPK product are estimated to be about one-quarter of those for petroleum-derived jet fuel. The bare-erected cost (BEC) estimated by WP for this plant is 588 million USD (2015). The authors’ best estimate of total plant cost (TPC) is 1,230 million USD, arrived at by assuming engineering, procurement and construction management services (20% of BEC), process 5 contingencies (35% of BEC), and project contingencies (35% of the sum of all other costs, i.e., 35% of 1.55xBEC). Not surprisingly, an annual discounted cash flow (DCF) analysis determined that it would be impossible to generate a positive net present value (NPV) over a wide range in key input assumptions. An SPK production cost subsidy of nearly 400/bbl USD over a 20-year plant life would be required to achieve zero NPV under a baseline set of assumptions (including 3% real weighted average cost of capital and a levelized crude oil price of 80/bbl USD). Alternatively, a capital grant in excess of the TPC value would also achieve zero NPV. The poor financial results reflect the small scale of the plant, the design principle to prioritize technical success, the levels of contingencies appropriate for the relatively early stage of project development, and the first-of-a-kind nature of the plant. Technology innovations, learning via construction and operating experience, alternative plant configurations, and larger scale should improve economics of future plants. To help understand these prospects, a preliminary analysis of N th -of-a-kind (NOAK) plants was developed, but with the limitation that plants would use only equipment components that for the most part are already commercial today: consideration of advanced, not-yet-commercial technologies and of R&D-driven improvements in existing technologies were beyond the scope of this analysis. The analysis found that a variety of NOAK plant designs that coprocess lignite and woody biomass to make jet fuel are unlikely to be economically competitive without subsidy even in the presence of a high future carbon tax or equivalent greenhouse gas mitigation policy. This conclusion applies to process configurations and input biomass/lignite ratios that result in net GHG emissions as high as those for petroleum-derived jet fuel and as low as zero. In contrast, encouraging results were found for plants processing only biomass. The economics of these “BECCS” plants (biomass energy with CCS) improve dramatically with the strength of carbon mitigation policies because of their strongly negative net GHG emissions. These findings do not imply that coal/biomass coprocessing strategies for making synfuels with CCS are not economically promising – only that, in the case of lignite, much more than a 25% biomass coprocessing rate would be needed. However, our analysis shows that all such systems are unlikely to be economic in the absence of a strong carbon mitigation policy. Future R&D driven technological innovations could modify this conclusion. Among other R&D priorities, an emphasis on better understanding and reducing plant auxiliary loads is warranted.

09 BIOMASS FUELS↗