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DOE OSTI2023

Why the observed spin evolution of older-than-solar-like stars might not require a dynamo mode change

ABSTRACT The spin evolution of main-sequence stars has long been of interest for basic stellar evolution, stellar ageing, stellar activity, and consequent influence on companion planets. Observations of older-than-solar late-type main-sequence stars have been interpreted to imply that a change from a dipole-dominated magnetic field to one with more prominent higher multipoles might be necessary to account for the data. The spin-down models that lead to this inference are essentially tuned to the Sun. Here, we take a different approach that considers individual stars as fixed points rather than just the Sun. We use a time-dependent theoretical model to solve for the spin evolution of low-mass main-sequence stars that includes a Parker-type wind and a time-evolving magnetic field coupled to the spin. Because the wind is exponentially sensitive to the stellar mass over radius and the coronal base temperature, the use of each observed star as a separate fixed point is more appropriate and, in turn, produces a set of solution curves that produces a solution envelope rather than a simple line. This envelope of solution curves, unlike a single line fit, is consistent with the data and does not unambiguously require a modal transition in the magnetic field to explain it.

Astronomy & Astrophysics↗

DOE OSTI2024

Reinforcement expectation in the honeybee ( Apis mellifera ): Can downshifts in reinforcement show conditioned inhibition?

When animals learn the association of a conditioned stimulus (CS) with an unconditioned stimulus (US), later presentation of the CS invokes a representation of the US. When the expected US fails to occur, theoretical accounts predict that conditioned inhibition can accrue to any other stimuli that are associated with this change in the US. Empirical work with mammals has confirmed the existence of conditioned inhibition. But the way it is manifested, the conditions that produce it, and determining whether it is the opposite of excitatory conditioning are important considerations. Invertebrates can make valuable contributions to this literature because of the well-established conditioning protocols and access to the central nervous system (CNS) for studying neural underpinnings of behavior. Nevertheless, although conditioned inhibition has been reported, it has yet to be thoroughly investigated in invertebrates. Here, we evaluate the role of the US in producing conditioned inhibition by using proboscis extension response conditioning of the honeybee (Apis mellifera). Specifically, using variations of a “feature-negative” experimental design, we use downshifts in US intensity relative to US intensity used during initial excitatory conditioning to show that an odorant in an odor–odor mixture can become a conditioned inhibitor. We argue that some alternative interpretations to conditioned inhibition are unlikely. However, we show variation across individuals in how strongly they show conditioned inhibition, with some individuals possibly revealing a different means of learning about changes in reinforcement. We discuss how the resolution of these differences is needed to fully understand whether and how conditioned inhibition is manifested in the honeybee, and whether it can be extended to investigate how it is encoded in the CNS. It is also important for extension to other insect models. In particular, work like this will be important as more is revealed of the complexity of the insect brain from connectome projects.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Mott gapping in 3 d A B O 3 perovskites without Mott-Hubbard interelectronic repulsion energy U

The existence of band gaps in Mott insulators such as perovskite oxides with partially filled 3d shells has been traditionally explained in terms of strong, dynamic interelectronic repulsion codified by the on-site repulsion energy U in the Hubbard Hamiltonian. The success of the “DFT+U approach” where an empirical on-site potential term U is added to the exchange- and correlation density functional theory (DFT) raised questions on whether U in DFT+U represents interelectronic correlation in the same way as it does in the Hubbard Hamiltonian, and if empiricism in selecting U can be avoided. Here we illustrate that ab initio DFT without any U is able to predict gapping trends and structural symmetry breaking (octahedra rotations, Jahn-Teller modes, bond disproportionation) for all 3dABO 3 perovskites from titanates to nickelates in both spin-ordered and spin-disordered paramagnetic phases. Thus, the mechanism of gap formation due to the Hubbard Hamiltonian dynamic interelectronic correlation is not a requirement in these 3d electron compounds. Here, we describe the paramagnetic phases as a supercell where individual sites can have different local environments thereby allowing DFT to develop finite moments on different sites as long as the total cell has zero moment. We use the recently developed strongly constrained appropriately normed exchange and correlation functional (SCAN) that is sanctioned by the usual single-determinant, mean-field DFT paradigm with static correlations, but has a more precise rendering of self-interaction cancelation. Our results suggest that strong dynamic electronic correlations are not playing a universal role in gapping of 3dABO 3 Mott insulators, and opens the way for future applications of DFT for studying a plethora of complexity effects that depend on the existence of gaps, such as doping, defects, and band alignment in ABO 3 oxides.

36 MATERIALS SCIENCE↗

DOE OSTI2017

P -wave π π scattering and the ρ resonance from lattice QCD

Here we calculate the parameters describing elastic I = 1, P-wave ππ scattering using lattice QCD with 2 +1 flavors of clover fermions. Our calculation is performed with a pion mass of m π ≈ 320 MeV and a lattice size of L ≈ 3.6 fm. We construct the two-point correlation matrices with both quark-antiquark and two-hadron interpolating fields using a combination of smeared forward, sequential and stochastic propagators. The spectra in all relevant irreducible representations for total momenta |$\vec{P}$| ≤ $\sqrt{3}\frac{2π}{L}$ are extracted with two alternative methods: a variational analysis as well as multiexponential matrix fits. We perform an analysis using Lüscher’s formalism for the energies below the inelastic thresholds, and investigate several phase shift models, including possible nonresonant contributions. We find that our data are well described by the minimal Breit-Wigner form, with no statistically significant nonresonant component. In determining the ρ resonance mass and coupling we compare two different approaches: fitting the individually extracted phase shifts versus fitting the t-matrix model directly to the energy spectrum. We find that both methods give consistent results, and at a pion mass of am π = 0.18295 (36) stat obtain gρππ = 5.69(13) stat (16) sys , am ρ = 0.4609(16) stat (14) sys , and am ρ /am N = 0.7476(38) stat (23) sys , where the first uncertainty is statistical and the second is the systematic uncertainty due to the choice of fit ranges.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Proton tensor charges from a Poincaré-covariant Faddeev equation

The proton’s tensor charges are calculated at leading order in a symmetry-preserving truncation of all matter-sector equations relevant to the associated bound-state and scattering problems. In particular, the nucleon three-body bound-state equation is solved without using a diquark approximation of the two-body scattering kernel. The computed charges are similar to those obtained in contemporary simulations of lattice-regularized quantum chromodynamics, an outcome which increases the tension between theory and phenomenology. Curiously, the theoretical calculations produce a value of the scale-invariant ratio (–δ T d/δ T u) which matches that obtained in simple quark models, even though the individual charges are themselves different. As a result, the proton's tensor charges can be used to constrain extensions of the Standard Model using empirical limits on nucleon electric dipole moments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Thermal transport in dimerized harmonic lattices: Exact solution, crossover behavior, and extended reservoirs

In this paper, we present a method for calculating analytically the thermal conductance of a classical harmonic lattice with both alternating masses and nearest-neighbor couplings when placed between individual Langevin reservoirs at different temperatures. The method utilizes recent advances in analytic diagonalization techniques for certain classes of tridiagonal matrices. It recovers the results from a previous method that was applicable for alternating on-site parameters only, and extends the applicability to realistic systems in which masses and couplings alternate simultaneously. With this analytic result in hand, we show that the thermal conductance is highly sensitive to the modulation of the couplings. This is due to the existence of topologically induced edge modes at the lattice-reservoir interface and is also a reflection of the symmetries of the lattice. We make a connection to a recent work that demonstrates thermal transport is analogous to chemical reaction rates in solution given by Kramers' theory [Velizhanin et al., Sci. Rep. 5, 17506 (2015)]. In particular, we show that the turnover behavior in the presence of edge modes prevents calculations based on single-site reservoirs from coming close to the natural—or intrinsic—conductance of the lattice. Obtaining the correct value of the intrinsic conductance through simulation of even a small lattice where ballistic effects are important requires quite large extended reservoir regions. Finally, our results thus offer a route for both the design and proper simulation of thermal conductance of nanoscale devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Enhanced γ -Ray Emission from Neutron Unbound States Populated in β Decay

Total absorption spectroscopy was used to investigate the β -decay intensity to states above the neutron separation energy followed by γ -ray emission in 87,88 Br and 94 Rb. Accurate results were obtained thanks to the careful control of systematic errors. An unexpectedly large γ intensity was observed in all three cases extending well beyond the excitation energy region where neutron penetration is hindered by low neutron energy. The γ branching as a function of excitation energy was compared to Hauser-Feshbach model calculations. For 87 Br and 88 Br the branching reaches 57% and 20% respectively, and could be explained as a nuclear structure effect. Some of the states populated in the daughter can only decay through the emission of a large orbital angular momentum neutron with a strongly reduced barrier penetrability. In the case of neutron-rich 94 Rb the observed 4.5% branching is much larger than the calculations performed with standard nuclear statistical model parameters, even after proper correction for fluctuation effects on individual transition widths. The difference can be reconciled introducing an enhancement of one order-of-magnitude in the photon strength to neutron strength ratio. An increase in the photon strength function of such magnitude for very neutron-rich nuclei, if it proved to be correct, leads to a similar increase in the (n, γ) cross section that would have an impact on r process abundance calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2014

The first X-ray diffraction measurements on Mars

The Mars Science Laboratory landed in Gale crater on Mars in August 2012, and the Curiosity rover then began field studies on its drive toward Mount Sharp, a central peak made of ancient sediments. CheMin is one of ten instruments on or inside the rover, all designed to provide detailed information on the rocks, soils and atmosphere in this region. CheMin is a miniaturized X-ray diffraction/X-ray fluorescence (XRD/XRF) instrument that uses transmission geometry with an energy-discriminating CCD detector. CheMin uses onboard standards for XRD and XRF calibration, and beryl:quartz mixtures constitute the primary XRD standards. Four samples have been analysed by CheMin, namely a soil sample, two samples drilled from mudstones and a sample drilled from a sandstone. Rietveld and full-pattern analysis of the XRD data reveal a complex mineralogy, with contributions from parent igneous rocks, amorphous components and several minerals relating to aqueous alteration. In particular, the mudstone samples all contain one or more phyllosilicates consistent with alteration in liquid water. In addition to quantitative mineralogy, Rietveld refinements also provide unit-cell parameters for the major phases, which can be used to infer the chemical compositions of individual minerals and, by difference, the composition of the amorphous component.

Chemistry↗

DOE OSTI2015

Ultraviolet Radiation Round-Robin Testing of Various Backsheets for Photovoltaic Modules

Durability testing of materials exposed to natural weathering requires testing of the ultraviolet (UV) stability, especially for polymeric materials. The type approval testing of photovoltaic (PV) modules according to standards IEC 61215 and IEC 61646, which includes a so-called UV preconditioning test with a total UV dose of 15 kWh/m2, does not correspond to the real loads during lifetime. Between 3%-10% of the UV radiation has to be in the spectral range between 280 and 320 nm (UV-B) in the recent editions of the standards. However, the spectral distribution of the radiation source is very important because different samples show very individual spectral sensitivity for the radiation offered. Less than 6% of the intensity of solar radiation exists in the UV range. In the case of an increase of the intensity of the light source for accelerating the UV test, overheating of the samples would have to be prevented more rigorously and the temperature of the samples have to be measured to avoid misinterpretation of the test results.

14 SOLAR ENERGY↗

DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗

DOE OSTI2018

The role of alanine and aspartate aminotransferases in C 4 photosynthesis

Alanine and aspartate are essential transfer metabolites for C 4 species of the NAD-malic enzyme and the phosphoenolpyruvate carboxykinase subtype. To some degree both amino acids are also part of the metabolite shuttle in NADP-malic enzyme plants. In comparison with C 3 species, the majority of C 4 species are therefore characterised by enhanced expression and activity of alanine and aspartate aminotransferases in the photosynthetically active tissue. Both enzymes exist in multiple copies and have been found in different subcellular compartments. We tested whether different C 4 species show preferential recruitment of enzymes from specific lineages and subcellular compartments. Phylogenetic analysis of alanine and aspartate aminotransferases from a variety of monocot and eudicot C 4 species and their C 3 relatives was combined with subcellular prediction tools and the analysis of the according transcript amounts in mature leaves. Recruitment of aspartate aminotransferases from a specific subcellular compartment was strongly connected to the biochemical subtype. Deviation from the main model was however observed in G. gynandra. The configuration of alanine aminotransferases was generally different in monocot and eudicot species. C 4 monocots recruited an alanine aminotransferase from a specific cytosolic branch, but eudicots use alanine aminotransferase copies from a mitochondrial branch. Generally, plants display high plasticity in the setup of the C 4 pathway. Beside the common models for the different C 4 subtypes, individual solutions were found for plant groups or lineages.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Code Verification of Multiple Physics-Fidelity Models in Hypersonic Aerodynamics

Hypersonic aerodynamics models exist across a range of physics fidelities with associated computational expenses. These models may be run independently or in a multifidelity framework that leverages their complementary strengths of speed for lower-fidelity and accuracy for higher-fidelity models. This work presents applied code verification of two lower-fidelity models contained within the Sandia hypersonic aerodynamics code. Each model has a different form that requires individualized verification approaches, including comparison to analytical solutions as well as manufactured solutions with order-of-accuracy testing. In conclusion, results of this effort include the identification and resolution of code errors and shortcomings, as well as the demonstration of code correctness and consistency for both models.

Aerodynamics↗

DOE OSTI2015

WE-AB-204-09: Respiratory Motion Correction in 4D-PET by Simultaneous Motion Estimation and Image Reconstruction (SMEIR)

Purpose: In conventional 4D-PET, images from different frames are reconstructed individually and aligned by registration methods. Two issues with these approaches are: 1) Reconstruction algorithms do not make full use of all projections statistics; and 2) Image registration between noisy images can Result in poor alignment. In this study we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) method for cone beam CT for motion estimation/correction in 4D-PET. Methods: Modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM- TV) is used to obtain a primary motion-compensated PET (pmc-PET) from all projection data using Demons derived deformation vector fields (DVFs) as initial. Motion model update is done to obtain an optimal set of DVFs between the pmc-PET and other phases by matching the forward projection of the deformed pmc-PET and measured projections of other phases. Using updated DVFs, OSEM- TV image reconstruction is repeated and new DVFs are estimated based on updated images. 4D XCAT phantom with typical FDG biodistribution and a 10mm diameter tumor was used to evaluate the performance of the SMEIR algorithm. Results: Image quality of 4D-PET is greatly improved by the SMEIR algorithm. When all projections are used to reconstruct a 3D-PET, motion blurring artifacts are present, leading to a more than 5 times overestimation of the tumor size and 54% tumor to lung contrast ratio underestimation. This error reduced to 37% and 20% for post reconstruction registration methods and SMEIR respectively. Conclusion: SMEIR method can be used for motion estimation/correction in 4D-PET. The statistics is greatly improved since all projection data are combined together to update the image. The performance of the SMEIR algorithm for 4D-PET is sensitive to smoothness control parameters in the DVF estimation step.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Convective Transition Statistics over Tropical Oceans for Climate Model Diagnostics: GCM Evaluation

To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗

DOE OSTI2015

2D Gridded Surface Data Value-Added Product

This report describes the Atmospheric Radiation Measurement (ARM) Best Estimate (ARMBE) 2-dimensional (2D) gridded surface data (ARMBE2DGRID) value-added product. Spatial variability is critically important to many scientific studies, especially those that involve processes of great spatial variations at high temporal frequency (e.g., precipitation, clouds, radiation, etc.). High-density ARM sites deployed at the Southern Great Plains (SGP) allow us to observe the spatial patterns of variables of scientific interests. The upcoming megasite at SGP with its enhanced spatial density will facilitate the studies at even finer scales. Currently, however, data are reported only at individual site locations at different time resolutions for different datastreams. It is difficult for users to locate all the data they need and requires extra effort to synchronize the data. To address these problems, the ARMBE2DGRID value-added product merges key surface measurements at the ARM SGP sites and interpolates the data to a regular 2D grid to facilitate the data application.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

Applying Diagnostics to Enhance Cable System Reliability (Cable Diagnostic Focused Initiative, Phase II)

The Cable Diagnostic Focused Initiative (CDFI) played a significant and powerful role in clarifying the concerns and understanding the benefits of performing diagnostic tests on underground power cable systems. This project focused on the medium and high voltage cable systems used in utility transmission and distribution (T&D) systems. While many of the analysis techniques and interpretations are applicable to diagnostics and cable systems outside of T&D, areas such as generating stations (nuclear, coal, wind, etc.) and other industrial environments were not the focus. Many large utilities in North America now deploy diagnostics or have changed their diagnostic testing approach as a result of this project. Previous to the CDFI, different diagnostic technology providers individually promoted their approach as the “the best” or “the only” means of detecting cable system defects.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Slag Management of Carbon Feedstock Used in Gasification FY17 TCF between Eastman Chemical and NETL (Final Report)

NETL, in collaboration with Eastman Chemical Corp (Kingsport, TN), targeted the joint evaluation and refinement of a slag management program developed by NETL (NETL IP, patent applied for) to control gasifier slag properties formed from ash in individual or combinations of different coal-based carbon feedstock materials and specific slag modifiers used in gasification. Slag forms from mineral or organic-metallic impurities in the carbon feedstock that melt at the high temperatures of gasification, coalescing to form a liquid (slag) that must flow from the gasifier. Slag additives, also known as slag modifiers, are often added to the carbon feedstock to modify the ash chemistry and achieve desired slag flow properties at target temperatures used in gasification. If slag does not flow from a gasifier, its buildup within the gasification chamber will cause either gasifier shutdown to physically remove the slag (consuming time and damaging the protective refractory lining), or the gasification temperature must be increased to lower slag viscosity so it can flow from the gasifier, leading to greater refractory wear/corrosion, along with increased fouling and downstream component wear. Because of these issues, gasification is typically targeted at the lowest temperature possible where slag will flow in a controlled manner (a temperature range called T100 for the lowest temperature, with T250 being the highest temperature where the slag has achieved the desired fluidity). That temperature range also needs to yield a desired syngas composition, highlighting the importance of knowing and being able to control carbon feedstock slag viscosity and the temperature of thermal breakdown of the carbon feedstock. Slag viscosity varies with changes in the gasification chamber temperature, oxygen partial pressure, and slag chemistry. The slag model work with Eastman Chemical targeted predicting and controlling molten coal ash slag behavior at specific gasification conditions. The final goal of the refined model was to develop/refine the NETL slag model so a gasifier operator would be able to predict slag behavior prior to purchasing or using a carbon feedstock and control slag properties once a carbon feedstock is being gasified, allowing a gasifier user to determine what mineral additives or blends of different carbon materials would be needed to achieve desired slag properties at targeted gasification conditions. For a given carbon feedstock and desired gasification condition, the actual gasification cost and slag behavior would be able to be predicted using the slag management program in advance of purchasing or using the carbon feedstock – allowing accurate control of slag ash behavior in real time. As part of the TCF project work with Eastman Chemical, the viscosity of several ash compositions of interest to them would be measured and used to populate the slag model, along with a large database of historical slag temperature, chemistry, and viscosity information they possess – information that would in-crease the predictive accuracy of the slag management model with slag ash chemistry of interest to them. As part of the model evaluation and refinement, the predictive model and its database will be installed on computers at Eastman Chemical and used to manage slag properties in a gasifier during real-time operation of their gasifier. In summary, success of the slag management program at Eastman Chemical would support its use by other gasifier operators to manage slag chemistry and properties over other desired gasification temperature ranges, allowing greater process control and efficiency of their carbon feedstock conversion during gasification.

01 COAL, LIGNITE, AND PEAT↗