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DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗

DOE OSTI2021

Department of Energy Water Power Technologies Office Cyber Response & Recovery Flipbook [Slides]

Protecting hydroelectric plants from incidents that adversely impact their cyber-physical systems presents unique challenges due to the plants’ widely dispersed geographic locations and varied configurations as well as the relative nascent nature of the cyberattacks targeting these facilities. To help hydroelectric plants better respond to and mitigate cybersecurity incidents, this Department of Energy Water Power Technologies Office Cyber Response & Recovery Flipbook is to be used at a hydroelectric plant to quickly respond to an anomalous event. In addition to this product, there are three other products meant to be distributed to a hydroelectric plant to assist in their cyber incident response and recovery. The first, a report on the processes of building this flip book based on a large set of existing guidance. The second, a handy guide of hydroelectric and cyber guidance in responding to the cyber and physical systems within a hydroelectric plant. And the third is a correlated alignment of the steps an hydroelectric plant operator would take for both a cyber incident as well as an emergency response process if the event rises to a cyber incident affecting the safe and reliable operations of a hydroelectric plant.

13 HYDRO ENERGY↗

DOE OSTI2018

On the predictability of land surface fluxes from meteorological variables

Previous research has demonstrated that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear toprovide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

58 GEOSCIENCES↗

DOE OSTI2023

And manufacture of generalized flow profile-producing devices

The present invention provides a process for making a flow conditioning device that transforms an input flow into a desired output flow. The process includes the steps of inputting into a computer program a set of design constraints representative of the input flow and the output flow. The computer program generates a design representative of a flow-conditioning device that transforms the input flow into the output flow. The process then provides the output design to an additive manufacturing or other suitable production system adapted to form a solid representation of the flow-conditioning device.

Ferrar, Anthony M.↗

DOE OSTI2021

Modeling protein structures from predicted contacts with modern molecular dynamics potentials: accuracy, sensitivity, and refinement

Protein structure prediction has become increasingly popular and successful in recent years. An essential step for fragment-free, template-free methods is the generation of a final three-dimensional protein model from a set of predicted amino acid contacts that are often described by interresidue pairwise atomic distances. Here we explore the use of modern, open-source molecular dynamics (MD) engines, which have been continually developed over the last three decades with all-atom Hamiltonians to model biomolecular structure and dynamics, to generate accurate protein structures starting from a set of inferred pairwise distances. Additionally, the ability of MD empirical physical potentials to correct inaccuracies in the predicted geometries is tested. We rigorously characterize the effect of modeling parameters on results, the effect of different amounts of error in the predicted distances on the final structures, and test the ability of post-processing analysis to sort the best models out of a set of statistical replicas. We find that with exact distances and with noisy distances, the method can produce excellent structural models, and that the molecular dynamics force field seems to help correct errors in distance predictions, resisting the effects of applied noise.

Davidson, Russ↗

DOE OSTI2020

Automated construction of clear-sky dictionary from all-sky imager data

All-sky imagers (ASIs) have significant promise as scalable sensors for short-term solar irradiance forecasting. Many of the current computational techniques that use ASIs for this purpose rely on collections of clear-sky images indexed by time of day, solar angle, or both, called clear-sky dictionaries (CSDs). These CSDs act as baselines against which images can be compared to locate and classify clouds within the image frame. CSDs are often compiled by hand, where individuals visually inspect collections of images one at a time to find clear-sky images. This process is not scalable, and it is prone to error. This paper proposes an automated, nonparametric alternative that uses the principles of digital image processing to find clear-sky images within a set of images taken over several days. We use ground-truth measurements of the clearness index to assess the performance of our method, and we show that the images it selects accurately correspond to clear-sky images. We also compare our proposal, which is nonparametric, with a state-of-the-art parametric method. The numerical results indicate that the performance of the method proposed here is superior.

14 SOLAR ENERGY↗

DOE OSTI2016

Gas Atomization of Molten Metal: Part I. Numerical Modeling Conception

This numerical analysis study entails creating and assessing a model that is capable of simulating molten metal droplets and the production of metal powder during the Gas Atomization (GA) method. The essential goal of this research aims to gather more information on simulating the process of creating metal powder. The model structure and perspective was built through the application of governing equations and aspects that utilized factors such as gas dynamics, droplet dynamics, energy balance, heat transfer, fluid mechanics and thermodynamics that were proposed from previous studies. The model is very simple and can be broken down into having a set of inputs to produce outputs. The inputs are the processing parameters such as the initial temperature of the metal alloy, the gas pressure and the size of the droplets. Additional inputs include the selection of the metal alloy and the atomization gas and factoring in their properties. The outputs can be designated by the velocity and thermal profiles of the droplet and gas. These profiles illustrate the speed of both as well as the rate of temperature change or cooling rate of the droplets. Here, the main focus is the temperature change and finding the right parameters to ensure that the metal powder is efficiently produced. Once the model was conceptualized and finalized, it was employed to verify the results of other previous studies.

36 MATERIALS SCIENCE↗

DOE OSTI2016

iMatTOUGH: An open-source Matlab-based graphical user interface for pre- and post-processing of TOUGH2 and iTOUGH2 models

TOUGH2 and iTOUGH2 are powerful models that simulate the heat and fluid flows in porous and fracture media, and perform parameter estimation, sensitivity analysis and uncertainty propagation analysis. However, setting up the input files is not only tedious, but error prone, and processing output files is time consuming. Here, we present an open source Matlab-based tool (iMatTOUGH) that supports the generation of all necessary inputs for both TOUGH2 and iTOUGH2 and visualize their outputs. The tool links the inputs of TOUGH2 and iTOUGH2, making sure the two input files are consistent. It supports the generation of rectangular computational mesh, i.e., it automatically generates the elements and connections as well as their properties as required by TOUGH2. The tool also allows the specification of initial and time-dependent boundary conditions for better subsurface heat and water flow simulations. The effectiveness of the tool is illustrated by an example that uses TOUGH2 and iTOUGH2 to estimate soil hydrological and thermal properties from soil temperature data and simulate the heat and water flows at the Rifle site in Colorado.

58 GEOSCIENCES↗

DOE OSTI2018

Failure stress comparison of different pairings of Ag-plating and reflow-oven-processed pressureless-sintered-Ag interconnects

Here, sintered-silver is a candidate material to supplant solders for interconnects in power electronic packaging for many reasons including its desirably high electrical and thermal conductivities and compliance to Restriction of Hazardous Substances (RoHS) guidelines. The shear failure stress of interconnects though is limited by whatever constituent is the weakest, and that includes any employed plating. In the present study, silver-platings were processed electrolytically, electrolessly, and through sputter deposition and their influence on the entire “interconnect system” shear strength was examined. Test sets employing gold plating and no plating were included for comparison and to aid interpretation. Ambient-air, reflow-oven-processed, pressureless-sintered-silver interconnects were identically fabricated with all plating test sets. It was found that all considered silver-plating methods produced consistently strong (characteristic failure stresses > 55 MPa) sintered-silver interconnects. Because failures tended to be adhesive in all six sets, differences in failure stress among the silver-plated sets and the gold-plated or unplated sets were likely due to differences in the adhesive strength of the sintered silver with silver-plating, gold-plating, or direct bonding with copper.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition) on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The R -process Alliance: A Nearly Complete R -process Abundance Template Derived from Ultraviolet Spectroscopy of the R -process-enhanced Metal-poor Star HD 222925 *

We present a nearly complete rapid neutron-capture process (r-process) chemical inventory of the metal-poor ([Fe/H] = -1.46 ± 0.10) r-process-enhanced ([Eu/Fe] = +1.32 ± 0.08) halo star HD 222925. This abundance set is the most complete for any object beyond the solar system, with a total of 63 metals detected and seven with upper limits. It comprises 42 elements from 31 ≤ Z ≤ 90, including elements rarely detected in r-process-enhanced stars, such as Ga, Ge, As, Se, Cd, In, Sn, Sb, Te, W, Re, Os, Ir, Pt, and Au. We derive these abundances from an analysis of 404 absorption lines in ultraviolet spectra collected using the Space Telescope Imaging Spectrograph on the Hubble Space Telescope and previously analyzed optical spectra. A series of appendices discusses the atomic data and quality of fits for these lines. The r-process elements from Ba to Pb, including all elements at the third r-process peak, exhibit remarkable agreement with the solar r-process residuals, with a standard deviation of the differences of only 0.08 dex (17%). In contrast, deviations among the lighter elements from Ga to Te span nearly 1.4 dex, and they show distinct trends from Ga to Se, Nb through Cd, and In through Te. The r-process contribution to Ga, Ge, and As is small, and Se is the lightest element whose production is dominated by the r-process. The lanthanide fraction, log X La = -1.39 ± 0.09, is typical for r-process-enhanced stars and higher than that of the kilonova from the GW170817 neutron-star merger event. We advocate adopting this pattern as an alternative to the solar r-process-element residuals when confronting future theoretical models of heavy-element nucleosynthesis with observations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Reconstructing probability distributions with Gaussian processes

Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete. Here we present a method for reconstructing the log-probability distributions of completed experiments from an existing chain (or any set of posterior samples). Here, the reconstruction is performed using Gaussian process regression for interpolating the log-probability. This allows for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations. As an example use case, we reconstruct the posterior distribution of the most recent Planck 2018 analysis. We then resample the posterior, and generate a new chain with 40 times as many points in only 30 min. Our likelihood reconstruction tool is made publicly available online.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Badge Office Process Analysis

The purpose of this process analysis was to analyze the Badge Offices’ current processes from a systems perspective and consider ways of pursuing objectives set forth by SEC-PS, namely increased customer flow (throughput) and reduced customer wait times. Information for the analysis was gathered for the project primarily through Badge Office Subject Matter Experts (SMEs), and in-person observation of prevailing processes. Using the information gathered, a process simulation model was constructed to represent current operations and allow assessment of potential process changes relative to factors mentioned previously. The overall purpose of the analysis was to provide SEC-PS management with information and recommendations to serve as a basis for additional focused study and areas for potential process improvements in the future.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Closed-Form Solutions for the Equations of Motion of the Heavy Symmetrical Top with One Point Fixed

The equations of motion (EOM) for the heavy symmetrical top with one point fixed are highly nonlinear. The literature describes the numerical methods that are used to resolve this classical system, including modern tools, such as the Runge–Kutta fourth–order method. Finding the derivate of closed-form solutions for the EOM is more difficult and, as mentioned in the literature, discovering the solution is not always possible for all the EOM. Fortunately, a few examples are available that serve as a guide to move further in this topic. The purpose of this paper is to find a methodology that will produce the solutions for a given subset of EOMs that fulfill certain requisites. This paper summarizes the literature available on this topic and then follows with the derivation of the EOM using the Euler–Lagrange method. The Routhian method will be used to reduce the size of the expression, and it continues with the formulation of the classical cubic function, ƒ(u), through a novel process. The roots of ƒ(u) are of the utmost importance in finding the EOM closed-form solution, and once the final roots are selected, the general method that will produce the closed-form solutions is presented. Two sets of examples are included to show the validity of the process, and comparisons of the results from the closed-form solutions vs. the numerical results for these examples are shown.

Laos, Hector↗

DOE OSTI2017

A New MHD-assisted Stokes Inversion Technique

We present a new method of Stokes inversion of spectropolarimetric data and evaluate it by taking the example of a Sunrise/IMaX observation. An archive of synthetic Stokes profiles is obtained by the spectral synthesis of state-of-the-art magnetohydrodynamics (MHD) simulations and a realistic degradation to the level of the observed data. The definition of a merit function allows the archive to be searched for the synthetic Stokes profiles that best match the observed profiles. In contrast to traditional Stokes inversion codes, which solve the Unno–Rachkovsky equations for the polarized radiative transfer numerically and fit the Stokes profiles iteratively, the new technique provides the full set of atmospheric parameters. This gives us the ability to start an MHD simulation that takes the inversion result as an initial condition. After a relaxation process of half an hour solar time we obtain physically consistent MHD data sets with a target similar to the observation. The new MHD simulation is used to repeat the method in a second iteration, which further improves the match between observation and simulation, resulting in a factor of 2.2 lower mean χ{sup 2} value. One advantage of the new technique is that it provides the physical parameters on a geometrical height scale. It constitutes a first step toward inversions that give results consistent with the MHD equations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Sustainability metrics for assessing water resource recovery facilities of the future

Abstract The recovery of water, energy, and nutrients from water resource recovery facilities ( WRRF s) is needed to address significant global challenges, such as increasing water demand and decreasing availability of nonrenewable resources. To meet these challenges, innovative technological developments must lead to increased adoption of water and resource recovery processes, while addressing stakeholder needs (e.g., innovators, practitioners, regulators). A test bed network of over 90 partner facilities within the United States and abroad will help accelerate innovation and widespread adoption of novel processes through multiscale testing and demonstration of technologies. In this paper, we define a common set of environmental, economic, technical, and social performance metrics for innovative technologies, that will meet the needs of multiple stakeholders in the decision‐making process. These triple bottom line performance metrics can be used to track the sustainability of technologies in a consistent and transparent manner, while aiding the decision‐making process for WRRF s. Practitioner points The Facilities Accelerating Science and Technology (FAST) Water Network includes over 90 test bed facilities dedicated to accelerating innovation and adoption of water energy, and nutrient recovery systems. A common set of environmental, economic, technical, and social performance metrics should be measured and reported when a new technology is evaluated in the FAST Water Network. Performance metrics can aid sustainable decision‐making at WRRF, while meeting the needs of multiple stakeholders.

Cornejo, Pablo K.↗

DOE OSTI2017

Identification of Low Coronal Sources of “Stealth” Coronal Mass Ejections Using New Image Processing Techniques

Coronal mass ejections (CMEs) are generally associated with low coronal signatures (LCSs), such as flares, filament eruptions, extreme ultraviolet (EUV) waves, or jets. A number of recent studies have reported the existence of stealth CMEs as events without LCSs, possibly due to observational limitations. Our study focuses on a set of 40 stealth CMEs identified from a study by D’Huys et al. New image processing techniques are applied to high-cadence, multi-instrument sets of images spanning the onset and propagation time of each of these CMEs to search for possible LCSs. Twenty-three of these events are identified as small, low-mass, unstructured blobs or puffs, often occurring in the aftermath of a large CME, but associated with LCSs such as small flares, jets, or filament eruptions. Of the larger CMEs, seven are associated with jets and eight with filament eruptions. Several of these filament eruptions are different from the standard model of an erupting filament/flux tube in that they are eruptions of large, faint flux tubes that seem to exist at large heights for a long time prior to their slow eruption. For two of these events, we see an eruption in Large Angle Spectrometric Coronagraph C2 images and the consequent changes at the bottom edge of the eruption in EUV images. All 40 events in our study are associated with some form of LCS. We conclude that stealth CMEs arise from observational and processing limitations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Using Best Basis Inventory Data to Direct Strategies for Real Time Monitoring of Hanford High Level Waste – 26226

The potential to accelerate the processing of low- and high-level tank waste by applying real-time monitoring (RTM) of chemical and physical properties has prompted research into the suitability of multiple analytical methods for that purpose. The broad variety of waste stream properties and the large number of analytes of interest (as evidenced by Waste Acceptance Criteria (WAC) and Process Control Limit (PCL) lists) lead to an overwhelming set of possible analytical scenarios. This report describes the use of Best Basis Inventory (BBI) data to find the most relevant analytical targets for the specific case of monitoring the blending of High Level Waste from multiple tanks prior to introduction into a vitrification facility. Campaigns for blending this waste to minimize the risk of exceeding WACs and PCLs have been proposed. However, the predicted compositions of the blended materials do not incorporate any uncertainties that may be associated with the representativeness of the waste layer samples or the laboratory analyses that generated the BBI data. Also, they do not include any uncertainty associated with the precision of collecting highly specific fractions of the layers during a blending campaign or any inhomogeneities that may exist in those layers. Monte Carlo methods are used to apply uncertainties to the compositions of the individual layers specified in the campaign recipes. The resulting variations in the compositions of the blended materials allow estimation of the risks of exceeding WACs and PCLs for each campaign. A critical subset of WACs/PCLs – NOx, NaK, AlFeZr, and S – are especially at risk of being exceeded in multiple campaigns. These analytes should be the focus of instrument development. We also have extracted the expected solid/supernate distribution for these analytes, which establishes important performance criteria for individual analytical methods. The BBI data also permits an understanding of the different chemical forms in which the analytes appear. Thus, the need to establish instrumental sensitivity to these forms can be gainfully addressed. Although concentrating on one specific application – the blending of tank waste - this approach should be generalizable for the analysis of other possible RTM applications for waste processing.

Lascola, Robert [Savannah River National Laborator↗