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DOE OSTI2023

Operation and Process Control Development for a Pilot-Scale Leaching and Solvent Extraction Circuit Recovering Rare Earth Elements From Coal-Based Sources

The US Department of Energy in 2010 has identified several rare earth elements as critical materials to enable clean technologies. As part of ongoing research in REEs (rare earth elements) recovery from coal sources, the University of Kentucky has designed, developed and is demonstrating a ¼ ton/hour pilot-scale processing plant to produce high-grade REEs from coal sources. Due to the need to control critical variables (e.g. pH, tank level, etc.), process control is required. To ensure adequate process control, a study was conducted on leaching and solvent extraction control to evaluate the potential of achieving low-cost REE recovery in addition to developing a process control PLC system. The overall operational design and utilization of Six Sigma methodologies is discussed. Further, the application of the controls design, both procedural and electronic for the control of process variables such as pH is discussed. Variations in output parameters were quantified as a function of time. Data trends show that the mean process variable was maintained within prescribed limits. Future work for the utilization of data analysis and integration for data-based decision-making will be discussed.

coal, rare earth elements, pilot plant, automation↗

DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗

DOE OSTI2025

Pointing stabilization of a 1 Hz high-power laser via machine learning

Abstract High-power lasers are vital for particle acceleration, imaging, fusion and materials processing, requiring precise control and high-energy delivery. Laser plasma accelerators (LPAs) demand laser positional stability at focus to ensure consistent electron beams in applications such as X-ray free-electron lasers and high-energy colliders. Achieving this stability is especially challenging for the low-repetition-rate lasers in current LPAs. We present a machine learning method that predicts and corrects laser pointing instabilities in real-time using a high-frequency pilot beam. By preemptively adjusting a correction mirror, this approach overcomes traditional feedback limits. Demonstrated on the BELLA petawatt laser operating at the terawatt level (30 mJ amplification), our method achieved root mean square pointing stabilization of 0.34 and 0.59 $\unicode{x3bc} \mathrm{rad}$ in the x and y directions, reducing jitter by 65% and 47%, respectively. This is the first successful application of predictive control for shot-to-shot stabilization in low-repetition-rate laser systems, paving the way for full-energy petawatt lasers and transformative advances across science, industry and security.

Amodio, Alessio↗

DOE OSTI2022

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Request for Information on Establishing a New Manufacturing Institute (DE-FOA-0002564)

Deep decarbonization of major industries such as metals manufacturing requires extensive process integration and controls to manage feedstocks, side reactions, heat, water, and waste streams. The scale of the energy and capital investment requires that process integration be validated to a high level of confidence with no bias. Industries such as steel production have very thin profit margins. Therefore, lack of confidence in process integration, product quality, and economics is a major deterrence to changes in manufacturing capital investment. Public sector investment in reconfigurable pilot testbeds and a first-of-a-kind plant would be necessary to de-risk technical and financial barriers prior to industry-wide buildout. DOE should consider a National Lab-led hub for the testbeds. Lab testbeds could be utilized in campaigns and allow multiple industrial partners to evaluate and validate technologies prior to making major capital investments.

08 HYDROGEN↗

DOE OSTI2025

Distribution of nickel(II) ions adsorbed at the muscovite mica (001)-water interface determined by in-situ resonant anomalous X-ray reflectivity

Mineral-water interfaces mediate adsorption, ion exchange, and secondary mineral formation that control element mobility in natural and engineered systems. Reliable prediction and control of these processes require a fundamental understanding of the interfacial structure that links adsorbed ion speciation to macroscopic sorption capacity and strength. Here, we determine atomic-scale changes in hydration and distribution of Ni(II) at the muscovite mica (001)-water interface using in situ high-resolution X-ray reflectivity (XR) and resonant anomalous X-ray reflectivity (RAXR) at 1 mM NiCl2 and pH 5.7. XR reveals reorganization of the primary hydration structure relative to that in deionized water: the water layer adsorbed in the cavity sites at a height of ~1.3 Å disappears, while distinct solution layers emerge at ~2.3, ~4.1, and ~5.6 Å above the basal oxygen plane. RAXR resolves three interfacial Ni(II) species: a dominant outer-sphere complex at 3.65 Å (~80% of the total coverage), a minor inner-sphere complex at 0.75 Å, and a low-coverage, more distant outer-sphere species at 5.63 Å. These three adsorbed Ni(II) species account for a total Ni(II) coverage of 0.54 ± 0.02 ion per unit cell area that compensates for the surface charge. These results highlight the role of interfacial hydration in controlling the speciation and stability of adsorbate cations on the negatively charged mica surface, providing quantitative insight into predicting the geochemical behavior of divalent metal cations in the aqueous environments.

Lee, Sang Soo↗

DOE OSTI2022

Assessing entropy for catalytic processes at complex reactive interfaces

When chemical reactions are accelerated by a catalyst, entropy differences between reactants and their transient intermediates can be the driving force behind the promotion or inhibition of desired and parasitic chemical pathways. Understanding and controlling catalytic processes therefore requires both a fundamental and practicable understanding of entropy in addition to enthalpy. In unstructured media such as the vapor phase equilibrated with sparsely covered surfaces, entropy can be adequately accounted for by well-established approaches based on translational, rotational, and harmonic vibrational partition functions. However, these approximations become inadequate in more complex condensed phase environments, e.g., solid liquid interfaces of confined reaction spaces. In this chapter, we provide an overview of the state-of-art in the computational quantification of entropy and its known ramifications on catalysis. The fundamental roles of thermodynamics and kinetics in catalysis are covered in enough detail to appreciate and contextualize the computational methods employed to compute chemically accurate estimates of entropy. These methods are discussed in appropriate detail and range from the ubiquitous harmonic oscillator approximation where entropy unrelated to high frequency oscillations is typically underestimated, to enhanced free energy sampling with molecular dynamics where the desired accuracy must be weighed against the associated computational cost of obtaining it. The rising importance of machine learning and artificial intelligence in accelerating methodological progress in this field is touched upon, as well. Finally, applications, successes, and pitfalls of using these methods are provided to showcase past and present accomplishments while clarifying where improvements in both understanding and methodology are still needed.

Kollias, Loukas↗

DOE OSTI2022

High temperature thick film sensor development based on doped lanthanum chromites refractory semiconductors materials

High temperature advanced sensing materials have generated high demand due the high accuracy temperature measurements requirements for process optimization, controlling and sensing. Some technological applications of harsh conditions sensing include monitoring tiles of space shuttles, rotating bearings in aircraft engines, turbines, jet engines dynamics and chemical reactors. High temperature conditions limit the sensing strategies, where typically traditional metal thermocouples are unstable, and the sensing options are limited to optical spectroscopy methods. Recently, refractory semiconductors thick- and thin-film thermocouples have been developed and, in many cases, preferred over conventional metallic thermocouples due their spatial resolution, and capability of direct deposition on any surface. Rare earth chromites ceramics materials, exhibit some properties of interest for high temperature sensing technologies development, such as: high microstructure and sintering stability, excellent conductive behavior at high temperatures, and matching thermal expansion coefficients relative to other conductors and refractory ceramics. In this work, high performance ultra-high temperature thermocouples using p-type and n-type doped lanthanum chromites materials were fabricated and tested at temperatures up to 1500 o C. Thermoelectric voltage, Seebeck Coefficients were established for all devices, evidencing high stability and performance in prolongated operational time and harsh conditions.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

2023 FORCE Development Status Update

Technical and economic analysis of integrated energy systems (IES) using software models is a complex process requiring multiple commodity market decision analysis, optimal control, process modeling, and stochastic analysis. Many assumptions used in traditional energy analysis tools do not hold in future energy markets with significant storage and variable renewable energy sources (VRE), let alone with multiple commodity markets. Capturing these intricate elements for accurate techno-economic analysis of IES led to the development of the Framework for Optimization of ResourCes and Economics (FORCE) tool suite under the U.S. Department of Energy’s Integrated Energy Systems crosscutting technology program. With the aim of a full framework release in 2025, many improvements to the FORCE tool suite were developed in fiscal year 2023 (FY23). These improvements broadly fit into three focuses for development of FORCE: capability, accessibility, and reliability. Capability refers to the ability of FORCE to accurately model the technical and economic viability of various IES. Accessibility refers to ease-of-use for new and existing IES analysts to efficiently set up, analyze, and produce results using FORCE. Reliability refers to the consistency of the software, allowing consistency to analysis regardless of erstwhile changes to the software. In addition to many smaller changes, there are three major capability improvements in FORCE in FY23. In summary, FORCE developments in FY23 have moved us close to all the capability requirements for FORCE 1.0 to be delivered in FY25. Inclusion of Bayesian optimization, resilience metrics, and levelized cost analysis expand the capability, accessibility, and reliability of FORCE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Federal Facility Agreement and Consent Order: Nevada National Security Site Use Restriction Management Plan with ROTC 1

This Use Restriction Management Plan (URMP) provides the information needed to create, modify, and manage use restrictions (URs) for sites on the Nevada National Security Site (NNSS), and sites accessed through the NNSS main gate, that were closed using the corrective action alternative (CAA) of closure in place under the Federal Facility Agreement and Consent Order (FFACO) (1996, as amended). (Note: This pertains to those FFACO sites not managed by the U.S. Department of Energy [DOE], Legacy Management.) The closure in place alternative is used for sites closed with residual contamination at levels requiring corrective action as determined using the FFACO process. The URs contain and control all requirements for long-term monitoring. This URMP also serves as the single repository of the URs implemented under the FFACO that identify use restricted areas and contain the current requirements for inspections, maintenance, and monitoring of the UR. The requirements in these URs replace all requirements listed in previous documentation. This consolidates post-closure monitoring requirements into a single source that ensures completeness and consistency of UR requirements and information. Standardized UR forms were developed to clearly document post-closure requirements that are consistent with current protocols and to ensure consistent information is contained in the URs. Standard notification, summary, and site controls statements were developed for all URs with provisions to insert site-specific options in the text. Current protocols for Industrial Sites and Soils URs are defined in this document and in the Soils Risk-Based Corrective Action (RBCA) Evaluation Process (DOE/EMNV, 2018). The standardized UR forms that have been approved to date are listed in Appendix A. Additional UR forms will be added once the review and approval process has been completed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Electrically Controlled All-Antiferromagnetic Tunnel Junctions on Silicon with Large Room-Temperature Magnetoresistance

Antiferromagnetic (AFM) materials are a pathway to spintronic memory and computing devices with unprecedented speed, energy efficiency, and bit density. Realizing this potential requires AFM devices with simultaneous electrical writing and reading of information, which are also compatible with established silicon-based manufacturing. Recent experiments have shown tunneling magnetoresistance (TMR) readout in epitaxial AFM tunnel junctions. However, these TMR structures are not grown using a silicon-compatible deposition process, and controlling their AFM order required external magnetic fields. Here are shown three-terminal AFM tunnel junctions based on the noncollinear antiferromagnet PtMn 3 , sputter-deposited on silicon. The devices simultaneously exhibit electrical switching using electric currents, and electrical readout by a large room-temperature TMR effect. First-principles calculations explain the TMR in terms of the momentum-resolved spin-dependent tunneling conduction in tunnel junctions with noncollinear AFM electrodes.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This presentation provides an overview of a recently initiated project "Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities" which will (1) develop and demonstrate data-driven process controls at full-scale facilities for five promising WRRF Applications (i.e., process technologies) that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) create a toolbox of new process control approaches and an implementation guide including five examples for application at utilities. The presentation also provides a detailed overview of the research approach and progress being made on one of the five Applications, namely Application 2: Biological Nutrient Removal (BNR): ammonium-based aeration control (ABAC) / ammonia vs. NOx (AvN) + partial denitration with anammox (PdNA), which is being implemented at Hampton Roads Sanitation District. This project is a collaboration of work being conducted by DC Water, Hampton Roads Sanitation District, Metro Water Recovery, University of Michigan, Northwestern University, US Military Academy - West Point, Black & Veatch, and Oak Ridge National Laboratory. Research partner: U.S. Department of Energy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Process Control Plan to Monitor Acceptable Levels of Flux and other residues

IPC J-STD –001G, Amendment 1 requires that the assembler have a sampling plan to assure that the process remains in control once qualified and validated. It is well documented that electrochemical failures occur at component sites where flux residues are not fully activated. It is also well known that specific components have a higher risk of electrochemical failure. Selecting a control plan that monitors the process and its performance on challenging components provides assurance that the process maintains control once established during process qualification. The test board used to qualify the process establishes an upper and lower spec limit for each of the component types on the test board. Surface Insulation Resistance upper and lower control limits, using challenging components that are representative of production hardware, represent a golden process condition. The golden image is a measure of process deviations that represents the performance of a resistance curve within a specific period. The objective is to judge whether the process in or out of control.

42 ENGINEERING↗

DOE OSTI2023

Experimental Performance of a Nonlinear Control Strategy to Regulate Temperature of a High-Temperature Solar Reactor

Abstract Despite the significant potential of solar thermochemical process technology for storing solar energy as solid-state solar fuel, several challenges have made its industrial application difficult. It is important to note that solar energy has a transient nature that causes instability and reduces process efficiency. Therefore, it is crucial to implement a robust control system to regulate the process temperature and tackle the shortage of incoming solar energy during cloudy weather. In our previous works, different model-based control strategies were developed namely a proportional integral derivative controller (PID) with gain scheduling and adaptive model predictive control (MPC). These methods were tested numerically to regulate the temperature inside a high-temperature tubular solar reactor. In this work, the proposed control strategies were experimentally tested under various operation conditions. The controllers were challenged to track different setpoints (500 °C, 1000 °C, and 1450 °C) with different amounts of gas/particle flowrates. Additionally, the flow controller was tested to regulate the reactor temperature under a cloudy weather scenario. The ultimate goal was to produce 5 kg of reduced solar fuel magnesium manganese oxide (MgMn2O4) successfully, and the controllers were able to track the required process temperature and reject disturbances despite the system's strong nonlinearity. The experimental results showed a maximum error in the temperature setpoint of less than 0.5% (6 °C), and the MPC controller demonstrated superior performance in reducing the control effort and rejecting disturbances.

Energy & Fuels↗

DOE OSTI2025

Rapid monitoring of fermentations: a feasibility study on biological 2,3-butanediol production

2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be produced by the fermentation of sugars using an engineered strain of Zymomonas mobilis . These fermentations require continuous monitoring and modification of fermentation conditions to maximize 2,3-BDO yields and minimize the production of the undesired coproducts glycerol and acetoin. Because of the time required for sampling and off-line chromatographic measurement of fermentation samples, the ability of fermentation scientists to modify fermentation conditions in a timely manner is limited. The goal of this study was to test if near-infrared spectroscopy (NIRS) along with multivariate statistics could reduce the time needed for this analysis and enable real-time monitoring and control of the fermentation. In this work we developed partial least squares (PLS) calibration models to predict the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol in fermentations via NIRS using two different spectrometers and two different spectroscopy modalities. We first evaluated the feasibility of rapid NIRS monitoring through experiments where we measured the signals from each analyte of interest and built NIRS-based PLS models using spectra from synthetic samples containing uncorrelated concentrations of these analytes. All analytes showed unique spectral signatures, and this initial modeling showed that all analytes could be detected simultaneously. We then began work with samples from laboratory fermentation experiments and tested the feasibility of regression model development across two spectral collection modalities (at-line and on-line) and two instruments: a laboratory-grade instrument and a low-cost instrument with a more limited spectral range. All modalities showed promise in the ability to monitor Z. mobilis fermentations of glucose and xylose to 2,3-BDO. The low-cost instrument displayed a lower signal-to-noise ratio than the laboratory-grade instrument, which led to comparatively lower performance overall, but still provided sufficient accuracy to monitor fermentation trends. While the ease of use of on-line monitoring systems was favored as compared to at-line systems due to the lack of sampling required and potential for automated process control, we observed some decrease in performance due to the additional complexity of the sample matrix. We have demonstrated that NIRS combined with multivariate analysis can be used for at-line and on-line monitoring of the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol during Z. mobilis fermentations. The decrease in signal-to-noise ratio when using a low-cost spectrometer led to greater prediction error than the laboratory-grade spectrometer for at-line monitoring. The on-line monitoring modality showed great promise for real time process control via NIRS.

09 BIOMASS FUELS↗

DOE OSTI2023

C1Po2D-04: Cryogenic Testing of the First HL-LHC Q1/Q3 Cryo-Assembly at

In early 2023, Fermilab is conducting horizontal cryogenic testing of the first Q1/Q3 Cryo-Assembly for the high-luminosity LHC upgrade (HL-LHC). The Cryo-Assembly was installed on the upgraded Fermilab horizontal test stand previously used for testing the LHC inner triplet quadrupoles. The cryogenic process requirements of this test include controlled cool-down and warm-up with a 100 K maximum temperature differential between the two ends of the cold mass, operation of a 1.3 bar, 1.9 K bath of subcooled superfluid helium during power testing and magnetic measurements, and operation at pressure up to 18 bar with full helium recovery after a quench.

Rabehl, R.↗