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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Harvesting Energy from Wastewater by Converting Sewage

This project aims were to develop and demonstrate a scalable, integrated process to convert sewage sludge into renewable natural gas (RNG), enabling wastewater treatment plants (WWTPs) to become net energy producers. The system proposal integrates autothermal hydrothermal liquefaction (AT-HTL), supercritical salt precipitation (SCSP), and hydrothermal gasification (HTG), collectively forming the Supercritical Sludge-to-Gas (SC-S2G) platform. Initially, batch hydrothermal liquefaction reactions were used to screen sewage sludge using AT-HTL (later termed RI-HTL) conversion to biocrude, aqueous and char phases compared to hydrothermal liquefaction (HTL). Significant improvement in biocrude yield using peroxide addition at O:C ratio of 0.05 and under conditions of 300°C for 10 minutes gave 57% biocrude yield and 85% fluid carbon yield (biocrude plus aqueous), while minimizing the loss of carbon to char solids (~7%). Hence, RI-HTL was shown to be effective for conversion of real sewage sludge. The corrosion of the alloy reactor tubes or vessels is an important factor when developing a process that includes an oxidant and a chemically complex feed like sewage sludge. We investigated the corrosion rates on metal alloys at 350°C for 240 hours. Corrosion rates of 0.21 and 0.26 mpy for 304L and 316L stainless steel were measured respectively. The corrosion information obtained in this investigation was utilized by PNNL for design, materials sourcing and construction of the pilot scale continuous flow system.

09 BIOMASS FUELS↗
DOE OSTI2026

Fluorescence Signatures of Rare Earth Metals during Precipitation in Various Conditions

Fluorescence spectroscopy is a widely used sensor methodology that analyzes light emitted from a compound or element as it decays from an excited state. This technique is very sensitive and selective, which is ideal to characterize analytes at lower limits of detection. Key example targets of significant industry and research interest include rare earth elements (REEs) such as dysprosium (Dy) and europium (Eu). These are widely used in advanced technologies including semiconductors, electric vehicle motors, lasers, and permanent magnets. Identifying new sources and responsible reutilization of REEs is essential, and new approaches to extract and recycle REEs could be notably enhanced through the integration of on-line sensors. The sensors can support faster process design, informed scale-up, and cost-effective deployment. This study covers the initial exploration of applying fluorescence-based on-line monitoring to REEs within a precipitation process. This study demonstrates the successful scale-up of a fluorescence -based sensing approach, from stationary cuvettes and small-volume microfluidic devices to continuous flow systems operating at the bench scale (10-25mL). This work also provides initial insight into the challenges of signal’s effects and utility within a turbid environment. Using a modular design for monitoring flowing solutions in a flow tube, fluorescence can be characterized for a variety of analytical targets. In this study, detection performance parameters between the cuvette and flow tube system were compared. Additionally, the response of Dy during precipitation by sodium bicarbonate in the two measurement designs was explored. This letter represents a starting point to bridge the gap between traditional fluorescence sensor measurements in a cuvette to future developments that explore the ability to integrate fluorescence sensors into extraction and separation processes at industrially relevant scales.

fluorescence↗
DOE OSTI2026

PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow With Continual Learning

Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

TTDAQ: A Continuous Flow, Timing and Trigger DAQ System

Final Scientific/Technical Report for DOE Award DE-SC0019581, “TTDAQ: A Continuous Flow, Timing and Trigger DAQ System.” The report summarizes Telluric Labs’ Phase II STTR work developing silicon-photonic building blocks for a software-defined, continuous-flow, trigger-less data acquisition system for next-generation high-energy and nuclear-physics detectors. The project focused on radiation-hard photonic integrated circuits, remote optical illumination, dense wavelength-division multiplexing, and a differential microring-resonator transceiver architecture designed to improve high-speed optical link stability and bandwidth. The report describes project objectives, technical accomplishments, AIM Photonics tape-outs, bench characterization, radiation-hardness testing, deferred integration work, and potential applications beyond physics readout.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
DOE OSTI2024

Site-specific Design Case Study for Wet Waste Hydrothermal Liquefaction and Biocrude Upgrading to Hydrocarbon Fuels

Hydrothermal liquefaction (HTL) is a thermal process that converts wet biomass to renewable hydrocarbon fuel blendstocks (i.e., renewable naphtha, renewable diesel, and sustainable aviation fuel (SAF)). It can utilize a wide range of pure and blended wet feedstocks, including sewage sludge from water resource recovery facilities (WRRF), food and agriculture wastes, algae, fats, oils and greases (FOG) and blends of dry and wet wastes/feedstocks. Historically, techno-economic analysis (TEA) and annual state of technology (SOT) assessments with standard economic assumptions used by the Bioenergy Technologies Office (BETO) were conducted for the wet waste HTL pathway leveraging experimental data collected from Pacific Northwest National Laboratory’s (PNNL) continuous flow reactor systems. The objective of the SOT assessment has been to guide and track progress of BETO’s HTL research and development (R&D) toward reduced cost and greenhouse gas (GHG) emissions for the pathway. However, gaps exist between BETO’s traditional SOT updates and the needs of key external stakeholders that – if addressed – will accelerate technology adoption. This Business Case Study aims to bridge this gap by providing an updated design, TEA, and LCA based on PNNL’s FY23 R&D with added analyses and information that provide enhanced relevance for stakeholders of the HTL technology. This includes specific siting, regional wet waste resource inventory and transportation cost analyses, fuel market information, sustainable fuel policy impacts, economic metrics of net present value (NPV) and internal rate of return (IRR), greenhouse gas (GHG) emissions analysis, and statistical analysis of cost and technical uncertainties of the HTL plant design. The study focuses on the “Detroit combined statistical area (CSA)” region for siting of a wet waste HTL plant adjacent to the Great Lakes Water Authority (GLWA) facility with guidance from industry participants. Regional resource and siting analyses were conducted to identify feedstock availability, scale, and cost, as well as a beneficial site location. TEA with detailed rigorous capital cost estimation for the specific site application was conducted to evaluate the key economic metrics of most value to industrial partners. These include total capital investment, operating costs, minimum fuel selling price (MFSP) of the biocrude and fuel blendstock, and NPV and internal rate of return IRR with sustainable fuel credits. Life cycle analysis was conducted to evaluate the supply chain greenhouse gas (GHG) emissions for the wet waste HTL process as compared with petroleum derived diesel. This study is also informed by years of R&D and process de-risking learnings and was conducted with a basic engineering HTL plant design and costing that akin to a “first-of-a-kind” plant economics. This differs from our conventional “nth plant ” SOT assessments. Specifically, the HTL process model has been updated with more operationally reliable methods for feed heating and phase separations. Further, we have implemented additional spare equipment for redundancy, a more rigorous installed equipment cost estimation approach, and additional costs associated with feed formatting and delivery, building, piping and site development. An Excel-based cost sheet based on the basic engineering design is also released alongside the report that allows users to conduct customized TEA with their own feed composition and financial assumptions.

09 BIOMASS FUELS↗
DOE OSTI2024

Immobilization of Urease for continuous flow conversion of waste urea

An efficient and robust system for the urease catalyzed conversion of urea to ammonia has been developed using urease immobilized on modified agarose beads. Two different immobilization strategies, adsorption and covalent binding were studies using six different types of modified agarose beads. The immobilization of urease on each of the beads was studied at different concentrations and times using immobilization efficiency as a measure of success. The data from these experiments was used to identify potential candidates for immobilization scale up and implementation into the continuous flow system. The enzyme was then immobilized on the candidates in a packed bed reactor and the optimal flow rate and storage stability was determined. Future work will utilize the data obtained from these experiments to expand to other resins and enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Porous Iron Electrodes Reduce Energy Consumption During Electrocoagulation of a Virus Surrogate: Insights into Performance Enhancements Using Three-Dimensional Neutron Computed Tomography

Electrocoagulation has attracted significant attention as an alternative to conventional chemical coagulation because it is capable of removing a wide range of contaminants and has several potential advantages. In contrast to most electrocoagulation research that has been performed with nonporous electrodes, in this study, we demonstrate energy-efficient iron electrocoagulation using porous electrodes. In batch operation, investigation of the external pore structures through optical microscopy suggested that a low porosity electrode with sparse connection between pores may lead to mechanical failure of the pore network during electrolysis, whereas a high porosity electrode is vulnerable to pore clogging. Electrodes with intermediate porosity, instead, only suffered a moderate surface deposition, leading to electrical energy savings of 21% and 36% in terms of electrocoagulant delivery and unit log virus reduction, respectively. Neutron computed tomography revealed the critical role of electrode porosity in utilizing the electrode’s internal surface for electrodissolution and effective delivery of electrocoagulant to the bulk. Energy savings of up to 88% in short-term operation were obtained with porous electrodes in a continuous flow-through system. Further investigation on the impact of current density and porosity in long-term operation is desired as well as the capital cost of porous electrodes.

42 ENGINEERING↗
DOE OSTI2026

Nondestructive Modular Leak Detection in 3D Printed 316L Stainless Steel Pipes via Laser Powder Bed Fusion

This research investigates the leak detection features of 316L Stainless Steel pipe structures manufactured via Laser Powder Bed Fusion (LPBF). This work involves the design of a modular sensor system integrating nondestructive evaluation (NDE) methods, including thermal imaging and ultrasonic frequency detection to detect and characterize leaks in components. This aims to improve leak detection sensitivity within medium-pressure gas systems, during continuous operation without halting flow or introducing safety risks. The system could be adaptable for use on unmanned aerial vehicles (UAVs), enabling remote leak detection in active environments. A custom pneumatic system incorporating temperature and pressure sensors was assembled to detect leaks in LPBF-printed 316L SS tee pipes. Experimental results and simulations confirm the system’s effectiveness in leak detection and material evaluation. This research program also integrated a Python-based image recognition platform based on a metallography and optical microscopy to assess the porosity and complement the leak detection data on the printed structures. This allows a detailed analysis of pore distribution and internal leak paths, which could compromise structural integrity, critical for quality control during manufacturing. Findings suggest that the investigated approach holds potential for enhancing leak detection technologies and adapt them for advanced manufactured parts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A flow-through gamma detection system for sub-surface radioxenon tracer measurements in LYNM PE1

A continuous flow-through, real-time radioxenon sensor array has been designed to monitor either 127 Xe or 133 Xe tracer gas as part of a series of underground chemical explosions in the Low-Yield Nuclear Monitoring (LYNM) Physics Experiment 1 (PE1). The sensors are pairs of shielded thallium-doped sodium iodide gamma-ray detectors that surround a gas measurement chamber. Each measurement chamber is connected to a sampling location in the geology surrounding the chemical explosion or in the access tunnel. The first experiment in the series, PE1 A, occurred in 2023 and the system quantified the 127 Xe tracer gas in the gas sampling lines for four weeks following the experiment execution. The array generated a set of radioxenon concentration time series with a 15-minute resolution that will be used to refine subsurface gas transport models.

Gamma-ray coincidence detection↗
DOE OSTI2025

Understanding the Dissolution and Passivation of an Aluminum Electrode during Electrocoagulation of Groundwater Using Neutron and X-ray Reflectometry

An aluminum (Al)-based electrocoagulation (EC) system can effectively remove dissolved silica and hardness in groundwater. The effectiveness of Al-EC in terms of pollutant removal, Faradaic efficiency, and energy consumption depends on the interfacial electrolysis or passivation of the electrode in water. Thus, understanding the electrolysis reaction at the liquid/electrode interface during operation is important for sustainable EC deployment. Here, a continuous flow-through Al-EC system was tested with various groundwater simulants, i.e., chloride (Cl – )-based, sulfate (SO 4 2– )-based, and mixed solutions. High pollutant removal with low energy consumption was observed in Cl – -based groundwater treatment, while low pollutant removal with high energy consumption was observed in SO 4 2– -based groundwater. For example, the required energy per unit mass of Al dosing in SO 4 2– -based groundwater is three times higher than that in Cl – -based groundwater at 10 mA/cm 2 . However, increasing the Cl – concentration significantly reduces this energy demand. In SO 4 2– -based groundwater, the silicate removal efficiency drops from 85.1% to 24.0% compared to that for Cl – -based groundwater, while Mg 2+ and Ca 2+ removal efficiencies decrease to 0.6% from 15.8% and 5.7% from 44.8%, respectively. To better understand this EC performance, we used in situ neutron reflectometry (NR) to examine the interfacial dynamics of Al dissolution and passivation at a 100 nm scale occurring on the surface of the sacrificial Al electrodes during EC. Ex situ X-ray reflectometry (XRR) was also used to support the in situ NR results. Both NR and XRR results revealed that Al dissolution is influenced by the presence of Cl – in the simulants, while a passivating layer forms on the electrode in a SO 4 2– -based solution. In the Cl – -based solution, anodic Al dissolution occurred locally and inhomogeneously across the surface of the Al anode film, resulting in a localized thickness reduction over time. In the SO 4 2– -based solution, no apparent dissolution of the Al anode was identified. Instead, Al underwent oxidation, forming an amorphous Al 2 O 3 surface layer within the Al electrode film that increased in thickness over time. In the mixed solution, both anodic Al dissolution and surface Al 2 O 3 layer formation occurred, indicating that Al dissolution and surface Al 2 O 3 layer formation are attributable to the Cl – and SO 4 2– ions, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Continuous Flow Photoelectrochemical Reactor with Gas Permeable Photocathode: Enhanced Photocurrent and Partial Current Density for CO 2 Reduction

Photoelectrochemical (PEC) CO 2 reduction using a photocathode is an attractive method for making valuable chemical products due to its simplicity and lower overpotential requirements. However, previous PEC processes have often been diffusion-limited leading to low production rates of the CO 2 reduction reaction, due to inefficient gas diffusion through the liquid electrolyte to the catalyst surface, particularly at high current densities. In this study, a gas-permeable photocathode in a continuous flow PEC reactor is incorporated, which facilitates the direct supply of CO 2 gas to the photocathode-electrolyte interface, unlike dark reaction-based flow reactors. This concept is demonstrated using Ag-TiO 2 on carbon paper, illuminated through a quartz window and flowing liquid electrolyte. CO 2 supply is managed via pressure and flow control on the non-illuminated side of the carbon paper. The photocurrent density is significantly influenced by the flow rates and pressure of CO 2 gas, and the electrolyte flow rates. Compared to the traditional H-cell, the continuous PEC flow reactor achieves ≈10-fold increase in CO faradaic efficiency, 30-fold increase in production rate and 16-fold increase in stability without catalyst modifications. This work provides essential insights into the design and application of continuous gas-liquid flow PEC reactor systems, highlighting their potential for other PEC reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A minor respiratory process with major global implications: is atmospheric methane oxidation in tree stems driven by stem respiration rather than microbial methanotrophy?

Tree stem surfaces are widely recognized as sites of carbon dioxide (CO₂) efflux and oxygen (O₂) influx, reflecting the dynamics of aerobic respiration of photosynthate substrates, such as sugars, delivered via the phloem. Stems are also largely considered passive conduits for methane (CH₄) produced in anoxic soils via microbial methanogenesis, where CH₄ is thought to be transported upward through the transpiration stream and/or diffusion and emitted through stem surfaces and the canopy. However, recent observations from dynamic stem chambers suggest that stems may also act as active sinks for atmospheric CH₄. Despite these findings, the extent and drivers of stem CH₄ consumption remain poorly characterized across biomes, species, and environmental gradients, and its quantitative relationship to stem respiration has not been established. Moreover, previous studies captured only snapshot fluxes, leaving diurnal patterns of CH₄ exchange uncharacterized. Here, we address these limitations by combining real-time measurements of stem CH₄ and O₂ uptake under ambient conditions in a California cherry tree, using a dynamic stem gas exchange system with three chambers receiving a continuous flow of ambient air and automated chamber and reference air sampling every 10 min. Our results confirm that stems of upland trees can actively consume both atmospheric CH₄ and O₂, but with decreasing temperature sensitivity as daily temperatures increase. Early mornings were marked by rapid influxes of both gases, followed by declining uptake as temperatures rose further. Methane uptake was tightly coupled with O₂ influx and represented a minor (0.012% ± 0.002%) fraction of stem respiratory activity, as determined by concurrent O₂ uptake. These findings suggest that while atmospheric CH₄ oxidation is a minor respiratory process in stems, it is strongly linked with stem physiological activity. This challenges the current assumption that terrestrial CH₄ uptake is driven solely by microbial methanotrophy and raises the possibility that living stem tissues may contribute to CH₄ oxidation through an as-yet-unidentified plant-based mechanism.

Atmospheric greenhouse gases↗
DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗
DOE OSTI2025

Relativistic gas accretion onto supermassive black hole binaries from inspiral through merger

Accreting supermassive black hole binaries are powerful multimessenger sources emitting both gravitational and electromagnetic (EM) radiation. Understanding the accretion dynamics of these systems and predicting their distinctive EM signals is crucial to informing and guiding upcoming efforts aimed at detecting gravitational waves produced by these binaries. To this end, accurate numerical modeling is required to describe both the spacetime and the magnetized gas around the black holes. In this paper, we present two key advances in this field of research. First, we have developed a novel 3D general relativistic magnetohydrodynamics (GRMHD) framework that combines multiple numerical codes to simulate the inspiral and merger of supermassive black hole binaries starting from realistic initial data and running all the way through merger. Throughout the evolution, we adopt a simple but functional prescription to account for gas cooling through photon emission. Next, we have applied our new computational method to follow the time evolution of a circular, equal-mass, nonspinning black hole binary for ∼200 orbits, starting from a separation of 20⁢𝑟 𝑔 and reaching the postmerger evolutionary stage of the system. We have shown how mass continues to flow toward the binary even after the binary “decouples” from its surrounding disk, but the accretion rate onto the black holes diminishes. We have identified how the minidisks orbiting each black hole are slowly drained and eventually dissolve as the binary compresses. We confirm previous findings that the system’s luminosity decreases by a factor of a few during inspiral; however, we observe an abrupt increase by ∼50% in this quantity at the time of merger, likely accompanied by an equally abrupt change in spectrum. Lastly, we have demonstrated that during the inspiral, fluid ram pressure regulates the fraction of the magnetic flux transported to the binary that attaches to the black holes’ horizons.

Accretion disk & black-hole plasma↗
DOE OSTI2026

Refining a Novel Process Monitoring Method to Safeguard Continuously Cycling Designs Using Isotopic Ratios

In advanced reactor (AR) designs, a common feature is continuous chemical processing and circulation of the nuclear material. This work bridges a significant measurement gap in safeguarding reactors with circulating fuel or continuous refueling by leveraging and building on the isotope ratio method first developed by our team under an FY21 Advanced Reactors International Safeguards Engagement (ARISE) project (Uribe et al. 2021). In circulating fuel designs, the radioisotope inventory changes from traditional effects (e.g., radioactive decay, fission) but also includes material transport due to pressure and temperature gradients. Such designs may also require regular or continuous additions or removals during operation, which significantly increases the rate of inventory change compared to traditional pressurized water reactor (PWR)s. Thus, directly tracking the nuclear inventory is ineffective since the isotopes are continuously added and removed. The isotope ratio method instead focuses on detecting changes to the input and output flows of radioisotopes. Previous work showed that for well-chosen pairs of isotopes, the isotopic ratio provides a sensitive and lasting indicator of deviation from normal conditions (e.g., startup, shutdown, diversion). The isotope ratio method is a process monitoring method with potential for application in for forward-looking approaches to International Atomic Energy Agency (IAEA) safeguards. The original process monitoring method was developed for a specific case—the decay tank of a thorium-fueled molten salt breeder reactor. In this expanded work, we explored other types of reactors and processes with nonstationary (e.g., flowing) nuclear material, which are difficult to safeguard with traditional methods because of the transient nature of the systems. The goal of the present work is to generalize the isotope ratio method for use in processes with continuously flowing nuclear material. All continuous processes have an average time for isotopes to be replaced in the system. The isotope ratio method works by choosing radioisotopes with half-lives both above and below the processing time. The present work seeks to explore which isotopes are suitable for the method by simulating the nuclear inventory, radioisotope emissions, and detector responses for several classes of advanced reactors. While the method can in principle be applied to other processes (e.g., enrichment or reprocessing facilities), the present work limits scope to ARs with continuously flowing fuel. Section 2 details the mathematics supporting the isotope ratio method, and Section 3 introduces the representative ARs selected for this work. Section 4 discusses how each reactor was analyzed, and Section 5 showcases the results for each representative reactor. Finally, Section 6 provides concluding remarks and suggests pathways for further analysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗