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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Dual Chemical Looping/Catalytic Process for Alkylation of Benzene With Ethane and Propane Yielding Ethylbenzene and Cumene Over Copper‐Containing Mordenite

Given the sustained demand for alkylated aromatics and the strained olefin market, there is an urgent need to develop efficient one‐step processes for the direct alkylation of aromatics using alkanes instead of olefins. Such technologies offer greater energy efficiency and sustainability by eliminating the need for separate, energy‐intensive alkane dehydrogenation steps. In this work, we report a dual chemical looping / catalytic process that couples alkane dehydrogenation with aromatic alkylation over a copper‐containing mordenite yielding up to 25% of alkylated aromatics with >97% selectivity per cycle. In situ MAS NMR and FTIR spectroscopies combined with DFT calculations showed that the alkylation of benzene with alkanes proceeds via a π‐bounded Cu(I)‐olefin intermediate, which subsequently interacts with benzene, catalyzed by Brønsted acid sites, leading to alkylated products that readily desorb from the active material into the gas phase. DFT calculations show that alkylation mediated solely by Cu(I) has prohibitively high barriers (>1.8 eV), whereas a bi‐functional pathway involving both Cu(I) and Brønsted acid sites can proceed with significantly lower barrier (0.8 eV) through a concerted C–C bond formation and proton transfer step.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Development of physically transparent, predictive structure-performance relationships for rational design of multi-component catalytic materials. Final Report

Solid surfaces act as catalysts for many important chemical processes, including commodity chemicals production, energy conversion and pollution mitigation. The outcome of catalytic processes is mainly governed by interactions of adsorbates with active sites on the catalyst surface. These interactions can be dramatically affected by the changes in the local characteristics of the active site, which can be accomplished by an introduction of various promoters and poisons, or by alloying with other metal atoms. In fact, many commercial heterogeneous catalysts contain metal nanoparticles promoted by various additives. Our goal is to develop fundamental understanding about the impact of perturbations in the local geometry of a catalytic site on the local surface reactivity. These insights are crucial for a more systematic and rational catalyst design. The project aims to advance the field of catalysis by formulating reliable structure/performance relationships and utilizing these relationships to identify novel catalytic materials. Benefits for DOE and our society are in the development of fundamental theories that guide the process of discovery of energy-efficient and environmentally friendly heterogeneous catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SURFACE FINISHING AND ELECTROLESS NICKEL PLATING OF ADDITIVELY MANUFACTURED (AM) METAL COMPONENTS

This study investigates the application of electroless nickel deposition on additively manufactured stainless steel samples. Current additive manufacturing (AM) technologies produce metal components with a rough surface. Rough surfaces generally exhibit fatigue characteristics, increasing the probability of initiating a crack or fracture to the printed part. For this reason, the direct use of as-produced parts in a finished product cannot be actualized, which presents a challenge. Post-processing of the AM parts is therefore required to smoothen the surface. This study analyzes chempolish (CP) and electropolish (EP) surface finishing techniques for post-processing AM stainless steel components CP has a great advantage in creating uniform, smooth surfaces regardless of size or part geometry EP creates an extremely smooth surface, which reduces the surface roughness to the sub-micrometer level. In this study, we also investigate nickel deposition on EP, CP, and as-built AM components using electroless nickel solutions. Electroless nickel plating is a method of alloy treatment designed to increase manufactured component's hardness and surface resistance to the unrelenting environment. The electroless nickel plating process is more straightforward than its counterpart electroplating.. We use low-phosphorus (2-5% P), medium-phosphorus (6-9% P), and high-phosphorus (10-13% P). These Ni deposition experiments were optimized using the L9 Taguchi design of experiments (TDOE), which compromises the prosperous content in the solution, surface finish, plane of the geometry, and bath temperature. The pre-and post-processed surface of the AM parts was characterized by KEYENCE Digital MicroscopeVHX-7000 and Phenom XL Desktop SEM. The experimental results show that electroless nickel deposition produces uniform Ni coating on the additively manufactured components up to 20 μm per hour. Mechanical properties of as-built and Ni coated AM samples were analyzed by applying a standard 10 N scratch test. Nickel coated AM samples were up to two times scratch resistant compared to the as-built samples. This study suggests electroless nickel plating is a robust viable option for surface hardening and finishing AM components for various applications and operating conditions.

Keywords: additive manufacturing, fatigue, chempol↗
DOE OSTI2022

Physics-informed machine learning modeling for predictive control using noisy data

Due to the occurrence of over-fitting at the learning phase, the modeling of chemical processes via artificial neural networks (ANN) by using corrupted data (i.e., noisy data) is an ongoing challenge. Therefore, this work investigates the effect of both Gaussian and non-Gaussian noise on the performance of process-structure based recurrent neural networks (RNN) models, which take the form of partially-connected RNN models in this work, that are used to approximate a class of multi-input-multi-outputs nonlinear systems. Furthermore, two different techniques, specifically Monte Carlo dropout and co-teaching, are utilized in the development of partially-connected RNN models. Here, these two techniques are employed to reduce the over-fitting in ANNs when noisy data is used in the training process and, hence, to improve the open-loop accuracy as well as the closed-loop performance under a Lyapunov-based model predictive controller (MPC). Aspen Plus Dynamics, a well-known high-fidelity process simulator, is used to simulate a large-scale chemical process application in order to demonstrate the anticipated improvements in both open-loop approximation and closed-loop controller performance in the presence of Gaussian and non-Gaussian noise in the data set using physics-informed RNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

In Situ Chemical Monitoring and Imaging of Contents within Microfluidic Devices Having a Porous Membrane Wall Using Liquid Microjunction Surface Sampling Probe Mass Spectrometry

The ability to observe dynamic chemical processes (e.g., signaling, transport, etc.) in vivo or in situ using nondestructive chemical imaging opens a new door to understanding the complex dynamics of developing biological systems. With the advent of “biology-on-a-chip” devices has come the ability to monitor dynamic chemical processes in a controlled environment, using these engineered habitats to capture key features of natural systems while allowing visual observation of system development. Having the capability to spatially and temporally map the chemical signals within these devices may yield new insights into the forces that drive biosystem development. Here, a porous membrane sealed microfluidic device was designed to allow normal microfluidic operation while enabling continuous, location specific sampling and chemical characterization by liquid microjunction surface sampling probe mass spectrometry (LMJ-SSP MS). LMJ-SSP was used to extract fluids with nL-to-μL/min flow rates directly from selected areas of the microfluidic device without negatively impacting the device function. These extracts were subsequently characterized using MS. This technique was used to acquire MS images of the entirety of several multi-input microfluidic devices having different degrees of fluid mixing. LMJ-SSP MS imaging visualized the spatial distribution of chemical components within the microfluidic channels and could visualize chemical reactions occurring in the device. These microfluidic devices with a porous membrane wall are wholly compatible with the construction of biology-on-a-chip devices. This ultimately would enable correlation of biosystem physical structure with an evolving chemical environment

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Structural analysis of molten materials by a remote fiber optic Raman sensor

This study presents a novel in situ high-temperature fiber optic Raman probe that enables the study of the physical properties and structure of molten samples at temperatures up to 1400 °C. To demonstrate the functionality of the high-temperature fiber optic Raman probe, different composition mold fluxes were evaluated in this report. The Raman spectra at flux molten temperature were successfully collected and analyzed. A deconvolution algorithm was employed to identify peaks in the spectra associated with the molecular structure of the components in each sample. Here, the experimental results demonstrate that the composition-dependent Raman signal shift can be detected at high temperatures, indicating that molten materials analysis using a high-temperature Raman system shows significant promise. This flexible and reliable high-temperature Raman measurement method has great potential for various applications, such as materials development, composition and structure monitoring during high-temperature processing, chemical identification, and process monitoring in industrial production.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Towards Net-Zero: Nuclear-Assisted Waste Biomass to Liquid Fuel in Eastern Idaho

A nuclear assisted carbon negative hybrid energy process that enables production of synthetic bio-crude oil and biochar from Eastern Idaho waste biomass is proposed. The process integrates nuclear powered electricity with high temperature steam electrolysis and biomass hydropyrolysis. The bio-crude oil is of sufficient composition and blended with traditional crude oil at a refinery. Hydrogen from the electrolyzer is pressurized and inserted into the pyrolyzer. Non condensable gases generated in the hydropyrolysis process are burned with oxygen from the electrolyzer to produce heat for the electrolyzer, biomass dryer, and pyrolyzer. The biochar is returned to the soil via fertilizer application and remains there for thousands of years. Since the total process uses nuclear generated electricity, the carbon in the biochar is ultimately sequestered from the atmosphere, thus making the process carbon negative. Using Eastern Idaho wheat or barley straw, this hybrid energy process has the potential to provide an alternative petroleum source. Two options exist for the system design: 1) send electricity from the nuclear plant and straw to a chemical processing plant to produce the bio-crude and biochar, 2) construct the biomass processing facility near the nuclear plant to allow use of nuclear-generated process heat to drive the chemical. Process model description and results are discussed. The process is sized to produce gasoline and diesel at the rate that the INL uses every day for fleet usage.

09 BIOMASS FUELS↗
DOE OSTI2020

Heat Integration Optimization and Dynamic Modeling Investigation for Advancing the Coal-Direct Chemical Looping Process

The purpose of the project is to address the optimization and startup operation of a modular coal direct chemical looping (CDCL) combustion system integrated with a steam cycle for power generation to reduce the risks involved in further scale-up of the technology. The modular reactor design of the CDCL process provides flexibility in the fabrication of the reactor and in its operating capacity (i.e. turndown ratio) at the cost of a more complex heat exchange network (HEN) design and integration. To address the technology gaps and advance the efficiency and economic feasibility of the CDCL technology, the project will perform a detailed and comprehensive analysis of the integration of a modular CDCL reactor system and a steam cycle system under both static and transient conditions via HEN process performance simulations and system dynamic modeling, respectively. The scope of work consists of 1) Experimental and computational studies of the CDCL combustor reactor 2) Comprehensive static (i.e. steady-state) system HEN design analysis in CDCL 550 MWe commercial unit for power generation and 3) Dynamic modeling of site specific design of 10MWe CDCL large pilot plant. The project team has successfully developed and validated a kinetic model for the oxidation of oxygen carriers in the combustor using the unreacted shrinking core model (UCSM). The model is capable of capturing the oxidation kinetics of fully or partially reduced oxygen carrier particles. A computational fluid dynamics (CFD) model is developed to simulate the hydrodynamics, heat transfer, and chemical reaction occurring in the CDCL combustor. The model is developed in MFIX and ANSYS Fluent. Key aspects of CDCL combustor operation, including heat transfer, oxygen carrier oxidation, and the transport of oxygen carrier particles, are simulated using this CFD model. The HEN for a commercial scale 550 MWe CDCL power plant is simulated and optimized using ASPEN Plus. Practical design considerations are incorporated based on industrial experiences. The performance and cost for the commercial CDCL plant is updated based on these analyses. A dynamic model for the 10 MWe CDCL pilot plant is developed in ProTRAX simulation software. The model is based on the pilot plant design developed in project DE-FE0027654 “10 MWe CDCL Large Pilot Plang – Pre-FEED Study” and the steam cycle data obtained from Dover Light & Power plant. The transient behaviors during pilot plant load variation are simulated using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Antifoam Development for Eliminating Flammability Hazards and Decreasing Cycle Time in the Defense Waste Processing Facility

The Savannah River National Laboratory (SRNL) was requested to develop a new antifoam control method for the Defense Waste Processing Facility’s (DWPF) Chemical Process Cell (CPC). SRNL completed testing of both chemical and nonchemical foam controls. The nonchemical foam controls were either ineffective (or worse, created more foam) or impractical (a water spray can control foam, but excessive water is needed). As a result, the focus of this study was on finding a superwetter or commercial antifoam for controlling foam. Thirty potential antifoams were tested as part of this study. A series of tests were developed to help screen out ineffective alternatives including: 1. Spreading testing of superspreaders, 2. Foam column testing with physical simulants, 3. Boiling testing with physical and chemical simulants, 4. Days-only Sludge Receipt and Adjustment Tank (SRAT) process simulations with sludge (containing noble metals and mercury), Precipitate Reactor Feed Tank (PRFT), and Slurry Mix Evaporator Feed Tank (SEFT) simulants in the RC1 Reaction Calorimeter (purchased for antifoam testing), and 5. Around-the-clock SRAT and Slurry Mix Evaporator (SME) process simulations with sludge(containing noble metals and mercury), PRFT, and SEFT simulants in the RC1 Reaction Calorimeter. Evonik Surfynol® MD20, a commercially available defoamer, was relatively effective in controlling foam, while remaining chemically stable in SRAT and SME processing across the pH range of 4 to 13. No degradation products were detected in the offgas, in the condensate or in the SRAT and SME products. In nitric-glycolic acid flowsheet testing, 250 mg/kg Evonik Surfynol® MD20 was needed for foam control compared to 1,625 mg/kg for Antifoam 747, DWPF’s current antifoam. In nitric-formic acid flowsheet testing, 1,125 mg/kg of Evonik Surfynol® MD20 was needed to control foam throughout the SRAT and SME cycles. The commercially available superspreader Momentive™ Y-17112 was even more effective than Evonik Surfynol® MD20 as both a defoamer and an antifoam. Not only was the foam destroyed upon addition but also was less persistent between additions. It was the most effective antifoam in testing using both the nitric-glycolic acid flowsheet and the nitric-formic acid flowsheet. In nitric-glycolic acid flowsheet testing, only 100 mg/kg Momentive™ Y-17112 was needed to control foam throughout the SRAT and SME cycles. In nitric-formic acid flowsheet testing, 300 mg/kg Momentive™ Y-17112 was needed to control foam throughout the SRAT and SME cycles. Momentive™ Y-17112 is also resistant to hydrolysis as demonstrated by its chemical stability in SRAT and SME processing across the pH range of 4 to 13 and lack of degradation products in offgas or condensate. Both candidates were effective as potential replacements for Antifoam 747, with Y-17112 demonstrating superior foam control. During nitric-glycolic flowsheet testing 50% less antifoam was needed when using Momentive™ Y-17112 compared to MD20. During nitric-formic flowsheet testing 75% less antifoam was needed when using Momentive™ Y-17112 compared to MD20. Foam remediated with Momentive™ Y-17112 was less persistent throughout testing. In addition, no degradation products were detected in the offgas, in the condensate or in the SRAT and SME products. Based on this testing, Momentive™ Y-17112 is clearly superior to Evonik Surfynol® MD20 and Antifoam 747, especially for the nitric-formic acid flowsheet processing; it is recommended that Momentive™ Y-17112 replace Antifoam 747 in DWPF. An antifoam addition strategy is recommended for both the nitric-glycolic acid flowsheet and the nitric-formic acid flowsheet. Implementation of Momentive™ Y-17112 is expected to decrease SRAT and SME boiling times by up to 50%, eliminate the issues resulting from Antifoam 747 degradation products, and minimize foamovers. To validate the effectiveness of these defoaming agents, SRNL recommends irradiation of a SRAT or SME product simulant containing fresh antifoam. The goal of this testing is to determine whether the irradiation causes decomposition of the antifoam that would make it less effective or produce new species in the offgas or slurry. This testing began in April 2020. An evaluation should be completed to determine the thermolytic hydrogen and methane generation rate in downstream equipment, including the High-Level Waste evaporators.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Utilization of Hemp Processing Waste for 3D Printing of Biocomposites

Unlike stem biomass, the residues after the extraction of cannabidiol (CBD) oil from hemp flower are challenging to utilize because of their high extractive content (~ 40%, mainly lipids) and are typically considered waste and landfilled. This study presented a novel approach to effectively valorize this underutilized hemp processing waste via chemical processing for three-dimensional (3D) printing applications. Hemp processing waste was processed with sodium hydroxide (NaOH) to control extractives for solving nozzle clogging and then applied as a biofiller in polylactic acid (PLA) composites to improve the mechanical strength. The novelty of this work lies in demonstrating that controlled extractive removal via NaOH treatment not only improves processability but also enhances mechanical performance in 3D-printed biocomposites. We systematically investigated the effect of the processed biofiller content (2.5–10 wt%) on the mechanical and thermal properties of the biocomposites. The decrease in the content of extractives reduced the non-structural components and improved the surface compatibility of the hemp waste with the PLA matrix, thereby enhancing the polymer-biofiller interactions. The best performance was achieved at 2.5 wt% loading, where Young’s modulus increased from 2.3 GPa to 2.6 GPa and tensile strength from 42.7 MPa to 48.8 MPa. Interestingly, the complete removal of extractives also reduced the mechanical strength of their biocomposites, indicating the interfacial adhesion effects of extractives. Furthermore, this study provides new insights into balancing extractive content for optimal mechanical properties, offering a sustainable solution for waste valorization in additive manufacturing.

Additive manufacturing↗
DOE OSTI2023

Catalyst: Fast and flexible modeling of reaction networks

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst’s broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

SRNL Sludge Batch 10 Qualification SRAT and SME Off-Gas Results

Savannah River National Laboratory (SRNL) completed a small-scale demonstration of the Defense Waste Processing Facility (DWPF) Chemical Process Cell (CPC) utilizing the nitric-glycolic acid (NGA) flowsheet to support Sludge Batch 10 (SB10) qualification. The demonstration utilized a Tank 51 slurry sample washed by SRNL (with added H-canyon material). The purpose of this document is to report the observed off-gas results from the demonstration. With the NGA flowsheet, DWPF has a CPC hydrogen generation limit of 2.4×10 -2 lb/h. The peak observed rate was nearly 90 times less than that limit during SRNL testing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Manufacturing Process Design of a Micro-Scale Liquid-Liquid Extractor and Multi-phase Separator

Advances in process innovation within the chemical process industry are impeded by the cost of producing new innovative components. In this paper, various manufacturing process routes were evaluated for scaling up a micro-scale liquid-liquid extractor and multi-phase separator capable of separating immiscible phases that are used for the extraction of a chemical product into a lower boiling point solvent to make downstream distillation easier. A manufacturing process design methodology was used to evaluate both photochemical machining and binder jetting as a means for producing the micropost arrays within the separator. Cost curves show that for a 0.3 mm deep micropost array, photochemical machining was found to be much cheaper than binder jetting. However, because binder jetting processes enable microposts with over 13 times higher depth-to-width aspect ratios, binder jetting was found to decrease the overall costs of the extractor/separator by more than half for a 500 barrel per day plant, with breakeven as low as 15 barrels per day. These economics were confirmed by building extractor/separator plates and characterizing them dimensionally and functionally. The binder jet plates were found to perform better than photochemical plates leading to the complete separation of the organic phase from water. The utilization of slower sintering cycles combined with surface grinding provided acceptable dimensional tolerances for the binder jet plates. Using pressurized air during depowdering improved the economics for the binder jetting process.

42 ENGINEERING↗
DOE OSTI2020

Ex-Situ Recrystallization of CIGS via Metal Halides

Post Deposition Treatments (PDT) have been key to improving the efficiency of CIGS solar cells. While efficiency has continued to increase, for thermal evaporation processes, high substrate temperature and low deposition rates are a necessity. For many chemical processes, chemical vapor transport techniques can be used to both purify and grow both large and pure crystalline compounds. This is often done with a halide species and temperature. Here, by using the substituent metals of CIGS in confluence with a halide, we explored the possibility to grow a CIGS layer at low temperature and fast rate while still having sufficiently high efficiency.

14 SOLAR ENERGY↗
DOE OSTI2020

The 2020 plasma catalysis roadmap

Abstract Plasma catalysis is gaining increasing interest for various gas conversion applications, such as CO 2 conversion into value-added chemicals and fuels, CH 4 activation into hydrogen, higher hydrocarbons or oxygenates, and NH 3 synthesis. Other applications are already more established, such as for air pollution control, e.g. volatile organic compound remediation, particulate matter and NO x removal. In addition, plasma is also very promising for catalyst synthesis and treatment. Plasma catalysis clearly has benefits over ‘conventional’ catalysis, as outlined in the Introduction. However, a better insight into the underlying physical and chemical processes is crucial. This can be obtained by experiments applying diagnostics, studying both the chemical processes at the catalyst surface and the physicochemical mechanisms of plasma-catalyst interactions, as well as by computer modeling. The key challenge is to design cost-effective, highly active and stable catalysts tailored to the plasma environment. Therefore, insight from thermal catalysis as well as electro- and photocatalysis is crucial. All these aspects are covered in this Roadmap paper, written by specialists in their field, presenting the state-of-the-art, the current and future challenges, as well as the advances in science and technology needed to meet these challenges.

Bogaerts, Annemie (ORCID:0000000198756460)↗
DOE OSTI2021

Modeling of Glycolate Destruction in the Recycle Collection Tank

The Savannah River Site’s DWPF is being upgraded with the introduction of the NG flowsheet. Glycolic acid has been shown to be a superior alternative to formic acid for sludge processing. The new flowsheet improves or maintains necessary parameters such as 1) reduction of mercury, 2) adjustment of feed rheology, 3) pH stability, and 4) adjustment of melter oxidation/reduction potential. Further, the use of glycolic acid virtually eliminates the potential for catalytic hydrogen generation in DWPF processing DWPF process condensates are collected and returned to the Savannah River Site (SRS) CSTF. The RCT collects off-gas condensate during chemical processing, vitrification, and other unit operations performed in DWPF and is the singular return vessel delivering recycle effluent back to CSTF. Each batch of recycle will have a small amount of glycolate from chemical processing and melter off-gas condensates. To avoid potential flammability issues due to thermolysis of glycolate in the CSTF, Savannah River National Laboratory (SRNL) provided to Savannah River Remediation (SRR) at their request a Task Technical and Quality Assurance Plan (TTQAP) to quantify and mitigate glycolate returns via DWPF’s recycle stream. The request included testing of a process to oxidize glycolate and other organic species that are responsible for hydrogen generation from thermolysis. Following that work SRR provided a Task Technical Request (TTR) that requested process modeling. In 2021 a TTQAP was issued to cover the modeling work. Modeling draws data from laboratory scale studies using chemical simulants and radioactive waste samples. Chemical kinetic modeling was performed to evaluate the feasibility of using sodium permanganate to destroy glycolate in the RCT. The results from the laboratory studies were summarized in a series of reports. Reference 9 is a report of lab scale processing of actual DWPF Slurry Mix Evaporate Condensate Tank (SMECT) and Offgas Condensate Tank (OGCT) samples in the SRNL Shielded Cells. Tests at caustic conditions demonstrated sodium permanganate was effective in converting glycolate to oxalate, and permanganate (Mn 7+ ) is reduced to manganate (Mn 6+ ) with no significant formation of carbon dioxide or carbonate. Equation (1) was found to best describe the observed reaction of glycolate with permanganate under nominal (60 to 145 mg/L in RCT) glycolate entrainment conditions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from used fuel for recycling. The separation of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. This testbed will allow researchers to refine separation processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry utilizing centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on acquired sensor data to extract signal features. The goal is to provide a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗