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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An Innovative Zero-Liquid Discharge Intermediate-Cold-Liquid Eutectic-Freeze Desalination System

A novel freeze desalination technology is presented for purification of water produced from various industrial processes, including oil and gas extraction. The main innovative idea is introducing a water-immiscible intermediate-cold-liquid (ICL) medium which absorbs the cold from a refrigeration cycle and transfers it to a freezing chamber where it is mixed with brine. The cold ICL enters the freezing chamber at temperatures between -10°C to -30°C. Precooled brine in injected into the freezing chamber and pure water freezes out of the brine upon mixing with the ICL. The rate of freezing inside the chamber is controlled by adjusting the flow rate of the cold ICL relative to the inlet brine. Both eutectic freeze and non-eutectic freezing are possible by controlling the temperature of the freezing chamber. The solid-liquid slurry created in the freezing chamber flows to a filter separation unit after passing through a separation column. The ice crystals are separated from the liquid by a rotary filter device. The collected ice is rinsed by a centrifugal unit. The rinsed ice is melted to produce fresh water. The developed technology resolves the issues common to current freeze-desalination systems while offering superior heat transfer performance ascribed to direct-contact between the cooling medium and the brine in addition to producing solid salt and pure water. The system operates under atmospheric pressure. The low operation temperature minimizes the corrosion issues common in systems exposed to brine. Also, due to the low operating temperature, there are no concerns about evaporation of volatile organics from the produced water during the treatment. The developed technology effectively eliminates the use of chemicals for pretreatment of brine. The system can be constructed from commercially available components. Extensive testing using a lab-scale prototype by using synthetic brine and actual produced water with TDS values from 50,000 ppm to 300,000 ppm showed that freshwater could be achieved with relatively small dependency on the quality of the input brine. The quality of the treated water was found to be mainly a function of the duration of the centrifugal process. Treated water with TDS as low as 200 ppm could be achieved in the tests. Thermo-economic modeling coupled with system level thermochemical process model were developed for prediction of the LCOW. The LCOW was predicted to be $\$$0.50 to $\$$0.90 per barrel of produced water. The potential markets for deployment of the technology are the oil and gas, in-land thermoelectric powerplants, RO desalination plants, and mining industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Overview of the Second Workshop on Scholarly Document Processing

With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 2nd Workshop on Scholarly Document Processing (SDP) at NAACL 2021 as a virtual event (https://sdproc.org/2021/). The SDP workshop consisted of a research track, three invited talks, and three Shared Tasks (LongSumm 2021, SCIVER, and 3C). The program was geared towards the application of NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.

Beltagy, Iz↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

Sattar, Naw Safrin↗
DOE OSTI2022

Distributed Real-time Plume Monitoring for Deep Sea Mineral Extraction​

In the emerging industry of deep-sea mining for minerals and deposits (e.g. polymetallic nodules for nickel, cobalt, copper, and manganese), more data is required to understand the effects of sediment plume generation and predict the distribution of disturbed sediment. There are two main sources of plume generation, the first being at the active mining site where the “collector” directly removes the top layer of the sea floor. The other is the “midwater plume” consisting of unwanted sediment that was collected during extraction that is pumped back into the aphotic zone. The vast majority of plume generation is caused by the collector, causing detrimental and long-lasting impacts on seafloor ecosystems due to the lack of wave activity or strong currents at the sea floor. Therefore, it is crucial to invest in the infrastructure to support the study and constant monitoring over a large area of the sea floor where plume generation is present. Due to the limited number of usable channels and power requirements, current subsea wireless communications technologies are not well suited to instrumenting the large areas of the sea floor needed to monitor plume migration. The scope of this effort is to transition experimental demonstrations of high-bandwidth, full-duplex scalable underwater laser communications to the seafloor in an open ocean environment. Specifically tackling challenges associated with the dynamic nature of the subsea world, including but not limited to, deployment logistics, sustainability, and range. The goal is to enable the internet of underwater things for deep sea industries by broadening the capabilities of subsea communications. By using high-precision laser transmitters, many of the challenges current subsea optical systems face can be circumvented, such as power consumption, interference, and bandwidth limitations. This approach lends itself to wireless interlinking multi-node networks, in series or parallel, facilitating the implementation of a wide array of sensor types. This interlinking allows all the data gathered from the network to be processed through a single hardline uplink to the surface, lowering the complexity required for near real-time data processing. Additionally, the laser control systems produce metadata that can be used to help characterize the water column between the nodes. Combining data from various sensors such as turbidity, temperature, current velocity with metadata such as beam attenuation and deflection can produce a high-resolution model of sea floor conditions around an active mining zone. The resulting near real-time model can be used to optimize location and flow rate of the mining operation to minimize and quantify the environmental impact.

Mons, Ishan↗
DOE OSTI2022

Economic Extraction, Recovery and Upgrading of Rare Earth Elements from Coal-Based Resources (Final Report)

The overall objective of this project is to produce a rare earth elements product with greater than 8% rare earth elements from coal-based resources using an economically viable and environmentally benign processing methodology. The objective of the coal processing work will be to acquire appropriate coal feed and produce a viable clean coal product, a concentrated REE feed stream, and a pyrite stream (where available) that can be used to enhance leaching. The objective of the bio-oxidation portion of the project is to utilize bacteria to oxidize sulfide minerals and ferrous ions to ferric ions to accelerate leaching and remove the sulfides to prevent future acid mine drainage and related liabilities. The objective of the solution conditioning work is to remove iron and control leaching solution conditions to facilitate extraction of REEs without significant thorium extraction. The objective of the column leaching work is to engineer/model and simulate heap leaching to demonstrate low-cost extraction of REEs from coal-based resources. The objective of the solvent extraction work is to selectively extract and recover through controlled stripping of REEs that are solubilized through leaching. The objective of the precipitation portion of the project is to recover the REEs from the solvent extraction stripping solutions and dry them to achieve the final product of rare earth elements that is > 8 % REEs. The objective of the technical, economic, and environmental analysis is to determine the overall viability of the processing approach that is demonstrated in this project

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Integrated CO 2 -facilitated Hydrometallurgical and Electrochemical Technology for Sustainable Mining and Recovery of Critical Elements from Wastes and Ashes

The availability of critical materials and other metals is very important for our national security and economic growth since they are widely used in defense and energy applications including batteries and wind turbines. Unfortunately, high grade ores have become scarce and their productions is dominated by limited countries. Thus, there is a strong need to obtain these materials from domestic sources. Municipal wastes are great candidates since they often contain high concentrations of metals. Since the metals in wastes and waste ashes are not in the form found in natural ores and contain a wide range of impurities, conventional extraction processes would not be effective. Thus, our project has developed an innovative MIDAS process based on the integrated CO2-facilitated hydrometallurgical and electrochemical technology that can effectively recover critical materials and metals from waste ashes while minimizing the environmental impacts. We have (i) characterized waste ashes to understand their physico-chemical properties, (ii) developed green solvent systems for hydrometallurgical extraction of critical elements and metals from waste ashes, (iii) explored electrochemical interfaces to refine the extracted metals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Analysis of backwash settings to maximize net water production in an engineering-scale ultrafiltration system for water reuse

Ultrafiltration (UF) has been widely utilized as water pretreatment for different applications especially in water reuse. The UF system operation is characterized by a filtration phase, where particles accumulate on the membrane surface resulting in an increase in the transmembrane pressure (TMP) and a cleaning phase, where foulants are removed through cleaning cycles including physical backwash and chemical-enhanced backwash (CEB). In this study, data from an engineering-scale UF system treating reclaimed wastewater were used to assess the impact of backwashing on the filtration process. TMP backwash trigger, backwash duration, and CEB frequency were purposely varied for a cycle-by-cycle investigation on the net water production, water recovery, initial operating TMP, and filtration cycle duration. As the TMP backwash trigger was varied between 62 and 145kPa, the maximum net water production (63 m 3 /d) was achieved at 103kPa and water recovery remained relatively constant at approximately 92%. Backwash durations of 45, 65, and 85s were performed where both net water production and water recovery yielded similar results (~63 m 3 /d and ~ 91%) compared with 103kPa TMP backwash trigger. The CEB frequency was also lowered from one every three backwashes (1/3) to 1/6 and 1/12 and resulted in decreased net water production and water recovery while the initial TMP increased. Interestingly, the total number of CEBs remained approximately constant regardless of their frequency. In conclusion, results suggest that CEB is an important fouling control process to maximize water production.

42 ENGINEERING↗
DOE OSTI2021

EGS Collab Experiment 2: Testbed Well Orientation Data

This dataset contains the north seeking gyro data for each of the 11 boreholes drilled at the Experiment 2 testbed on the 4100 foot level of the SURF (Sanford Underground Research Facility). Each gyro file is in individual folders for each well. A single folder called 'E2 All Well Trajectories' contains the trajectories for all the wells but in the mine coordinate system. An image of the well layout is provided in a PowerPoint.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Carbonation and Sulfidation of Mg- and Ni-Containing Solutions: Implications for Carbon Mineralization and Critical Element Recovery

Carbonation of alkaline earth metals ( e.g. , magnesium (Mg)) and sulfidation of nickel (Ni) are promising methods to achieve concurrent carbon mineralization and selective Ni recovery. However, the coexistence of alkaline earth metals and Ni from silicate ores or mining wastewater complicates the carbonation and sulfidation owing to cation coprecipitation. To better understand simultaneous metal carbonation and Ni-sulfide formation, we used Mg- and Ni-containing solutions and systematically investigated the Mg and Ni coprecipitates’ phase transformation during sequential/concurrent carbonation and sulfidation. During a single carbonation process, hydromagnesite dehydrated and formed magnesite over time. Nickel bicarbonate formed and became a Mg–Ni carbonate solid solution because of their similar ionic radii. During a single sulfidation process, the pH did not affect Ni-sulfide formation, but it controlled Mg behavior. Specifically, at pH 9.6, brucite formed, while at pH 7.8, Mg 2+ remained in the solution. For the sequential carbonation–sulfidation process, Ni-carbonate formed during carbonation converted to Ni-sulfide because of the low Ni-sulfide K sp . For the sulfidation–carbonation process, Ni-sulfide remained the same even after carbonation and Mg-carbonate precipitates. For the concurrent carbonation and sulfidation process, Mg-carbonate and Ni-sulfide formed simultaneously. Finally, this study develops a scientific foundation of carbonation and sulfidation processes, benefiting coupled CO 2 storage and sulfide-enabled resource recovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The use and abuse of cinnabar in Late Neolithic and Copper Age Iberia

Abstract In this study, total mercury (THg) was analyzed in archaeological human bone from 23 sites dating to between the Middle Neolithic and the Antiquity. A total of 370 individuals from individual or collective burials was sampled, mostly using cortical bone from the humerus. These individuals were recovered from over 50 different funerary structures ranging from tholoi, pits, caves, and hypogea. Although cinnabar (HgS) is a likely cause of mercury poisoning and toxicity for people exposed to this mineral from mining or use as a paint or pigment, not all sites investigated here had cinnabar associated with the burials or other excavated areas. We found unusual levels of THg in many of the sampled individuals that we assume were caused by exposure to cinnabar in life, and not by diagenetic processes or other exposures to mercury such as through diet, which would only cause negligible accumulation of THg in bone. Our data, based on the largest sampling ever undertaken on contamination of human bone through archaeological evidence, provide a baseline for additional research on cinnabar and its use in Prehistory. Moderate to high levels of THg in human bone are mainly associated with societies dating from the second half of the 4th to late 3rd millennia B.C. (Late Neolithic to Middle Chalcolithic) in southern Iberia. By the Late Chalcolithic and Early Bronze Age, the use of cinnabar decreased significantly and became minimal or absent. The use and abuse of cinnabar appears to have been pervasive throughout the above‐mentioned period and particularly between c. 2900–2300 B.C. This occurred in connection with the high symbolic and probably sacred value of the substance, which was sought after, traded, and extensively used in a variety of rituals and social practices.

Emslie, Steven D.↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bioleaching ion-unexchangeable rare earth in ion-adsorption type rare earth waste tailing

Ion-adsorption type rare earth ores (IREO) in China serve as the primary source of medium and heavy rare earth globally. With the rapidly growing demand for rare earth elements (REEs) and the dwindling supply of premium IREO, enhancing the recovery of REEs, especially the ion-unexchangeable REEs with ultra-low content and ambiguous speciation from IREO or its tailing, has become a critical trend and challenge. Here, this study identified the occurrences of ion-unexchangeable REEs, primarily detected in Fe-enriched minerals, xenotime, and monazite of IREO using TESCAN integrated mineral analyzer (TIMA) and laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS) analysis. To extract these elusive REEs, a bioleaching technique utilizing Aspergillus niger (A. niger) metabolites was proposed, achieving a leaching yield of 31.4 wt%. Complementary sequential chemical extraction methods (SCEM), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) calculations further elucidate the underlying mechanism, involving the carboxylic acid produced by the metabolic process of microbes that dissolves goethite by disrupting Fe—O bonds and liberating REEs, which complexed with carboxylate (R—COO—), to promote further dissolution. This work offers insight into enhancing the recovery of ion-unexchangeable REEs from IREO or its tailing, paving the way for sustainable and efficient rare earth mining practices.

42 ENGINEERING↗
DOE OSTI2020

A Perspective on the Successes of the NNSS Underground Test Area (UGTA) Activity - 20221

Between 1951 and 1992, 828 underground nuclear tests were performed at the Nevada National Security Site (NNSS). Underground testing was done in five major testing areas, which included (1) Frenchman Flat, (2) Rainier Mesa/Shoshone Mountain, (3) Yucca Flat/Climax Mine (4) Central Pahute Mesa, and (5) Western Pahute Mesa. About one third of the underground tests were detonated near, at or below the water table, and thus radioactive contamination was introduced to the groundwater system. The U.S. Department of Energy's Underground Test Area (UGTA) Activity was established in the late 1990's to address the fate and movement of residual radioactivity in groundwater, and characterize the risk that it may pose to NNSS workers and the offsite public. It has accomplished this goal through a process of iterative groundwater sampling and numerical groundwater flow and transport modeling, and long-term monitoring. The UGTA Activity draws on the expertise of scientists in the fields of geology, hydrology, radiochemistry, and risk assessment from the U.S. DOE staff, the lead contractor (currently Navarro) and many other organizations, in cooperation with the governing regulatory body, the State of Nevada's Division of Environmental Protection (NDEP). Los Alamos National Laboratory (LANL) is one of the participating research organizations involved with the UGTA Activity. Under the direction of DOE and the lead contractor, LANL's role in UGTA has evolved over time from an initial focus on geologic characterization, to conducting field and laboratory experiments, and finally to its current role of providing modeling and geochemistry expertise to characterize the rates and directions of groundwater and radionuclide movement. In its current role, LANL has either developed or provided the numerical tools for developing flow and transport models in each of the four major testing areas. In addition, LANL has provided an independent assessment of future groundwater flow and transport behavior through the analysis of naturally-occurring geochemical and isotopic tracers in groundwater. The five major testing areas are now in different stages of investigation or closure: (1) Frenchman Flat is in its fifth year of post closure modeling; (2) The Rainier Mesa/Shoshone Mountain Closure Report has been submitted to NDEP and is awaiting approval; (3) The Yucca Flat/Climax Mine Closure Report is under development; and (4) Central and (5) Western Pahute Mesa is still undergoing investigation and flow and transport model development. The success of the UGTA Activity in reaching or approaching closure in three of the five major testing areas is primarily due to the DoE's focus on regulatory closure requirements, but also is in part attributable to a rigorous internal review process and the involvement of NDEP and Nye County water professionals as participants and observers in the reviews. County and state involvement in the internal review process has promoted trust that the U.S. DOE is deeply committed to ensuring the present and future safely of NNSS workers and the surrounding communities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Data Mining – Image Analysis of Radiography for Zr Redistribution

The research effort described in this report represents a first attempt to investigate the radial redistribution of Zr in ternary fuel alloys U-xPu-10Zr (x = 0, 8, 19) irradiated in the in-reactor fuel experiments during the operation of the U.S. Department of Energy’s (DOE’s) Experimental Breeder Reactor II (EBR-II) at Idaho National Laboratory (INL) using methods of image processing on post-irradiation neutron radiographs. Approximately 130,000 metal fuel pins were irradiated in EBR-II during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed historically now allowing application of modern image analysis techniques to characterize fuel behavior, such as fuel swelling, fluff formation, and now, fuel alloy constituent redistribution. The redistribution of fuel components depends on the temperature field, radially, within the fuel. Specifically, Zr is expected to redistribute radially towards the center of the pin, as well as towards the outer zones. Currently, direct imaging of a pin cross-section through optical methods or scanning electron microscopy (SEM) is used to study the constituents’ redistribution, which is very time-consuming and can only be applied to a limited number of pins. An automated image processing technique allowing for the investigation of fuel radial redistribution zones would significantly accelerate data analysis. In general, if the fuel temperature is hot enough, three redistribution zones are expected, corresponding to the main fuel components (e.g., U, Pu, Zr). While more assessment will be performed in fiscal year (FY)-2023, it seems possible to differentiate the pins according to their fuel composition using image analysis techniques on neutron radiographs of metallic fuel pins based on the analysis to date.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Performance Testing of a Moving-Bed Gasifier Using Coal, Biomass, and Waste Plastic Blends with Washed and Unwashed Legacy Coals and Other Waste Fuels to Generate White Hydrogen

The objective of this effort, primarily funded by the United States Department of Energy (DOE), and led by the Electric Power Research Institute, Inc. (EPRI), with support by Hamilton Maurer International (HMI) and Sotacarbo S.p.A. (Sotacarbo), has been to qualify coal, biomass, and plastic waste blends based on performance testing of selected fuel pellet compositions in a pilot-scale updraft moving-bed (UDMB) gasifier. The testing provided relevant data to advance the commercial-scale design of the moving-bed gasifier to be able to successfully use these feedstocks to produce hydrogen. In particular, the effects of waste plastics on feedstock development (i.e., blending and pelletizing) and the resulting products (i.e., syngas compositions, organic condensate production, and ash characteristics) are the focus. The gasifier used for testing is HMI’s moving-bed gasifier, which has been proven capable of gasifying nearly all coal ranks. It has also shown the ability in prior testing work to gasify wood chips (biomass). However, mixtures of these fuels with plastic wastes have not been prepared and gasified together. The three feedstocks were densified and pelletized by California Pellet Mill (CPM) to meet the feedstock size required by Sotacarbo’s 30mm ID UDMB gasifier, under contract to HMI. The technical tasks and results from this two-year research project included: (1) Feed Procurement and Preparation: Nine different tri-fuel pellets were prepared from varying compositions of fresh mined PRB coal, corn stover biomass, and car fluff waste plastics. Tri-fuel pellets were produced by CPM and shipped to Sotacarbo’s test facility in Carbonia, Sardinia, Italy. (2) Test Plan Development: A test plan was created to define the test runs to be performed. The test plan detailed the different UDMB gasification tests to be performed in Sotacarbo’s 12-inch ID pilot scale gasifier, the process monitoring instrumentation used, and the extractive samples recovered for analysis of the total gasification process mass and energy balance. (3) Gasifier Testing: Nine different gasification runs were performed in the pilot-scale gasifier at Sotacarbo using nine different fuel feedstock compositions generated from varying mixtures of PRB coal, biomass, and plastic wastes. The testing generated performance data on gasification reaction efficiency and performance, yielding relevant data for models used to scale up the gasifier design. This task also included work to refurbish and reassemble the pilot gasifier at Sotacarbo and perform a baseline 100% PRB coal run. (4) Data Analysis and Reporting: Review of the data, determination of figures of merit, and interpretation of the results are reported in the project’s final report, published in March 2024. The results show that all tri-fuel pellets gasified well and maintained structural integrity throughout the gasification process. The syngas generated can be shifted to hydrogen by using commercial syngas shifting technologies. (5) High Fidelity computational fluid dynamics (CFD) Simulation: The National Energy Technology Laboratory (NETL) team performed CFD simulations of the UDMB gasifier for two of the tri-fuel pellets gasified in Sotacarbo’s pilot scale gasifier. The kinetic mechanisms for the pyrolysis of each constituent, PRB coal, corn stover biomass, and waste plastics are based on thermogravimetric analysis performed by Sotacarbo. The gasification model was validated by comparing the predicted syngas composition at the exit of the gasifier with the measured syngas composition. In addition, the reactor’s measured internal temperature profile agreed well with the predicted internal reactor temperature profile. These results validate that the model can be used to predict the performance of the updraft moving bed gasifier for different feedstocks and operating conditions. This paper summarizes the results of the completed work in which the pelletizing procedure was validated to ensure the viability of the tri-fuel pellets for the gasification runs performed at Sotacarbo’s 30 mm UDMB gasifier. The gasification performance data from this series of nine runs will enable modeling of a full-scale HMI industrial scale gasifier supporting both combined heat and power, and Hydrogen production from coal (both fresh mined and legacy) combined with various biomass and waste plastics. Additionally, plans and progress on a follow-up project, being executed by the same project team, will be presented. In this project, a total of twenty (20) different feedstocks are being prepared from varying compositions of biomass (both woody biomass and corn stover) with a mixture of legacy coal waste, plastic waste, and refuse-derived fuel (RDF). The testing will provide information on gasification reaction efficiency/performance, yielding relevant data for models used to scale up the gasifier design to 50 megawatt electric (MWe) (equivalent hydrogen production). Tests will also be performed on a bench-scale fluidized-bed gasifier for comparison purposes. The results of this testing will be used to specify the range of feedstock blends that can be successfully gasified as well as quantify gasifier outputs based on specific blends.

08 HYDROGEN↗