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UCFER Fourth Round of Solicitations
RFP closed on February 6, 2019 after receiving forty-one proposals. The UCFER Technical Advisory Council, Core Competency Board, Executive Council, and external peer reviewers reviewed the proposals. Applicants requested a total of $10.6 million across the forty-one proposals, while only $1.9 million was available to fund projects. The following chart summarizes the funding requested by topic area:
Based on the review process, UCFER provided funding recommendations to NETL for consideration. The following six proposals received funding:
Upscaling Experimental Measurements to the Field Scale Using a Machine-Learning-Based, Scale-Bridging Data Assimilation Approach—Virginia Tech, 24-month project, $480,000
Upscaling laboratory-measured, core-scale rock and flow properties to the field scale is a fundamental challenge in a wide range of applications, including geologic carbon sequestration, oil and gas recovery, and transport in hydrologic systems. The project aims to combine physics with data analytics to address this fundamental challenge by developing a novel machine-learning-based, scale-bridging data assimilation framework.
The specific challenges in the upscaling process are extreme sparsity of available data and disparate scales at which the data are located.
To tackle these challenges, researchers propose to use a scale-bridging data assimilation framework to upscale core-plug-scale relative permeability measurements to the reservoir model cell scale, which allows the inference of smaller-scale parameters by assimilating the field data. To address the problem of data sparsity, researchers propose to use physics-informed machine learning to augment the amount of core-scale, multiphase flow property data by learning from both on-site and off-site data in order to predict multiphase flow properties based on rock/fluid features (e.g., contact angle, viscosity ratio, capillary number, and a rock’s mineralogy and petrophysical properties).
Here, on-site data refer to laboratory measurements on the core samples directly extracted from the carbon dioxide (CO2) storage reservoir of interest, whereas off-site data refer to the enormous amounts of core analysis data and X-ray CT image data collected and stored in NETL’s Geoimaging Lab and U.S. Department of Energy (DOE) databases. The four spatial scales involved in the upscaling workflow are the core scale (inches), lithofacies scale (one to ten feet), geologic model cell scale (ten to one-hundred feet), and reservoir model cell scale (one-hundred to one-thousand feet).
The researchers seek to: develop a statistics/simulation-coupled model to upscale core-scale relative permeability curves to the lithofacies scale; use machine learning to predict relative permeability curves for different lithofacies and then use analytical methods to upscale the lithofacies-scale curves to the geologic model cell scale and reservoir model cell scale; and develop a scale-bridging data assimilation framework to calibrate the upscaled relative permeability curves using field-scale observation data of CO2 plume migration.
Cheng Chen, assistant professor in the Department of Mining and Minerals Engineering at Virginia Tech, is principal investigator on the project.
Porous Silicon/Lignite-Derived Graphene Composite Anodes for Lithium-Ion Batteries—University of North Dakota, 18-month project, $295,311
This research leverages the unique properties of the low-rank coal lignite-derived humic acid (HA) and the porous silica (pSi). This project is a continuing collaboration between the University of North Dakota (UND)’s Institute of Energy Studies (IES) and a lithium-ion battery (LIB)-producing company, Clean Republic, LLC, based on their success in the development of cathode materials. A previous project established a high purity HA extraction procedure, a key step to this work. The ultimate outcome of this project is a market-ready silica-to-graphene (Si/G) anode material for LIBs with a well-balanced performance and price.
This eighteen-month project has two phases. In the first phase, efforts will focus on the development and optimization of the synthesis method for pSi/G anodes and evaluation of their electrochemical performance at the laboratory scale. In the second phase, scientists will scale up the optimal procedure.
This proposed project will have numerous technological and economic impacts and benefits. With a projected price of $150,000 per ton, if these pSi/G anodes could replace only 10 percent of the global market demand for LIB anodes by 2020, the total value will be $3 billion. This technology will stimulate other competitors to accelerate the commercialization of their products, and that will trigger a chain reaction in the entire LIB industry, improving the competitiveness of existing LIB products and promoting innovative products by domestic businesses.
This project can utilize the HA synergistically produced by a DOE project at UND aiming to extract rare-earth elements from lignite. The new project will promote the local energy-based economy. For example, the high-temperature treatment process (e.g., graphitization) can consume 15,000 kilowatt hours per ton, a quarter to a third of the total cost in the manufacturing of anode materials. That is very beneficial to North Dakota, a leading energy-exporting state in the United States.
Xiaodong Hou, research assistant professor in the Institute of Energy Studies at the University of North Dakota, is the principal investigator on this project.
Developing a Novel Ultrafine Coal Dewatering Process—Virginia Tech, 12-month project, $267,471
U.S. coal production has declined since 2014 due to competition from shale gas as an alternate fuel source to electricity generation. One bright spot for the coal industry is the export market, particularly for metallurgical coals, for which coal quality plays an important role.
Therefore, the objective of the proposed work is to develop a novel ultrafine coal dewatering technology. Coal fines are usually dewatered by filtration, for which a ten-fold decrease in particle size would require a ten thousand times higher pressure drop across a filter cake to achieve the same level of dewatering. In effect, mechanical dewatering has reached its limit. In the proposed work, an organic solvent displaces water on the surface of coal, and the spent solvent is recovered and recycled. If the solvent has a higher affinity for a coal than water does, the dewatering-by-displacement (DbD) process is spontaneous. Laboratory and pilot-scale test work showed that the process can reduce the moisture to less than 5 percent by weight of coal regardless of particle size. If this process is further developed for commercial deployment, U.S. coal can be cleaned to any desired levels, so that the U.S. coal industry can produce high quality coals that are more competitive at the export market but also can be used to produce high-value carbon products such as carbon fibers, graphene, and activated carbons, etc.
A specific objective of the proposed work is to develop an efficient solvent recovery system that can recover the bulk of a spent solvent by solid-liquid separation without a phase change and with only a small amount of the residual solvent recovered by evaporation in situ. The researchers will design the new dewatering system by developing a rigorous heat and mass transfer model and the related computer codes, followed by laboratory test work. The results will help create scale-up criteria via engineering and economic analysis and modeling work. It is anticipated that sufficient information will be generated within one year to design a larger prototype unit in cooperation with NETL, an equipment manufacturer, and the technology provider.
Rui Qiao, John R. Jones III Faculty Fellow at Virginia Tech, is principal investigator on the project.
Computer Vision and Machine Learning Making the Processing-Microstructure-Property Connection in Heat Resistant Alloys—Carnegie Mellon University, 24-month project, $240,000
Microstructure denotes the substructures that form from the interaction between composition and processing. A fundamental tenet of materials science is that processing generates the microstructure that mediates material properties – referred to as the PSP connection. In that sense, microstructure is the key link between what we control (processing parameters) and what we achieve (material performance).
Given its ability to find relationships in large, complex data sets, machine learning (ML) seems tailor-made for exploring PSP connections. In this project, the research team will develop and apply computer vision (CV) tools to create quantitative representations of microstructural images and apply ML methods to answer the question: Can material properties be predicted from images of the material microstructure? The researchers will develop tools to be relevant to the performance of heat resistant alloys used in power plants, initially 347 stainless steel and subsequently nickel-based superalloys.
The project objectives are to collect microstructural image data and property metadata for heat resistant alloy systems; develop material-agnostic CV techniques to extract knowledge from microstructural images; create ML systems to find relationships between microstructures and property metadata; and analyze and interpret the results to discover new PSP connections.
Developing a CV/ML system to discover PSP connections will involve three stages. In the first stage, the research team will assemble a data set of microstructural images and their associated property metadata. They will assess three approaches: identifying and extracting existing data from archives; collecting new data; and generating synthetic data. They will then select the best candidate based on data set attributes including size, cost, and quality.
In the next stage, the researchers will compare two CV image representation models —constituent segmentation and measurement and CNN-based hypercolumn pixels — in order to develop a CV approach to quantify the visual information contained in the microstructural images. Finally, the research team will choose an ML method suitable for learning from the selected image representation. The advantages of the integrative CV/ML system are that it is autonomous and unbiased, so can potentially discover trends that humans can’t perceive. However, it is a black box.
As the first application of these methods to heat resistant alloy design, this project is certain to provide critical experience and insight to inform the path forward, with the potential to revolutionize microstructural design for performance.
Elizabeth A. Holm, professor of materials science and engineering at Carnegie Mellon University, is principal investigator on this project.
Development of a Novel Supersonic Hybrid Non-Equilibrium Plasma Reactor for Efficient and Tunable Co-Production of Hydrogen and Value-Added Solid Carbons—Princeton University, 12-24-month project, $193,000
The objective of this proposal is to develop and optimize an innovative supersonic hybrid nonequilibrium plasma reactor for efficient and tunable co-production of hydrogen and value-added solid carbons with a negative CO2 footprint.
The research consists of four major thrusts: developing a supersonic hybrid non-equilibrium plasma reactor; characterizing and optimizing the hybrid plasma properties and the residence time of supersonic reactor to control non-equilibrium species excitation and energy relaxation to increase the yield of hydrogen and the quality of valued solid carbon production; understanding the effect of composition and impurity variations in natural gas on carbon and hydrogen production; and analyzing the energy, mass balance, and carbon footprints for the plasma synthesis in comparison with the commercial thermal cracking method.
Specifically, in the proposed supersonic hybrid non-equilibrium plasma reactor technique, the researchers introduce two novel design concepts to maximize the yield of hydrogen and the value of solid carbon. The use of hybrid gliding arc with a nanosecond repetitive discharge or a microwave discharge will realize both high electron density and high electron energy for efficient non-equilibrium plasma enhanced natural gas dissociation to hydrogen and carbon.
The use of supersonic flow not only reduces the pressure to shift the chemical reaction equilibrium towards hydrogen and carbon formation but also increases the plasma uniformity and reduces flow residence time so that the vibrational excitation mode of methane remains in non-equilibrium for fast dissociation into hydrogen and carbon. By using this novel plasma reactor, the plasma properties and the yield of hydrogen and solid carbon and the morphology of carbon will be quantitatively examined by using Thomson/Raman scattering, micro-gas chromatography, and scanning electron microscopes.
Researchers will analyze the yields of hydrogen and carbon, mass balances, the quality of the energy costs, and the CO2 footprint of using U.S. electricity production. Researchers will compare those results with the commercial thermal cracking method. Collaborations will be established with NETL scientists, making use of their existing facilities such as their microwave reactor and materials characterization equipment to develop a supersonic hybrid gliding arc and a novel microwave reactor.
Yiguang Ju, Robert Porter Patterson Professor at Princeton University, is the principal investigator on this project.
Metal-free Catalyzed Synthesis of Novel Carbon by Carbon Allotrope Seeds—Penn State, 12-month project, $189,451
This proposal seeks to overcome catalyst deactivation and address catalyst regeneration by negating the need for separation and recovery of the catalyzed carbon from catalyst while simultaneously optimizing the amount/type of high-value carbon produced in order to improve process economics in thermo-catalytic decomposition (TCD) of natural gas. The project will demonstrate carbon allotrope fragments as catalyst “seeds” by which to synthesize novel solid carbon forms under TCD conditions while also eliminating the need for catalyst separation and regeneration.
Specific objectives include: identifying the different solid carbon forms “catalyzed” by carbon allotrope segments; optimizing the carbon catalyst to obtain the desired solid carbon types; optimizing the experimental parameters to obtain the best solid carbon yields; and optimizing the amount of hydrogen produced by these catalyst systems.
Researchers will use carbon allotrope fragments as “catalysts” to seed directed growth of novel carbon forms. Allotropes will include carbon nanotubes (CNTs), graphene and fullerene-like carbons. Researchers will activate these fragments prior to TCD by partial oxidation, mechanical milling and plasma exposure, respectively. Acting as consumable catalysts, the carbons will seed directed growth of similar morphologies under TCD conditions using natural gas as feedstock. Reaction conditions will range between 700 – 1,100℃ using varied synthetic natural gas mixtures, to test by design of experiments natural gas component contributions and effects of including impurities such as CO2 and hydrogen sulfide (H2S). Toward scalability and economic assessment, catalyst development and testing will proceed from fixed bed to entrained flow. Researchers will seek process intensification by using microwave energy to increase radical concentrations driving the carbon growth.
Researchers can use decomposed natural gas to manufacture hydrogen and solid carbon products without producing CO2. The co-production of hydrogen and high-value solid carbon materials from natural gas offers opportunities to reduce the costs associated with large-scale hydrogen energy products; utilize domestic natural gas for manufacturing energy and synthetic carbon products; and enable scalable and economic production of novel carbons for advanced construction and structural materials.
Randy Vander Wal, professor of energy and mineral engineering, materials science and engineering, and mechanical engineering at Penn State, is the principal investigator on this project.