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At least 199 records · Page 11

Coal to Carbon Fiber (C2CF) Continuous Processing for High Value Composites (Final Report)

Coal tar is a condensed and recovered by-product of the coking of metallurgical coal for steel production. The heaviest fraction of distilled coal tar is an isotropic pitch largely used as a binder in the manufacturing of carbonaceous electrodes for primary aluminum smelting and in electric arc furnaces. Coal tar pitch offers high carbon yield upon carbonization. In this project, a process to convert the domestically sourced isotropic coal tar pitch, containing very low particulates (QI = 0.32 wt.%), to form flow-domain mesophase pitch amenable for melt spinning into precursor (green) fibers for carbon fiber, was developed and optimized. The final reproducible processing developed is reviewed in this report, along with several characterizations of the mesophase pitch. A final definition of the characteristics of a ‘spinnable’ mesophase pitch is presented. With this mesophase pitch, reproducible and stable multifilament melt spinning was developed, producing green fiber tows with filament diameters of approximately 20 m. The multifilament melt spinning was the most challenging aspect of the project and required the most effort. The best practices learned from this project for melt spinning are reviewed in this report. Once spun, the green fibers were oxidatively stabilized, carbonized and graphitized under inert gas atmosphere to form the final carbon fibers. Given the relatively high softening point of the mesophase pitch, no issues of interfilament fusion were observed during batch oxidation, and subsequent batch carbonization and graphitization went smoothly in all cases. An ~ 80 wt.% conversion of the green fiber mass to final carbon fiber was achieved. After graphitization, the carbon fibers showed high tensile moduli (most were ~ 600 GPa, or 87 Msi) consistent with commercially-available high-performance pitch-based carbon fiber. However, tensile strength and strain to failure were comparatively low. Further work to reduce defects in and on the fiber surfaces would increase these properties. SEM imaging of the graphitic fiber textures is presented herein. Rudimentary composites were fabricated from the carbon fibers and characterized showing similar modulus to baseline composites fabricated with commercial carbon fiber. Finally, a basic economic analysis was done showing the potential to increase the value of the isotropic coal tar pitch by up to 13.6 to 136 times based on a carbon fiber value of $\$$5/lb to $\$$50/lb, respectively. Moreover, the site case study suggests that the coal tar from the single integrated steel mill could supply production of up to 16 kt/yr of carbon fiber. Finally, a technological gap analysis was done which shed light on remaining technical challenges. These challenges included: recovery and utilization of condensates from the mesophase pitch processing, further advancing and increasing stability of the multifilament melt spinning processing, optimization of the oxidation processing, defect reduction for increased carbon fiber strength, and the development of a weaving process towards carbon fiber fabrics. To maximize the coal value chain, the primary objectives of this project were to (a) develop and scale efficient processing technology for producing melt-spinnable mesophase pitch from isotropic coal tar pitch, (b) clarify and simplify tedious continuous fiber processing technologies (particularly multifilament melt spinning of mesophase pitch) towards the efficient production of high performance carbon fiber, and (c) demonstrate and characterize representative composite parts derived from the final carbon fiber. Immense progress was made on all 3 objectives and is detailed in this report.

01 COAL, LIGNITE, AND PEAT↗

Estimating the Total Value Proposition of Coal Fly Ash and Produced Water

Here we describe the total potential "sale" value of produced water and coal fly ash due to the mineral content they contain. Produced water is water that is co-produced during oil and gas operations. Coal fly ash is the solid powder that remains after coal is burned. Both coal fly ash and produced water matter partly because of they contain critical minerals that have economic value and could be extracted. Here we calculate the total potential value of all the mineral products, critical minerals and other products such as salts, so that we can gain a more complete economic outlook on both feedstocks. Poster presented at the American Geophysical Union (AGU) Annual Meeting held in New Orleans, Louisiana, December 15 to 19, 2025.

critical minerals, lithium, rare earth elements↗

How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging

Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

What is Produced Water Worth? The Total Value Proposition in the Marcellus Formation and Permian Basin

Produced water is a byproduct of oil and gas extraction and is known to contain lithium, a critical mineral. Produced water can also contain other minerals and commodities that are recoverable and have economic value. Here we focus on estimating the total value proposition of produced water in two important basins. That is, we assess the potential ‘sale price’ of all commodities that could be reasonably extracted from a unit volume of produced water. This value estimate helps better understand the economic and technological opportunities associated with produced water.

02 PETROLEUM↗

Pilot-Scale Pelleting Tests on High-Moisture Pine, Switchgrass, and Their Blends: Impact on Pellet Physical Properties, Chemical Composition, and Heating Values

In this study, we evaluated the pelleting characteristics of southern yellow pine (SYP), switchgrass (SG), and their blends for thermochemical conversion processes, such as pyrolysis and gasification. Using a pilot-scale ring-die pellet mill, we specifically assessed the impact of blend moisture, length-to-diameter (L/D) ratio in the pellet die, and ratio of pine to SG on the physico-chemical properties of the resulting pellets. We found that an increase in pine content by 25–50% marginally affected the bulk density; however, it also led to an increase in calorific value by 7% and a decrease in ash content by 72%. A moisture content of 25% (wet basis) and an L/D ratio of 5 resulted in poor pellet durability at <90% and bulk density values of <500 kg/m 3 , but increasing the L/D ratio to 9 and lowering the moisture content to 20% (w.b.) improved the pellet durability to >90% and the bulk density to >500 kg/m 3 . Blends with ≥50% pine content resulted in lower energy consumption, while a lower L/D ratio resulted in higher pelleting energy. Based on these findings, we successfully demonstrated the high-moisture pelleting of 2.5 ton of pine top residues blended with SG at 60:40 and 50:50 ratios. The quality of the pellets was monitored off-line and at-line by near infrared (NIR) spectroscopy. Multivariate models constructed by combining the NIR data and the pelleting process variables could successfully predict the pine content (R 2 = 0.99), higher heating value (R 2 = 0.98), ash (R 2 = 0.95), durability (R 2 = 0.94), and bulk density (R 2 = 0.86) of the pellets. Thus, we established how blending and densification of SYP and SG biomass could improve feedstock specifications and that NIR spectroscopy can effectively monitor the pellet properties during the high-moisture pelleting process.

09 BIOMASS FUELS↗

Stream water sourcing from high-elevation snowpack inferred from stable isotopes of water: a novel application of d-excess values

About 80 % of the precipitation at the Colorado River's headwaters is snow, and the resulting snowmelt-driven hydrograph is a crucial water source for about 40 million people. Snowmelt from alpine and subalpine snowpack contributes substantially to groundwater recharge and river flow. However, the dynamics of snowmelt progression are not well understood because observations of the high-elevation snowpack are difficult due to challenging access in complex mountainous terrain as well as the cost and labor intensity of currently available methods. We present a novel approach to infer the processes and dynamics of high-elevation snowmelt contributions predicated upon stable hydrogen and oxygen isotope ratios observed in streamflow. We show that deuterium-excess (d-excess) values of stream water could serve as a comparatively cost-effective proxy for a catchment-integrated signal of high-elevation snowmelt contributions to catchment runoff. We sampled stable hydrogen and oxygen isotope ratios of the precipitation, snowpack, and stream water in the East River, a headwater catchment of the Colorado River, and the stream water of larger catchments at sites on the Gunnison River and Colorado River. The d-excess of snowpack increased with elevation; the upper subalpine and alpine snowpack (> 3200 m) had substantially higher d-excess compared to lower elevations (< 3200 m) in the study area. The d-excess values of stream water reflected this because d-excess values increased as the higher-elevation snowpack contributed more to stream water generation later in the snowmelt/runoff season. End-member mixing analyses based on the d-excess data showed that the share of high-elevation snowmelt contributions within the snowmelt hydrograph was on average 44 % and generally increased during melt period progression, up to 70 %. The observed pattern was consistent during 6 years for the East River, and a similar relation was found for the larger catchments on the Gunnison and Colorado rivers. High-elevation snowpack contributions were found to be higher for years with lower snowpack and warmer spring temperatures. Thus, we conclude that the d-excess of stream water is a viable proxy to observe changes in high-elevation snowmelt contributions in catchments at various scales. Inter-catchment comparisons and temporal trends of the d-excess of stream water could therefore serve as a catchment-integrated measure to monitor if mountain systems rely on high-elevation water inputs more during snow drought compared to years of average snowpack depths.

54 ENVIRONMENTAL SCIENCES↗

Applying Value at Risk Analysis towards Energy Efficiency Investments

Value at Risk (VaR) is a measure of risk for potential losses within a portfolio of investments. Investments in energy efficiency in a manufacturing environment compete in terms of payback period, ease of investment, and transaction costs with other investments (production, safety, engineering, etc.). Because manufacturers have limited amounts of capital to allocate, investments in productivity tend to be prioritized over energy efficiency projects that offer similar paybacks. Value at Risk (VaR) can be used to quantify how risk-weighted expenditures change over time. Energy efficiency investments are often believed to offer relatively stable payoffs compared to changes in production and other assets, particularly when evaluated using methodologies such as simple payback. Incorporating methodologies such as VaR can provide a stronger business case for energy efficiency investments by quantifying value beyond what is captured in other Return on Investment calculations.In this paper we outline a methodology for applying VaR in the context of the variety of investments made by manufacturing organizations. Additionally, we profile the stability of energy efficiency (EE) and renewable energy (RE) investments compared to other types of investments made in manufacturing. By applying VaR to these investments, energy managers can better translate the financial benefits of energy-related capital projects in a way that is more compelling to Corporate Finance and Treasury departments. This common language also allows Corporate Finance and Treasury departments to support large, strategic deployments of capital for such projects based on a targeted objective – the management of financial risk for the organization’s shareholders.

Levine, Eli↗

Electrolysis' Potential Value for Supporting the Electrical Grid

The H2@Scale concept includes a value proposition for electrolysis that benefits both hydrogen markets and the electricity system. Electrolysis has the potential to be an alternative low-cost hydrogen source by using low-price electricity that may be available for only a limited number of hours annually. It also has the potential to provide value to the grid through use of that low-cost electricity. By doing so, electrolysis can create a price floor which could support additional investment in low-cost, dispatch-constrained electricity (LDE) generation such as photovoltaic solar, wind, and nuclear. The rapid-response characteristics of low-temperature electrolysis enables it to also provide ancillary services to the grid such as operating reserves and frequency control. We evaluate the economic performance of water electrolysis in a selection of electricity markets - both current and potential future markets. To perform that evaluation, we collect locational marginal prices (LMPs) in each market, calculate levelized hydrogen costs, and compare them to cost estimates of producing hydrogen via steam methane reforming of natural gas. Those calculations also include electolyzer capital and operating costs. We perform the calculations across a range of capacity factors to identify the minimum levelized hydrogen cost in that range. We discuss the implications of that capacity factor for each market analyzed. For a couple of the analyzed electricity markets, we include the potential value of providing ancillary services in the calculation and discuss those impacts. LDE generation has been shown to suppress electricity prices because it generates electricity when the resource is available (i.e., the sun is shining, wind is blowing, nuclear heat is being produced) and the generation cannot be reduced without an economic penalty. Price suppression is likely to reduce investment in those technologies due to reductions in projected income of each marginal unit. If electrolyzers are willing to pay for the LDE, it could provide a price floor for electricity. We analyze how setting a price for LDE could impact the electricity generation fleet, the availability of LDE, and the potential levelized costs of producing hydrogen using that LDE. Among other results, we will present a set of supply curves showing potential production costs and availability for hydrogen produced from LDE in a future with high penetrations of renewable electricity on the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Fractionation of Lignocellulosic Biomass into High-Value Products - CRADA Report

Idaho National Laboratory (INL) has developed a process for adding value in the biomass feedstock supply chain. The key aspects of this process include: (1) anaerobic and chemical treatment of biomass during storage, (2) fractionation of biomass into conversion-ready feedstock and high-value coproducts, and (3) high-moisture pelleting to increase the bulk density and stabilize the feedstock. The purpose of the chemical treatment is to overcome the recalcitrant nature of the feedstock to improve the biochemical conversion process of breaking down the biomass into sugars. To maximize the utilization of the biomass, the feedstock can be fractionated into hemicellulose and lignin rich fractions. Finally, to increase the materials overall energy density, produce a shelf-stable material, and to facilitate transport, the feedstock is densified in a pelleting process. The first two steps of the processes have been verified at bench scale showing an improvement in theoretical glucose yield from 23.1% to 44.5% and an improvement in theoretical xylose yield from 8.9% to 23.1% from ground corn stover. It is hypothesized that densification will further increase the carbohydrate yield in the sample. The goal of this project was to evaluate the technical feasibility and process economics of the above process and to provide preliminary data to Golden Leaf Energy, Inc to access its potential commercialization for converting biomass to ethanol and renewable chemicals. Corn stover (CS), the largest available agricultural residue, and Chinese tallow (CT), an invasive wood species with no current commercial value, were chosen as feedstocks for this project.

09 BIOMASS FUELS↗

Extending Power of Nature from Binary Problems to Real-Valued Graph Learning in Real World

Nature performs complex computations constantly at clearly lower cost and higher performance than digital computers. It is crucial to understand how to harness the unique computational power of nature in Machine Learning (ML). In the past decade, besides the development of Neural Networks (NNs), the community has also relentlessly explored nature-powered ML paradigms. Although most of them are still predominantly theoretical, a new practical paradigm enabled by the recent advent of CMOS-compatible room-temperature nature-based computers has emerged. By harnessing the nature's power of entropy increase, this paradigm can solve binary learning problems delivering immense speedup and energy savings compared with NNs, while maintaining comparable accuracy. Regrettably, its values to the real world are highly constrained by its binary nature. A clear pathway to its extension to real-valued problems remains elusive. This paper aims to unleash this pathway by proposing a novel end-to-end Nature-Powered Graph Learning (NP-GL) framework. Specifically, through a three-dimensional co-design, NP-GL can leverage the nature's power of entropy increase to efficiently solve real-valued graph learning problems. Experimental results across 4 real-world applications with 6 datasets demonstrate that NP-GL delivers, on average, 6970X speedup and 10^5x energy consumption reduction with comparable or even higher accuracy than Graph Neural Networks (GNNs).

artificial intelligence↗

Purdue University: Results of B-Value Tests for Rock Saturation

This dataset includes an Excel file with the results of B-value tests to determine the magnitude of the back pressure required for full saturation of Sierra White granite samples. Test were conducted on cylindrical rock specimens of Sierra White granite in a custom water pressurized chamber. Dimensions and properties of Sierra White granite specimens are summarized in the PDF file. The PDF file also includes a description of the B-value tests, experimental procedure, and results.

15 GEOTHERMAL ENERGY↗

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

97 MATHEMATICS AND COMPUTING↗

Solution of Periodic Boundary-Value Problems of the Spatial Theory of Elasticity in the Vector Form

We discuss boundary-value problems for the system of equations of the spatial theory of elasticity in the class of double-periodic functions and obtain a general solution of the system. We distinguish six types of elementary Floquet waves and examine their energy characteristics. We consider fundamental boundary-value problems in the half-space in the vector form. The diffraction problem for an elastic wave on a periodic system of defects in the vector form is reduced to the paired summator functional equation.

97 MATHEMATICS AND COMPUTING↗

Simulated effects of sample size and grain neighborhood on the modeling of extreme value fatigue response

Assessing the size of representative volume elements (RVEs) for fatigue-related applications is challenging. A RVE relevant to random microstructure requires a volume of material that is sufficiently large to capture the grain/phase heterogeneity that captures all statistical moments of the distribution of the driving force for fatigue crack formation at “hot spot” grains. Consequently, the large size of a microstructure RVE required to study fatigue phenomena is largely computationally intractable and difficult to explore. A more realistic objective in this work is to systematically study, as a function of the size of a statistical sample of microstructure, trends towards convergence of the simulated distribution of driving force for fatigue crack formation. Our present work accordingly leverages the recently developed open-source PRISMS-Fatigue framework to examine the trends in convergence of extreme value distributions (EVD) of Fatigue Indicator Parameters (FIPs) in progressively larger polycrystalline microstructure realizations of FCC Al alloy 7075-T6 using crystal plasticity finite element method simulations. The results are compared to the traditional method in which ensembles of statistical volume elements (SVEs) are simulated to build up statistics intended to approximate those associated with a larger volume of material. The convergence of EVDs with increase of size of a SVE of microstructure is closely related to the extent of grain nearest neighbor (NN) interactions. Accordingly, the sensitivity of the local micromechanical response at hot spot grains is quantitatively investigated by systematically varying the orientations of NN grains. Results indicate that SVEs with cubic crystallographic texture tend towards convergence of the EVD of FIPs with tens of thousands of grains while the random and rolled textures require larger volumes. Simple relationships based on microstructure parameters (e.g., Schmid Factor, grain size, NN misorientation) do not completely correlate to fatigue hot spot grains. Finally, the sensitivity of the extreme value fatigue response at hot spot grains extends to the 3rd NN when a single neighborhood grain orientation is altered.

36 MATERIALS SCIENCE↗

Decomposing sources of value for electricity and negative emissions technologies in net-zero power systems

Deep decarbonization of the US power system would require rapid deployment of variable renewable energy (VRE) resources, which are projected to provide a substantial share of electricity generation at the time of net-zero emissions. However, the exact share of generation met by VRE and the roles of other technologies in supplying key electricity services—energy and firm capacity—remain uncertain. This study employs a detailed model of the US power sector to decompose the provision and value of electricity services, including negative emissions, by technology across a range of deep decarbonization scenarios. Results indicate that while technology deployment and the share of services provided by each technology vary significantly depending on future technological and market conditions, the value composition and future roles of individual technologies remain consistent. These findings offer guidance for research and development priorities and provide insights to inform electricity policy and planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Recent advances in CO 2 hydrogenation to value-added products — Current challenges and future directions

Climate change, global warming, fossil fuel depletion and rising fuel prices have created great incentives to seek alternative fuel production technologies. CO 2 transformation to value-added products using renewable H 2 has proven to be an emerging solution to enable this goal. In this regard, three different promising processes, namely methane, methanol and hydrocarbon synthesis via CO 2 hydrogenation are thoroughly discussed. In addition, the influential factors affecting process efficiencies such as catalyst design and mechanistic insight, operating conditions as well as reactor types are investigated, with key pathways that dictate catalyst activity and selectivity of the most promising materials described. Furthermore, a brief overview of the reactor configuration and its crucial role in the improving process viability is analyzed. Accordingly, fixed-bed, fluidized-bed, annular and spherical reactors along with H 2 O/H 2 perm-selective membrane reactors are disscussed for hydrocarbon production. In addition, different reactor configurations are compared to assess the best one that is adjustable depending on the reaction mechanism. In conclusion, a corrugated-wall dual-type membrane reactor is proposed as an emerging alternative for CO 2 hydrogenation to value-added products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Generating multiple benefits from forests by producing value-added products: Lessons from the Pacific Northwestern US

This paper asks what conditions are needed to counteract a declining forest industry. The mobilization of supply chains for new value-added forest products and services can be supported by programs designed to simultaneously achieve multiple goals related to health, climate, and rural economic development. But several challenges are identified that underscore the need to stack benefits and incentives to achieve muti-sectoral goals. Based on a case study in Washington state, we find opportunities and constraints for sourcing forest products are determined by the local and regional conditions such as availability of working forests and their residues, socioeconomic conditions, workforce availability, transportation options, and perceptions about social license of the new industry. Key recommendations are the need for (1) a consistent demand and resilient supply chain for the new product; (2) a stable, supportive policy and assessment framework; (3) timely, transparent, and trusted monitoring and reporting of benefits and costs; (4) fostering engagement and buy-in from stakeholders; (5) a context-specific evaluation of opportunities and constraints; and (6) agreement on the goals of sustainable forestry. A diversity of products and markets builds a more resilient forest-based economy by mitigating impacts of disruptions in any individual market, adding value to biomass, increasing employment, and reducing forest risks to fire, insects, and disease.

Bioenergy↗

An approach to urban waterway assessment using holistic values and reciprocity

Current aquatic ecosystem assessment methods and tools often focus on physical, chemical, and biological indicators of ecosystem health. This approach to ecosystem assessment is not always straightforward to execute in urban environments and ignores potential connectivity between social and environmental outcomes. During a workshop at the Symposium on Urbanization and Stream Ecology in Brisbane, Australia in 2023 (SUSE6), we developed an approach to urban aquatic ecosystem assessment that incorporates a holistic perspective. Specifically, our approach considers both environmental (biological, chemical, and physical integrity) and social (community connection, human safety, resource use) values of urban waterways. This approach is inclusive of Indigenous perspectives, such as the concept of reciprocity, whereby consideration of both the environment and society leads to a healthy ecosystem. Here, to highlight how this holistic assessment approach could be used, we present real-world examples that included assessing both environmental and societal values informed by reciprocity or balanced perspectives. This approach can be broadly applied and adapted to specific aquatic ecosystem conditions and projects, providing an inclusive, community-centered approach for assessing the health of waterways in urban environments.

Indigenous knowledge↗