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At least 361 records · Page 20

Robust bidding strategy for aggregation of distributed prosumers in flexiramp market

Distributed prosumers (DPs) are the grid customers that own energy production/storage assets. Due to the flexibility and fast response of their assets, they can procure ancillary service products (ASP) in the wholesale market. An appealing ASP offered by California ISO in the real-time market (RTM) is flexiramp for which market participants do not submit direct offers, and the compensation is based on their energy opportunity costs. Here in this report, we propose a bidding strategy model for DP aggregator participation in the RTM considering energy and flexiramp. First, we develop a risk-averse optimization to determine the optimal energy and reserve product to trade in day-ahead market while considering proper amounts of flexiramp to trade in the RTM. In the RTM, to obtain optimal amounts of energy and flexiramp, the aggregator must submit hourly multi-level price-quantity energy bids for multiple RTM intervals with 15 min time-steps. On this basis, we propose a robust hourly economic bidding strategy model that determines the optimal energy bids in the RTM. We develop an adjustable robust counterpart of the model to address the RTM energy and flexiramp price uncertainties. The simulation results justify the efficacy of our proposed framework in gaining profits from the wholesale market.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Dynamic Linking of Upstream Energy and Freight Demands for Bio and Fossil Energy Pathways in the Global Change Analysis Model

Comprehensive study of the environmental impacts associated with demand for an energy resource or carrier in any one sector requires a full consideration of the direct and indirect impacts on the rest of the regional and global energy system. It is important to consider the energy that is consumed in producing primary energy resources and energy consumed in transporting these energy resources internationally and domestically in order to produce a complete picture of these impacts. Biofuels are especially complex since they have feedbacks not just to the energy system but also to regional and global crop markets. Different modeling and analysis strategies have been applied to consider these impacts, which occur “upstream” of final energy consumption, with some success. Traditional life cycle models allow for rich technological detail in linking upstream impacts of energy demand with downstream final consumption; however, they cannot by themselves represent dynamic economic feedbacks across multiple sectors and regions over time. Computable general equilibrium (CGE) modeling does account for economic feedback among all sectors and regions; however, CGE modeling does not lend itself to physical representation of technological detail. Partial equilibrium (PE) modeling is a heterogeneous category describing economic models that focus on a subset of the economy, and they differ in coverage and ease in incorporating more sectors and economic links. In this study, we present a strategy for dynamically including the direct and indirect impacts of energy demand with physical and technology detail by explicitly adding these upstream energy and transportation links to GCAM, a PE model of global energy, land use, and emissions. We incorporate the following inter-sectoral linkages: energy inputs to crop production, energy inputs to fossil resource production, and freight transport requirements of energy and agricultural commodities. We assess the implications of explicitly including these links by measuring the global impacts of increased corn ethanol demand in the United States with and without these links included. Although the net global impact of the upstream links on energy and emissions are relatively modest in the scenarios we studied, the inclusion of these links illustrates interesting trade-offs in energy and transportation demand among fossil fuel and agriculture sectors. These sectoral interactions suggest that this level of modeling detail could be important in evaluating future analytical questions.

Sampedro Martinez de Estivariz, Jon↗

A geospatial environmental and techno-economic framework for sustainable phosphorus management at livestock facilities

Nutrient pollution of waterbodies is a major worldwide water quality problem. Excessive use and discharge of nutrients can lead to eutrophication and algal blooms in fresh and marine waters, resulting in environmental problems associated with hypoxia, public health issues related to the release of toxins and freshwater scarcity. A promising option to address this problem is the recovery of nutrient releases prior to being discharged into the environment. Driven by the sustainable materials management concept, the COW2NUTRIENT (Cattle Organic Waste to NUTRIent and ENergy Technologies) framework is developed for the techno-economic evaluation and selection of nutrient recovery systems at livestock facilities. Furthermore, environmental vulnerability to nutrient pollution determined through a geographic information system (GIS)-based model and techno-economic information of different state-of-the-art nutrient management technologies are combined in a multi-criteria decision analysis (MCDA) model, resulting in the selection and economic analysis of the most suitable process for each studied livestock facility. This framework has been employed for studying the implementation of sustainable phosphorus management systems at 2,217 livestock facilities in the Great Lakes area, resulting in capital expenses of 2.5 billion USD if only phosphorus recovery technologies are installed, and up to 5.2 billion USD if nutrient management is combined with biogas and power production. However, considering potential economic incentives for the recovery of phosphorus, net revenues up to 230 million USD per year can be achieved. Therefore, the framework presented reveals the potential of implementing nutrient management systems at regional scale for the abatement of phosphorus releases from livestock facilities.

54 ENVIRONMENTAL SCIENCES↗

A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch considering Wind Power Penetration

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in this stochastic economic dispatch problem for forecasting purposes, the conventional Monte-Carlo method is prohibitively time-consuming for practical applications. To overcome this problem, here we propose a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling as the computer experimental design, a Gaussian-process emulator is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated stochastic economic dispatch model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.

17 WIND ENERGY↗

Assessing the Economic Value of Underground Thermal Storage for Hybrid Geothermal Power

Solutions are needed to address resource adequacy in the electric power system for highly decarbonized systems. The storage duration, the length of time a storage device can provide continuous output at its rated capacity, must be sufficient to receive full credit toward resource adequacy. Longer peaks and high fractions of variable renewable generation have increased the required duration to potentially seasonal durations. Underground Thermal Energy Storage (UTES) can be adapted to a hybrid storage power plant or heating and cooling applications to satisfy the need for long-duration storage. In this study, we use the Renewable Energy Deployment System (ReEDS) capacity expansion model to evaluate the increase in value for an enhanced geothermal system (EGS) resources by adding UTES. In modeled scenarios, using geothermal without storage as a baseline we compare the increase in economic value for plants with a range of storage characteristics. The added value of a hybrid storage plant changes depending on assumptions including the length of storage duration, efficiency, and ability to charge storage from the grid during periods of low energy prices. Relating proposed characteristics for geothermal UTES hybrids to the modeled economic value provides insight into economically viable costs for developing UTES as well as what combination of technology characteristics and future energy and policy assumptions drive significant value increases.

capacity expansion model↗

District Geothermal Heating + Cooling Deployment in a CT Environmental Justice Community

The report marks the team’s completion of all required tasks and milestones. Work completed for Task 1 (Technical and Economic Feasibility Assessment & Procurement Drafting) included development of analysis and design model; completion of technical, economic, and environmental assessments; and technical outreach and coalition design. Components for Task 2 (Outreach & Community Engagement) involved broad outreach and community-engagement efforts (including stakeholder meetings and a webinar as well as development of a formal engagement plan) and development of a web page and a case study. For Task 3 (Workforce Transition, Development, & Training Plan), the team undertook a formal statewide geothermal workforce needs assessment, developed corresponding recommendations for both the state as a whole and the Wallingford project, and held several workshops. For Task 4 (Project Management & Data Sharing), the team drafted a data-sharing plan.

15 GEOTHERMAL ENERGY↗

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Optimization for Bioenergy Systems

The Sustainable Aviation Fuel (SAF) Grand Challenge (Langholtz, 2024 ) seeks to generate 35 billion gallons of SAF each year by 2050, with corn stover, an agricultural byproduct, playing a key role as a feedstock. This study develops an optimization framework to enhance the quality and quantity of corn stover while ensuring economic and environmental viability. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model, we simulate the effects of cover crops on rotation yield, soil moisture balance, and nitrogen cycling across diverse climates and soils. The model outputs, including yield data and soil quality changes, inform a Mixed-Integer Linear Programming (MILP) optimization model. This model aims to maximize economic and environmental returns by incorporating production costs, direct and indirect income, and environmental incentives. The optimization model evaluates 280 agriculture management plans composed of various crop management strategies, including corn stover removal rates, cover crop adoption, and fertilization practices. It seeks to identify the optimal combination of crop and tillage decisions for each subfield, maximizing profits while enhancing soil carbon sequestration and reducing greenhouse gas emissions. Outputs include detailed subfield locations, optimal management plans, and profits per hectare and per acre, allowing for comparison with literature values on farm profits. This study provides a robust optimization framework supporting the SAF Grand Challenge by proposing economically viable and environmentally sustainable strategies for corn stover utilization. The findings highlight corn stover's potential as a sustainable feedstock for SAF production, offering practical solutions to enhance its quality and quantity while maintaining soil health. Idaho is used as a case study to demonstrate the framework's applicability and effectiveness in real-world scenarios. Langholtz, M. H., Davis, M., Hellwinckel, C., De La Torre Ugarte, D., Efroymson, R., Jacobson, R., Milbrandt, A., Coleman, A., Davis, R., Kline, K. L., Badgett, A., Curran, S., Schmidt, E., Theiss, T., Fried, J., English, B., Lambert, L., Cook, H., Field, J., ... Walker, L. (2024). 2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources. https://doi.org/10.2172/2441098 DSSAT Foundation. (2025). Decision Support System for Agrotechnology Transfer (DSSAT). Retrieved from https://dssat.net/

09 - BIOMASS FUELS↗

The impact of behavioral and geographic heterogeneity on residential-sector carbon abatement costs

Analyses of monetary and emissions savings from residential efficiency upgrades usually neglect behavioral differences between consumers. Variations in behavior are frequently large: 13% of the U.S. population sets thermostats for cooling at 20 °C (68 °F) or lower and 15% at 25.5 °C (78 °F) or higher. Efficiency analyses should account for behavioral heterogeneity as well as variations in climate and housing characteristics. We model energy and economic savings of efficiency upgrades for U.S. single-family detached houses, accounting for differences in thermostat settings, climate zone, fuel prices, and home characteristics. We consider five efficiency interventions: wall insulation, attic insulation, air sealing, high-efficiency furnaces, and high-efficiency air conditioners. Energy and economic savings vary widely by consumer; the average net present value for wall insulation is $3,020 United States Dollar (USD), but considering heterogeneity, it has a 10th percentile value of -$231 and 90th percentile value of $5,000. Neglecting heterogeneity, technology upgrades can be prioritized from lower to higher carbon abatement costs: air conditioners, wall insulation, furnace, air sealing, and attic insulation. Accounting for heterogeneity reorders this prioritization, and consumer-specific conditions affect mitigation costs such that technologies are mixed in with one another and are no longer arranged one after another. These results indicate that interventions to improve efficiency should consider differences between consumers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Techno-Economic Analysis and Market Potential of Geological Thermal Energy Storage (GeoTES) Charged With Solar Thermal and Heat Pumps

In this project, we developed a techno-economic analysis (TEA) model that can be used to evaluate the viability of a proposed Geological Thermal Energy Storage (GeoTES) design. This MATLAB-based model integrates distinct subsystem models for the reservoir, wells, power cycle, and solar field to capture their distinct characteristics. It applies this approach in simulating GeoTES storage and dispatch operations for durations ranging from hourly to seasonal. Using cases studies based on GeoTES designs provided by industry partners - Premier Resource Management (PRM) and EarthBridge Energy - we validated the TEA model estimations of system performance and costs (such as thermal and electrical power/energy inflow and outflow, capital costs, and levelized costs of energy and storage) for both concentrating solar thermal (CST) and Carnot Battery (CB) pairings with GeoTES (CST-GeoTES and CB-GeoTES). For the CST-GeoTES case, the model was validated against the proposed system designed by PRM. It showed good agreement with PRM's estimations when well and pump costs derived from PRM's estimations were used. When GETEM-based costs were used, there was a slight overprediction due to GETEM's project/site agnostic assumption of these costs. From a sensitivity analysis perspective, the levelized cost of electricity (LCOE) of the CST-GeoTES case was most sensitive to well flow rate and the charging temperature. An optimal design scenario resulted in an LCOE of 0.11 $\$$/kWhe. CST-GeoTES can also provide a source of heat to meet seasonal demands. With 12-hour and 24-hour levelized cost of heat (LCOH) of 0.018 $\$$/kWhth and 0.022 $\$$/kWhth, respectively, CST-GeoTES could be competitive in the California market with an average industrial price of natural gas in California between 0.041-0.047 $\$$/kWhth. The levelized cost of storage (LCOS) for CST-GeoTES depends on the energy storage duration. Although the LCOS is relatively higher for shorter durations (e.g., ~0.50 $\$$/kWhe for 1 hour of storage), it is an order of magnitude lower (0.06 $\$$/kWhe) for longer storage durations and competitive with lithium-ion batteries (beyond 12 hours of storage) and molten-salt thermal energy storage (beyond 32 hours). Energy. Three options were explored and applied to the EarthBridge case study: (1) A Carnot Battery design using R125 working fluid with both hot and cold storage; (2) A Carnot Battery design using R125 working fluid with only hot storage; (3) A Carnot Battery using a commercially available heat pump with carbon dioxide (CO2) working fluid and hot storage only. The CB-GeoTES with cold storage only had a slight (round-trip) efficiency advantage over the system without (43.4% vs. 42.8%). This is because the cold storage is limited by the freezing point of water, so the cold storage is not much colder than the environment. The system using commercially available technologies was the least efficient - partly because different cycles were used in the heat pump (CO2) and heat engine (binary cycle) which leads to some inefficiencies. Using the commercially available design, the levelized cost of energy (LCOS) from the model (0.10 $\$$/kWhe) was higher than that estimated by EarthBridge (0.068 $\$$/kWhe). This is because of the low round-trip (38.7%) efficiency of the commercially available design. Sensitivity analysis reveals that the model is most sensitive to electricity price. Including electricity price in the TEA for CB-GeoTES leads to an increase in LCOS from the base value to 0.25 $\$$/kWhe. To determine storage sites suitable for GeoTES, we gathered and analyzed geological, petrophysical, and geophysical data of oil and gas reservoir and aquifers in California and Texas. We down-selected possible sites based on cut-off values for site characteristics (e.g., reservoir temperature, formation thickness, permeability, porosity, depth, and brine salinity) and preliminary costs. Using this approach, the Carrizo-Wilcox, Yegua-Jackson, and Dockum brackish aquifers in Texas were identified as having the highest suitability. Similarly, in the central California region, the White Wolf, Belridge South Tulare, and Belridge South Reef Ridge were the most suitable. Going further, we assessed the storage potential in the selected sites. To do this we developed distributions of reservoir characteristic data and applied a Monte Carlo-based analysis to account for intrinsic uncertainty in the acquired data. The analysis revealed that the Carrizo-Wilcox aquifer had the highest storage potential with a mean capacity of 554 TWhth (i.e., 63 TWhe). The estimated capacity serves as an upper limit of storage potential given that not all fields in the basin will be developed. We participated in multiple outreach activities including conference presentations, panel session discussions, and the facilitation of a GeoTES workshop at the NREL Golden campus.

15 GEOTHERMAL ENERGY↗

Techno-economic analysis of carbon dioxide capture from low concentration sources using membranes

Rising carbon dioxide (CO 2 ) levels in the atmosphere lead to global warming, causing climate change. As such, carbon capture has become necessary to slow the increase and reduce CO 2 levels in the atmosphere. Point source emissions have a wide range of CO 2 concentrations, but emissions below 3% CO 2 have mostly been ignored because Carbon capture from these sources has been viewed as costly and economically unsustainable. Membrane technologies are considered the most viable solution by virtue of more energy-efficient operation. Our group at Idaho National Laboratory (INL) has developed poly[bis((2-methoxyethoxy)ethoxy)phosphazene] (MEEP)-based carbon dioxide selective membranes with CO 2 /N 2 selectivity greater than 40 and CO 2 permeability greater than 450 Barrer. To understand the economics of carbon capture, a spreadsheet-based techno-economic analysis (TEA) model was developed to consider multiple parameters, including selectivity and permeability of the membranes, performance conditions such as the number of stages, module material, electricity price, membrane price, and capital financing. The cost of carbon capture in US $\$$/metric ton was calculated at various purities and compared with other membrane processes, cryogenic capture, solvent-based capture, and pressure swing adsorption-based capture. It was determined that a MEEP-based three-stage process had a capture cost of US $\$$ 50.1/metric ton for 99.8% purity CO 2 from a 1% CO 2 feed source in nitrogen (N 2 ). In conclusion, the capture cost using the best performing Pebax-based membrane was 464% higher, cryogenic capture was 60%–140% higher, pressure swing adsorption was 55%–165% higher, and chemical absorption was -10%–110% higher than MEEP-based membrane capture, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The need to consider regional supply chains and water usage in H 2 –steel transition

The iron and steel industry is one of the largest contributors to U.S. and global greenhouse gas emissions. Hydrogen can act as a promising reducing agent and clean energy carrier to decarbonize this sector, and has received significant attention in terms of process modelling, techno-economic analysis, and life cycle assessment in recent years. Policy incentives, hydrogen storage and transportation, and water stress levels are key factors that require significantly more consideration in order to realize hydrogen’s potential to decarbonize this industry. This review demonstrates the need for a systematic understanding and critical assessment of these areas, and their profound impacts on the decarbonization of the iron and steel sector. Furthermore, hydrogen and water supply face competition from other hard-to-decarbonize sectors, which should be considered on national and regional levels. Lastly, future research should also consider the impact of other environmental factors and hydrogen leak when deploying hydrogen at scale for industrial decarbonization.

Green steel↗

Forecasting distributed energy resources adoption for power systems

Failing to incorporate accurate distributed energy resource penetration forecasts into long-term resource and transmission planning can lead to cost inefficiencies at best and system failures at worst. We have developed an open-source tool that employs an advanced Bass specification to calibrate and forecast technology adoption. The advanced specification includes geographic clustering, exogenously estimated market size, and dynamic time steps. Training on historical adoption of rooftop photovoltaics at the U.S. county-level and using detailed techno-economic estimates, our model achieves a two-year average mean-absolute-percentage-error of 19% in predicting system counts at the county-level, weighted by population. Model error was negatively correlated with market maturity - the error was 12% for counties in states with at least 28 W-per-capita of installed capacity. The advanced specification significantly reduces unweighted forecasting percent error compared to a conventional Bass specification: from 196% to 25% for capacity and from 226% to 22% for system count.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Techno-economic assessment of emerging CO 2 electrolysis technologies

Recycling of waste CO 2 to bulk chemicals has a tremendous potential for the decarbonization of the chemical industry. Quantitative analysis of the prospects of this technology is hindered by the lack of flexible techno-economic assessment (TEA) models that enable evaluation of the processing costs under different deployment scenarios. In this protocol, we explain how to convert literature data into metrics useful for evaluation of the emerging electrolysis technologies, derive TEA models, and illustrate their use with a CO 2 -to-ethylene example. For complete details on the use and execution of this protocol, please refer to Barecka et al. (2021a).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Upcycling waste polystyrene to adipic acid through a hybrid chemical and biological process

Oxidative catalytic depolymerization of polystyrene (PS) can produce benzoic acid, but the annual consumption of benzoic acid is ~40 times lower than PS. For this catalytic oxidation method to be a viable means to manage PS waste, benzoic acid should be converted to higher-volume chemicals. We demonstrate a hybrid chemical and biological process that uses PS as feedstock for production of adipic acid, a high-volume co-monomer for nylon 6,6 via benzoic acid. Mn/Br co-catalyzed autoxidation of PS to benzoic acid proceeds with a yield of up to 94% in a solvent mixture of benzoic acid and water. The PS-derived benzoic acid undergoes bioconversion at near-quantitative yield to muconic acid, which is readily converted to adipic acid through catalytic hydrogenation. Process modeling, techno-economic analysis, and life cycle assessment estimate an adipic acid minimum selling price of $3.18/kg, with a 61% decrease in greenhouse gas emissions relative to production from fossil fuels.

09 BIOMASS FUELS↗

Quantifying the Relationship Between Higher Photovoltaic Module Efficiency and the Adoption of Distributed Solar

The purpose of this study is understanding the relationship between module efficiency, system costs, and the economic and adoption potential of distributed rooftop solar. We use the dGen model to project the economic potential and adoption of high-efficiency photovoltaic (PV) modules across a range of efficiencies and cost scenarios in the continental United States. Our results reveal that high-efficiency modules can modestly increase the projected adoption of distributed solar, but system capital costs may play a larger role in changing the amount of projected adoption.

14 SOLAR ENERGY↗

Salton Sea Geothermal Development: Nontechnical Barriers to Entry – Analysis and Perspectives

Geothermal energy offers an opportunity to generate baseload, renewable energy that can help support the transition to an energy economy with reduced impacts on climate change and replace older, more expensive, nonrenewable, and more resource-impacting energy-generation facilities. The United States has the largest known geothermal resource in the world, with over 31 GW of conventional geothermal potential. However, due to market conditions, an inability to properly quantify both electrical grid benefits and resource stability, and the difficulty of exploring and developing the geothermal resource, few new geothermal projects have come online over the past three decades. The Salton Sea, in Imperial County, California, provides a prime location and opportunity to develop new geothermal resources. The Salton Sea contains a robust, well-mapped, geothermal resource, with opportunities for concurrent development of lithium and other mineral resources. This report describes the history of geothermal development at the Salton Sea and compares geothermal to other renewable energy sources in the area. The report then uses a techno-economic analysis (TEA) model to analyze the relative benefits and costs of various challenges and opportunities and provides recommendations for streamlining geothermal development at the Salton Sea and elsewhere. The challenges and opportunities analyzed in the TEA model were informed by stakeholder interviews and literature reviews. Based upon the identified challenges and opportunities and the results of the TEA model, primary findings are that certain nontechnical barriers such as permitting costs play only a minor role in determining the viability of development of the geothermal resource at the Salton Sea. Other barriers such as permitting timelines, government/agency coordination, and the potential co-location of lithium extraction with a geothermal plant may result in much larger impacts on project viability.

15 GEOTHERMAL ENERGY↗

Sustainable Ethylene via Chemical Looping – Oxidative Dehydrogenation

This Phase I final report evaluates chemical looping–oxidative dehydrogenation as a lower-energy and cost-competitive alternative to conventional ethane steam cracking for ethylene production. The project combined catalyst development and experimental testing with process modeling, techno-economic analysis, and life-cycle assessment. Results indicate that the proposed process could improve olefin production, reduce energy use, and lower production costs, particularly when integrated with carbon capture and renewable electricity. The report also identifies remaining technical, environmental, and safety considerations and presents a technology maturation plan focused on longer-term catalyst evaluation and advancement toward pilot-scale demonstration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗