Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “process design”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of a Flotation Simulator That Can Predict Grade vs. Recovery Curves from Mineral Liberation Data

Flotation is a kinetic process designed to separate mineral fines by control of hydrophobicity. In general, both the recovery and selectivity of a flotation process increase with contact angle. It is difficult, however, to model flotation kinetics using the thermodynamic parameter. In the present work, we consider flotation as heterocoagulation so that the bubble-particle interactions occurring in the pulp phase of a flotation cell can be modeled using the hydrophobic force as a kinetic parameter that can represent the role of contact angles in flotation. Here, the pulp-phase model developed in this manner has been combined with a froth-phase model developed earlier by considering the effect of particles in film thinning kinetics and bubble coarsening (Park et al, 2018). The combined model has been used to develop a flotation simulator that can be used to construct grade vs. recovery curves from feed characteristics (e.g., mineral liberation and particle size) and various operating parameters. The simulator has been tested successfully against a set of bench-scale continuous flotation test results reported in the literature (dos Santos and Galery, 2018).

58 GEOSCIENCES↗
DOE OSTI2024

Melt Crystallization of CsF from Alkali Fluorides

Fractional melt-crystallization is a technique used to separate components in a multicomponent liquid mixture through controlled cooling. In fiscal year (FY) 2023, this technique was successfully used to separate CsCl from LiCl-KCl. This demonstrated a potential route for concentrating electrorefiner fission product waste streams in pyrochemical fuel cycles, building on previous work that developed the melt-crystallization system for fission product removal from LiCl-based electrolytes used for oxide reduction. This work investigated whether a thermally controlled process of a solid-liquid separation process could effectively remove CsF from LiF-NaF-KF (FLiNaK)-CsF salt for MSR fuel cycle applications. The designed process aimed to recover purified LiF-NaF-KF salt as solid precipitates while concentrating CsF to a remaining salt heel. This concentrated CsF can then be immobilized during a salt waste stream treatment operation, minimizing waste volume.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerate Nuclear Research and Development by Reducing Time and Cost Spend in the Pre-conceptual Design Phase of Advanced Reactor Experiments

The design process of every new concept, such as advanced nuclear reactors or associated experiments, starts with the pre-conceptual design phase. In this phase, the viability of a wide range of design options needs to be assessed quickly, to understand the operating envelope and its feasibility. A variety of physics models (thermal-hydraulics, neutronics, mechanical design, etc.) has to be considered at this very first design stage and optimum component sizes and materials (e.g. heat exchangers, piping, turbomachinery, coolant type, etc.) have to be chosen for a given set of boundary conditions (e.g. heat source, heat sink, flow rate, etc.). Detailed solutions such as provided by high fidelity methods like computational fluid dynamics (CFD), Monte Carlo methods, etc. and even lower fidelity tools such as system or subchannel codes, etc. are usually not used during the pre-conceptual design due to the relatively long time needed to create input models, the computational time to obtain a solution and the lack of flexibility to quickly investigate different combinations of components, individual component sizes and material properties. High fidelity tools are usually only employed in the conceptual design and later phases once a base concept has been identified during the pre-conceptual design stage. The current practice during the pre-conceptual design stage is that analysts collect the needed equations, material properties, closure laws, etc. and create ad-hoc solutions form scratch for every new problem. There clearly is a lack of a flexible scoping tool that can be used during pre-conceptional design before higher fidelity tools (as described above) come into play. To reduce user errors in ad-hoc solutions and increase fidelity and efficiency, this project aims to investigate and develop a user-friendly scoping tool to address the thermal-hydraulic designing needs during preconceptual experiment design, i.e. Thermal-hydraulic Research Universal Scoping Tool (TRUST). The success of TRUST will provide the nuclear engineers with an easy-to-use and affordable calculator for early reactor system design and optimization.

42 ENGINEERING↗
DOE OSTI2024

TEAMER Technical Support for Aquantis (Modeling): Cooperative Research and Development Final Report, CRADA Number CRD-21-17763

NREL will provide numerical modelling support to Requestor by completing four successive stages of numerical modeling effort which are capable of accurate predicting the performance of the Aquantis turbine. NREL will develop a baseline OpenFAST model (a couple model of AeroDyn and ElastoDyn solver) of the Aquantis turbine, subsequently couples this model to OrcaFlex or within OpenFAST framework to preliminarily design and explore a full floating tidal turbine system in a control co-design process.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Downselection and Basic Properties of Additively Manufactured ODS Alloys

In the AMMT program the work package for development of new materials aimed to explore the advanced manufacturing techniques that are feasible for the manufacturing of advanced materials components. A merit-based feasibility study was attempted to identify an accelerated development path for dispersion strengthened (ODS) alloys by creatively combining additive manufacturing (AM) technologies with the recent advances in ODS materials and traditional manufacturing technologies. For the fiscal year, the primary tasks were focused to develop additive manufacturing (AM) and post-build processing route for oxide dispersion strengthened (ODS) ferritic (Fe-Cr alloy or 14YWT) and austenitic (Fe-Cr-Ni alloys or 316L and 316H) alloys and to perform basic microstructural and mechanical characterizations to provide feedbacks to the alloy and processing design. Further, the multi-laboratory effort created collaboratively a decision criteria matrix to evaluate and downselect the new materials processed by advanced manufacturing methods. This report is to describe the ODS alloy processing routes combining AM processes and post-build thermomechanical treatments, mechanical and microstructural characteristics of the newly developed materials, and the application results of the decision criteria matrix for the AM ODS alloys including a downselected material and feasible processing route. Key mechanical test results including tensile strength, tensile ductility, and fracture toughness data and are reported and explained. Higher strength was measured from the ferritic ODS alloys, while higher ductility and fracture toughness were measured from the austenitic ODS alloys. Many of the decision criteria were scored the same for the AM ferritic and austenitic ODS alloys, however, the generally known fact that the austenitic alloys have higher corrosion resistance and significantly better ductility. Although these scores are not significantly different to make them highly discernable, the austenitic ODS alloys were downselected to be the primary materials group in the future research on ODS materials in AMMT program.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2026

Making Small-Volume Heat Pump Water Heaters Larger: A Design Framework for Integrated Phase Change Material Heat Exchangers

Those that use electricity for water heating, the majority use resistive elements rather than heat pump water heaters (HPWHs), the latter of which use 60-70% less energy than the former. One major barrier to wider HPWH adoption is the added equipment required, which prevents current 50-80-gallon tanks on the market from fitting into smaller utility closets sized for 30-40 gallons, such as those found in manufactured housing. Additionally, these smaller HPWHs tend to underperform relative to their larger counterparts. One solution that addresses both space and performance concerns is thermal energy storage, and in particular, phase change materials (PCMs). PCMs have been studied extensively in building envelope and HVAC systems, but remain a nascent technology in residential water heating. While water itself has a uniquely high energy storage capacity, PCMs have an even higher energy storage density, thus providing the potential to elevate the performance of 40-gallon HPWHs to that of 50-gallon or larger tanks. This research is part of a larger project that seeks to utilize thermal energy storage to enable decarbonized water heating in low-income communities. In this study, we outline the design process used to produce novel PCM heat exchangers for use in small-volume HPWH tanks, including the identification of design constraints and performance targets relevant to real-world applications. In order to ensure optimal PCM utilization and tank storage capacity, we focus here on co-maximizing surface area and PCM volume in the heat exchangers; therefore, this research targets triply periodic minimal surface (TPMS) lattices. TPMS lattices boast enhanced heat transfer capabilities compared to traditional heat exchanger geometries and offer highly tailorable designs; thus, they pair well with the growing field of additive manufacturing, or 3D printing. Starting with a suite of TPMS lattices, we demonstrate a systematic approach for narrowing down feasible designs that comply with identified constraints while meeting PCM performance objectives.

25 ENERGY STORAGE↗
DOE OSTI2026

Design Considerations for Phase Change Material-Incorporated Heat Exchangers in Water Heating

In recent years, the buildings sector has seen major pushes towards decarbonization through innovations that promote deep electrification. 20% of an average household's energy use comes from water heating, and in the US, over half of all households still use gas water heaters. Of those that use electricity for water heating, the majority use resistive elements rather than heat pump water heaters (HPWHs), the latter of which use 60-70% less energy than the former. However, HPWHs tend to have larger dimensions, preventing current 50-80-gallon tanks on the market from fitting into smaller utility closets sized for 30-40 gallons, such as those found in manufactured housing. Additionally, these smaller HPWHs tend to underperform relative to their larger counterparts. One solution that addresses both space and performance concerns is thermal energy storage, and in particular, phase change materials (PCMs). This study outlines the design process used to produce novel PCM heat exchangers for use in small-volume HPWH tanks, including the identification of design constraints and performance targets relevant to real-world applications. To ensure optimal PCM utilization and tank storage capacity, this works seeks to co-maximize surface area and PCM volume; therefore, this research targets triply periodic minimal surface (TPMS) lattices, which boast enhanced heat transfer capabilities compared to traditional heat exchanger geometries. Starting with a suite of TPMS lattices, we demonstrate a systematic approach for narrowing down feasible designs that comply with identified constraints while meeting PCM performance objectives. Our current results indicate that tuning lattice properties can effectively produce geometries that provide enough energy storage to achieve a 50-gallon capacity out of a 40-gallon HPWH, even when placing the PCM heat exchanger inside the tank. Additionally, we demonstrate successful fabrication of lattices with these tuned properties.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A hybrid method to evaluate the life cycle climate performance of heat pumps

Heat pumps have a significant impact on the climate, and numerous studies exist discussing the environmental impact of heat pumps quantitatively. Life Cycle Climate Performance (LCCP) is a widely accepted metric and can evaluate the carbon footprint of heat pumps from cradle to grave. LCCP calculation typically requires inputs like annual energy consumption, the material used, refrigerant type, and charge level, which are usually the intermediate or final results from heat pump simulation tools. Thus, a platform linking the heat pump simulation software, local weather conditions, and LCCP evaluation tool can simplify the heat pump design process, especially for complicated systems. This paper presents an LCCP evaluation tool accessing the outputs from the DOE Heat Pump Design Model and the building loads and temperature bin data from AHRI standard 210/240. As for case studies, this paper investigates the LCCP values and life cycle electricity costs (LCEC) of a given cooling-based heat pump with R410A in five typical US cities. In conclusion, this cooling-based heat pump has the highest LCCP value at Chicago and lowest LCCP value at Seattle. In contrast, the heat pump has the highest LCEC at Seattle and the lowest LCEC at Miami. As a future work, a more comprehensive carbon emission and cost analysis of heat pump systems will be conducted through all major cities around the world.

Shen, Bo↗
DOE OSTI2023

Multidisciplinary design optimization in Architecture, Engineering, and Construction: a detailed review and call for collaboration

The design of buildings has become a complex and multidisciplinary problem involving multiple conflicting objectives as architects and designers address competing technical, economic, environmental, and societal concerns. This has been driving research in Architecture, Engineering, and Construction (AEC) toward rigorous multidisciplinary decision-making frameworks that generate and evaluate numerous design alternatives using multi-objective optimization in concert with simulation and analysis models of varying fidelity and computational expense. While such frameworks are well known and widely employed in the aerospace and systems engineering domains, efforts by design professionals and researchers in the AEC field are scattered at best. In this paper, we provide a detailed review of recent developments in optimization frameworks in the AEC field and subsequently highlight how such developments are largely compartmentalized into separate domains such as structural, energy, daylighting, and other performance factors. Herein, we further discuss the technical challenges involved in concurrent coupled multidisciplinary design optimization (MDO) in the AEC field such as interoperability issues between Building Information Modeling (BIM) environments, analysis/simulation tools, and optimization frameworks. We conclude by outlining research needed for more unified modeling and simulation-based optimization frameworks to aid in complex and multidisciplinary building design processes. We also highlight the need for the identification and development of multi-fidelity simulation tools for use across multiple design phases. As such, this paper contributes a novel roadmap to leverage aerospace and systems engineering research in MDO into the field of AEC, along with a call for researchers in the MDO community to seek collaborations in AEC field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Frictional Contacts Between Individual Woody Biomass Particles under Wet and Dry Conditions

Friction between wood particles can be a critical property when designing processing equipment for creating cellulosic biofuels and similar natural products. Also, as wood processing is likely to occur year-round, understanding the friction between wet as well as dry wood particles can help design and model equipment, such as screw feeders. Here, a tribo-rheometry method was developed using a commercial rheometer that measured the torque required to rotate two contacting millimeter-scale wood chip particles. First, a range of normal forces were used to find the limits of the experimental method. Torque values were measured for seven pairs of wood chip particles at 1 N normal force under both dry and wet conditions, at both ambient conditions and in a humidity-controlled chamber. The torque required to rotate the wet particles was consistently higher than that of the dry particles, although differences were not quite statistically significant when measurements were performed at ambient conditions. Using humidity control resulted in torque values for wet particles of 1900 uNm and 900 uNm for dry particles. Finally, the friction coefficient was calculated as 0.23 for wet particles and 0.11 for dry particles when humidity was controlled. These frictions coefficients agreed with values for wood reported in the literature. Overall, controlling humidity is strongly recommended to quantify the range of friction for particles that uptake water, including granular systems from biomass to food to powders.

biomass↗
DOE OSTI2021

Analysis of energetics and economics of sub-ambient hybrid post-combustion carbon dioxide capture

Adsorption of CO 2 from post-combustion flue gas is one of the leading candidates for globally impactful carbon capture systems. In this report, work focused on understanding the opportunities and limitations of sub-ambient CO 2 capture processes utilizing a multistage separation process. A hybrid process design using a combination of pressure-driven separation of CO 2 from flue gas (e.g., adsorption- or membrane-based separation) followed by CO 2 -rich product liquefaction to produce high-purity (>99%) CO 2 at pipeline conditions is considered. The operating pressure of the separation unit is a key cost parameter and also an important process variable that regulates the available heat removal necessary to reach the sub-ambient operating conditions. The economic viability of applying pressure swing adsorption (PSA) processes using fiber sorbent contactors with internal heat management was found to be most influenced by the productivity of the adsorption system, with productivities as high as 0.015 mol CO2 /kg sorb -1 sec -1 being required to reduce costs of capture below $60/ton CO 2 captured. This analysis was carried out using a simplified two-bed process, and thus there is opportunity for further cost reduction with exploration of more complex cycle designs. Three exemplar fiber sorbents (MIL-101(Cr), UiO-66, and zeolite 13X) were considered for application in the sub-ambient process of PSA unit. Among the considered sorbents, zeolite 13X fiber composites were found to perform better at ambient temperatures as compared to sub-ambient. MIL-101(Cr) and UiO-66 fiber composites had improved purity, recovery, and productivity at colder temperatures reducing costs of capture as low as $61/ton CO 2 . Future economic improvement could be achieved by reducing the required operating pressure of the PSA unit and pushing the Pareto frontier closer to the final pipeline requirement via a combination of PSA cycle design and material selection.

42 ENGINEERING↗
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Draft Rules for Alloy 617 Creep-Fatigue Design Using an EPP+SMT Approach

The report describes an improved new creep-fatigue design method for structural components in high temperature nuclear service. The new method uses an integrated elastic-perfectly plastic (EPP) analysis and Simplified Model Test (SMT) approach that greatly simplifies the design process by avoiding the separate evaluation of creep and fatigue damages and eliminating the requirement of stress classification. The report includes draft design rules, presented in a format compatible with an ASME nuclear Code Case, as well as a commentary on the rules and the validation of the rules using pressurized SMT (p-SMT) test data. Areas of potential improvement have been identified for further evaluation. The report also develops EPP+SMT design charts for Alloy 617 at temperatures between 800°C and 950°C by extrapolating high strain range, short hold test data to low strain range and long hold times, typical for structural components in high temperature nuclear service. The design charts are currently limited to 10,000 cycles to avoid excessive extrapolation outside the test database. More robust alternate extrapolation procedures will be considered in future work. The report includes several verification problems comparing the new EPP+SMT design method with current ASME creep-fatigue design methods. The comparison demonstrates that the EPP+SMT method significantly reduces the over-conservatism in current methods. Finally, the report includes a fully-documented sample problem with multiple load cases detailing the application of proposed EPP+SMT design rules.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bench-scale Development of a Transformational Graphene Oxide-based Membrane Process for Post-combustion CO 2 Capture

Graphene-based materials, such as graphene and graphene oxide (GO), have been considered as next-generation membrane materials. GTI Energy and The State University of New York at Buffalo (UB) have been developing a transformational GO-based membrane process (designated as GO2) that integrates a high CO 2 /N 2 selectivity membrane (GO-1) and a high CO 2 flux membrane (GO-2) for post-combustion CO 2 capture. An innovative membrane structure, consisting of GO nanochannels intercalated by single-walled carbon nanotube (SWCNT), was developed. The membrane prepared on hollow fiber substrate showed CO 2 permeance as high as 1,300 GPU with CO 2 /N 2 selectivity >200. The membranes were successfully scaled up to effective area of 50-100 cm 2 . The 50-100 cm 2 membranes showed CO 2 /N 2 selectivity ≥200 and CO 2 permeance ≥1,000 GPU for the GO-1 type, and CO 2 /N 2 selectivity ≥20 and CO 2 permeance ≥2,500 GPU for the GO-2 type. The CO 2 capture performance of the GO-based membranes was tested using a simulated flue gas. The testing results indicate that the GO-based membranes are stable in the presence of flue gas contaminants. The GO-based membranes were then further scaled up to a surface area of 1,000 cm 2 . Good stability was achieved during an integrated testing with GO-1 and GO-2 membranes using simulated flue gas. A bench-scale system was designed, constructed, and tested at the National Carbon Capture Center (NCCC). Good stability was achieved during testing of a single-stage process with >10 shutdowns/startups at NCCC. During the integrated testing, the membranes showed good stability at 50°C and 57°C. 70-90% CO 2 removal efficiencies and ≥95% CO 2 purity were validated during the steady state operation at NCCC. Techno-economic analysis indicates the GO2 membrane-based process technology provides a reduction in both the levelized cost of electricity (LCOE) and cost of capture when compared to the reference B12B case presented in the Cost and Performance Baseline for Fossil Energy Plants Volume 1: Bituminous Coal and Natural Gas to Electricity study prepared by the National Energy Technology Laboratory (NETL), before considering any system optimization or improvement opportunities. The benefits are primarily driven by a reduction in the equipment costs of the CO 2 capture process vs. the solvent-based reference process in NETL Case B12B as well as a decrease in the base plant size.

20 FOSSIL-FUELED POWER PLANTS↗