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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.

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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Targeted electrochemical reduction of carcinogenic N -nitrosamines from emission control systems within CO 2 capture plants

N-Nitrosamines are one of the environmentally significant byproducts from aqueous amine-based post-combustion carbon capture systems (CCS) due to their potential risk to human health. Safely mitigating nitrosamines before they are emitted from these CO 2 capture systems is therefore a key concern before widescale deployment of CCS can be used to address worldwide decarbonization goals. Electrochemical decomposition is one viable route to neutralize these harmful compounds. The circulating emission control waterwash system, commonly installed at the end of the flue gas treatment trains to minimize amine solvent emissions, plays an important role to capture N-nitrosamines and control their emission into the environment. The waterwash solution is the last point where these compounds can be properly neutralized before becoming an environmental hazard. In this study, the decomposition mechanisms of N-nitrosamines in a simulated CCS waterwash with residual alkanolamines was investigated using several laboratory-scale electrolyzers utilizing carbon xerogel (CX) electrodes. Hcell experiments revealed that N-nitrosamines were decomposed through a reduction reaction and are converted into their corresponding secondary amines thereby neutralizing their environmental impact. Batch-cell experiments statistically examined the kinetic models of N-nitrosamine removal by a combined adsorption and decomposition processes. The cathodic reduction of the N-nitrosamines statistically obeyed the first-order reaction model. Finally, a prototype flow-through reactor using an authentic waterwash was used to successfully target and decompose N-nitrosamines to below the detectable level without degrading the amine solvent compounds allowing them to be return to the CCS and lower the system operating costs. Furthermore, the developed electrolyzer was able to efficiently remove greater than 98% of N-nitrosamines from the waterwash solution without producing any additional environmentally harmful compounds and offers an effective and safe route to mitigate these compounds from CO 2 capture systems.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO 2 capture process from a baseload coal-fired power plant under cycling conditions. A Tchebycheff-based method is used for the multi-objective optimization (MOO) component. Also, a hybrid approach consisting of Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) is implemented for the first time in process systems engineering to solve the dynamic real-time optimization (DRTO) component. The objectives considered in the MOO-DRTO framework are economic and environmental. The proposed MOO-DRTO strategy is successfully implemented and 24-h optimal output trajectories for the carbon capture system under cycling are generated. Also, the optimal compromise is chosen from the Pareto front according to a set of selected weights for the objectives with minimal interaction between the framework and decision maker. The results indicated that the developed framework has potential to be extended to plant-wide optimization applications under cycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

SimH 2 : an integrated techno-economic modeling framework for hydrogen pipeline infrastructure and network optimization

Large-scale hydrogen (H 2 ) pipeline transport design and network optimization have seldom been reported due to the lack of a cost model accounting for the relationship between transport cost and hydrogen mass flow rate. Here, this work introduced a system-level cost model for hydrogen pipeline transport at supercritical state and integrated it with an existing CO 2 pipeline network tool, SimCCS, for hydrogen-specific pipeline design and optimization. The Intermountain West (I-West) region of the U.S., historically dependent on fossil fuel-based economies, is chosen to demonstrate the capabilities of our H 2 pipeline cost model and transport network optimization platform called SimH 2 . Two scenarios are examined: one where the pipeline is not allowed to pass through disadvantaged communities and the other where it is permitted. The results highlight that incorporating disadvantaged-community constraints lead to longer pipeline routes and increased transport costs, reflecting the trade-offs involved in equitable infrastructure development. It is demonstrated that the newly developed SimH 2 tool not only enables the efficient design of H 2 transportation pipelines but also optimizes the network by accounting for local terrain and the presence of disadvantaged areas.

08 HYDROGEN↗
DOE OSTI2020

A data-driven model for thermodynamic properties of a steam generator under cycling operation

The varying electricity demand from coal power plants due to the intermittent nature of renewable sources leads to load-follow and on/off operations referred to as cycling. Cycling causes transients of properties such as pressure and temperature within various components of the steam generation system.These transients cause increased damage because of fatigue and creep-fatigue interactions shortening the life of components. An algorithm is developed to identify cycling operations from the gross power data. The data-driven model based on artificial neural networks (ANN) is developed using 10 years data from Coal Creek Station power plant located in North Dakota, USA to estimate properties of the steam generator components during cycling operations. Furthermore, the uniqueness of this model is the ability to predict component properties for the cycling as well as base-load operations and is reported for the first time. The ANN model estimates the component properties, for a given gross power profile and initial conditions, as they vary during cycling operations. As a representative example, the ANN estimates are presented for the superheater outlet pressure, reheater inlet temperature, and flue gas temperature at the air heater inlet. The changes in these variables as a function of the gross power over the time duration are compared with measurements to assess the predictive capability of the model. Mean square errors of 4.49E-04 for superheater outlet pressure, 1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature at the air heater inlet were observed.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Data analytics for leak detection in a subcritical boiler

For decades, boiler leaks have been the leading cause of forced outages in the coal-fired unit. The leak occurrences are currently escalating since the existing plants must satisfy faster-ramping rates to support grid operation. Data analytics including Principal Component Analysis, Canonical Variate, and Fisher Discriminant Analysis were combined for detecting and characterizing the leak in a commercial 650 MW subcritical coal-fired power plant. The combined approach was shown to be highly effective in the fault investigation that would not have been easily achieved by an individual technique. The variability in both training and validation datasets was first evaluated using PCA. Then, the CV-FDA was employed to discriminate among faults, and to categorize the processed data into two main groups: no-leak (0) and leak (1), providing the timeframe and location of the leak occurrence. Furthermore, about 8,014 observations from 81 process variables were initially included in the calculation, while the variable count was reduced to 4 with less than 1% misclassification rate in total observations. Finally, the leak was isolated in the waterwall section. Thus, the outcome of this research may provide early detection and isolation of faulty operations in the coal-fired power plant that involves a considerable number of process variables.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Development of a health monitoring framework: Application to a supercritical pulverized coal-fired boiler

In this study, this work details the development of a physics-based equipment health monitoring framework for a supercritical boiler, using first-principles models to estimate the remaining useful life (RUL) of its components. The framework accounts for fatigue and creep life consumption, generating spatio-temporal variations in mechanical and thermal stress. Analysis of the stress profile throughout the boiler highlights the finishing superheater inlet steam header as a vulnerable location susceptible to damage from cycling operation. The framework also yields quantified uncertainty in the RUL projection for specific locations, accounting for uncertainties in material properties and boiler operation. Results indicate that operational uncertainties (e.g., seasonal variation and operational strategy) and material properties (e.g., rupture time coefficients, Young’s modulus, yield strength, and coefficient of thermal expansion) significantly impact the RUL of the finishing superheater inlet steam header. Additionally, case studies demonstrate the use of the health monitoring framework as a predictive tool for operational planning under uncertainty, including scenarios with and without updates on the operation of the boiler.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Advanced defrosting techniques in air source heat pumps: A review of vapor injection, thermal energy storage, and experimental frost accumulation data

Electrification is a critical step for reducing greenhouse gas emissions from heating. Air source heat pumps (ASHPs) are a promising alternative to fossil fuel-based systems due to their high coefficients of performance (COP), dual heating and cooling capability, and lower carbon footprint. However, for ASHPs to achieve widespread adoption, they must operate reliably across all climates, including cold regions. Additionally, defrosting techniques should be energy efficient and minimally disruptive to indoor comfort. Vapor injection (VI) technology can address the high-pressure and high-temperature lift challenges encountered in low ambient conditions. More recently, in addition to enhancing heating performance, VI has also been shown to improve the speed and efficiency of reverse cycle defrosting. Likewise, thermal energy storage (TES) has steadily gained attention for its ability to serve as an auxiliary heat source during both normal operation and defrosting. This review analyzes the benefits and limitations of VI- and TES-assisted defrosting approaches. While both technologies show strong potential individually, no studies to date have explored their combined use in ASHP systems. Additionally, to support continued development of defrosting strategies, both in modeling and experimental work, it is critical to establish frost accumulation data under a range of operating conditions. By compiling the available data from the literature, this paper also highlights the limited availability of such experimental data and the wide variation in frosting and defrosting durations and termination criteria, which are often influenced by system design and test setups.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Economic viability of using thermal energy storage for flexible carbon capture on natural gas power plants

Fossil fuel-based power plants generate 80% of the electricity in the United States and provide a reliable generation source for both base and peak power demands. These plants are expected to adapt to changes in environmental policies that will require carbon management with carbon capture and storage (CCS) representing a possible solution. Current solvent-based CCS has a detrimental impact on a power plant's performance due to large heat loads required for carbon capture solvent regeneration. This parasitic load restricts the power plant's output and operation flexibility. Therefore, this study evaluates the feasibility of using thermal storage technologies for natural gas combined cycle (NGCC) power plants coupled with CCS to minimize the impact of solvent regeneration and enable the plant to operate at peak power output. Thermal storage can minimize the impact of CCS on the power plant by providing the heat load required for solvent regeneration during times of peak demand which will allow the plant to operate unrestricted and at full power. In total, fifteen unique thermal storage configurations were evaluated from three thermal storage categories: Brayton cycle heat pump, vapor compression heat pump, and heat recovery steam generator steam extraction for storage. The viability of these systems was determined by evaluating each configuration on thousands of real-world Locational Marginal Pricing (LMP) profiles from the New York Independent System Operator and California Independent System Operator electricity markets using a techno-economic analysis. Afterwards, results were compared to the performance of a base power plant (NGCC with CCS and no thermal storage) to determine the impact of thermal storage on power plant economics. Overall, six of the thermal storage configurations performed better than base CCS enabled power plant on between 11.5% and 38.7% of the LMP signals evaluated. The best performing configuration was a vapor compression heat pump that used flue gas as the working fluid and had both hot and cold thermal storage units. This configuration performed better than the base CCS power plant on 38.7% of the LMP profiles. The results of this study show thermal storage can mitigate the economic impact of carbon capture solvent regeneration on NGCC power plants. Discussion focuses on the impact of electricity pricing on the optimal thermal storage system, the advantages and disadvantages of the systems evaluated, and identifies limitations with the study.

25 ENERGY STORAGE↗
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2025

Multi-scale dynamic modeling and validation of radial flow fixed bed contactors for post-combustion CO 2 capture using bench scale and pilot plant data

Here, in this work, a multi-scale model of a radial flow fixed bed contactor packed with a carbon sorbent is developed and validated with laboratory-scale and pilot plant scale dynamic data. For the lab scale system, the model results were compared with low, medium and high gas and sweep flowrates, yielding root mean square error (RMSE) of 0.80, 0.63, 0.96 CO 2 mol%, respectively, for the outlet CO 2 concentration profile considering the entire A-D cycle. For the bed outer temperature profile, maximum RMSE was found to be 5.5 °C considering all flowrates and entire A-D cycles. An experimental campaign was developed and applied to a pilot plant at Technology Center Mongstad (TCM), Norway. Approaches were developed for pre-processing of data including consideration of the effect of gas mixing, measurement delay, and determination of cyclic steady-state conditions. Considering profiles during A-D cycles for all test runs, it was found that the maximum RMSE for pressure drop, temperature for the outer section of the bed, temperature for the middle section of the bed, and outlet CO 2 concentration profile remained less than 1.5 mbar, 3.5 °C, 2.8 °C, and 1.3 CO 2 mol%, respectively. The validated model was used to perform sensitivity studies on several key design operating variables for the adsorption-desorption cycle. It was found that the flow rate and concentration of flue gas have dominant nonlinear effects on the breakthrough time while the desorption time was strongly affected by the sweep gas flowrate for the specific sorbent being evaluated in this study.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Microwave-assisted ammonia decomposition over metal nitride catalysts at low temperatures

The negative environmental impact of fossil fuel-based energy systems has unveiled the need to develop a CO x -free sustainable hydrogen (H 2 ) economy. Employing a microwave-assisted route, a ternary metal nitride catalyst (i.e., Co 2 Mo 3 N), and a low-temperature-pressure NH 3 decomposition process, this study investigated the possibility of developing a distributed H 2 production process. Here, this study not only explored lower cost-based catalyst systems but also the use of a microwave reactor to increase the energy efficiency of the process. Results from catalytic NH 3 decomposition experiments, performed in microwave reactors on Co 2 Mo 3 N catalyst, demonstrated the peak energy efficiency (of ~0.006 kgH 2 /kWh) at 400 °C in ambient pressure (with an NH 3 conversion >90%) which was around ninety times (~90 x) more efficient than a conventional system. Activation energy calculation also displayed a 20% less energy requirement for the microwave-based process (~31 kJ mol -1 ) than the conventional system (~37 kJ mol -1 ), indicating the advantage of the microwave-based process. Further microwave-assisted catalytic measurements and characterization of the Co 2 Mo 3 N catalyst, using x-ray diffraction (XRD) technique and scanning electron microscopic (SEM) images, revealed the excellent stability of this material at its peak performance (at 400 °C) and illustrated the potential of using this catalyst for a sustainable, economic, and energy-efficient approach for producing CO x -free H 2 .

08 HYDROGEN↗
DOE OSTI2022

A multi-product landscape life-cycle assessment approach for evaluating local climate mitigation potential

Increasing demand for land-based climate mitigation requires more efficient management of agricultural landscapes for competing objectives. Here we develop methods for assessing trade-offs and synergies between intensification and carbon-sequestering conservation measures in annual crop production landscapes using the DayCent ecosystem model and the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) life-cycle assessment (LCA) model. We compiled county-scaled crop yields, fertilizer application rates, and tillage intensity for a corn–soybean farming case study landscape in the US state of Iowa. Using DayCent, we estimated a baseline soil organic carbon (SOC) accrual rate of 0.29 Mg C ha -1 y -1 driven by historical increases in crop productivity and reductions in tillage intensity. We then simulated the effects of management interventions targeted toward intensification (stover removal) and SOC sequestration (tillage intensity reduction and winter cover crop addition) individually and in combination. We propose a new multi-product landscape–LCA approach that analyzes marginal changes in corn grain, corn stover, and soybean production from the landscape in terms of their value for biofuel production (corn ethanol, soy biodiesel, and cellulosic ethanol from stover) and associated net displacement of conventional fossil-derived fuel use. This enables us to evaluate both intensification and sequestration effects in common CO 2 -equivalent mitigation units. We also used DayCent-simulated yields under the different land management scenarios to estimate farm-level costs and revenues. Our results show that intensification via collecting 30% of corn stover for biofuel production would increase the total greenhouse gas (GHG) mitigation potential of this landscape by 0.93 Mg CO 2 e ha -1 y -1 and provide $49 ha -1 y -1 of additional net revenue from biomass sales, but would reduce the baseline SOC accumulation rate by approximately 40%. In contrast, integrated approaches that include co-adoption of winter cover cropping and/or tillage intensity reduction would result in increased rates of SOC accumulation above the baseline, achieving simultaneous improvements in both farm profits and the overall GHG mitigation potential of the landscape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Estimation of abatement potentials and costs of air pollution emissions in China

Understanding the air pollution emission abatement potential and associated control cost is a prerequisite to design cost ef?cient control policies. In this study, a linear programming algorithm model, International Control Cost Estimate Tool, was updated with cost data for applications of 56 types of end-of-pipe technologies and ?ve types of renewable energy in 10 major sectors namely power generation, industry combustion, cement pro-duction, iron and steel production, other industry processes, domestic combustion, transportation, solvent use, livestock rearing, and fertilizer use. The updated model was implemented to estimate the abatement potential and marginal cost of multiple pollutants in China. The total maximum abatement potentials of sulfur dioxide (SO2), nitrogen oxides (NOx), primary particulate matter (PM2.5), non-volatile organic compounds (NMVOCs), and ammonia (NH3) in China were estimated to be 19.2, 20.8, 9.1, 17.2 and 8.6 Mt, respectively, which accounted for 89.7%, 89.9%, 94.6%, 74.0%, and 80.2% of their total emissions in 2014, respectively. The associated control cost of such reductions was estimated as 92.5, 469.7, 75.7, 449.0, and 361.8 billion CNY in SO2, NOx, primary PM2.5, NMVOCs and NH3, respectively. Shandong, Jiangsu, Henan, Zhejiang, and Guangdong provinces exhibited large abatement potentials for all pollutants. Provincial disparity analysis shows that high GDP regions tend to have higher reduction potential and total abatement costs. End-of-pipe technologies tended be a cost-ef?cient way to control pollution in industries processes (i.e., cement plants, iron and steel plants, lime production, building ceramic production, glass and brick production), whereas such technologies were less cost- effective in fossil fuel-related sectors (i.e., power plants, industry combustion, domestic combustion, and transportation) compared with renewable energy. The abatement potentials and marginal abatement cost curves developed in this study can further be used as a crucial component in an integrated model to design optimized cost-ef?cient control policies.

Zhang, Fenfen↗
DOE OSTI2021

Influence of TiO 2 on the densification behaviour of Yb 2 O 3

The effect of temperature and heating rate on the densification of ytterbia (Yb 2 O 3 ), with and without titania (TiO 2 ) doping was investigated. It is shown that up to a certain doping level, titania doping enhances the densification behaviour of ytterbia. The effect of titania doping on crystal structure confirms that titania is substitutionally incorporated in ytterbia up to the solubility limit, which corresponds well with the densification results. The increased densification rate of titania-doped ytterbia is attributed to the formation of cation vacancy and lattice distortion. Using constant heating rate experiments, the activation energy for densification has been calculated and it is shown that in the intermediate density range (60% to 85%), the activation energy is independent of the density. Furthermore, titania doping increases the activation energy for densification.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Exploring the impact of multivalent substitution on high-temperature electrical conductivity in doped lanthanum chromite perovskites: An experimental and ab-initio study

Doped LaCrO 3 perovskites hold promise as robust materials for electrical interconnects and sensor applications in harsh environments. In this work, we investigated the high-temperature behavior of La 1–x Sr x CrO 3 , La 1–x Ca x CrO 3 and La 0.8 Sr 0.2 Cr 1-x Mn x O3 (0.1 ≤ x ≤ 0.4) through a combination of computational modeling and physical characterization up to 1500 °C. Crystalline structural properties were determined and compared with ab-initio calculations, which demonstrated excellent agreement with experimental findings. High-temperature electrical conductivity measurements were performed under different atmospheres. Calcium and strontium/manganese co-doped lanthanum chromites exhibited typical semiconductor exponential trends and conductivity showed proportional correlation with substitutions levels up to 30 %. The DFT modelling was completed up to 1500 °C, including low and high temperature chromite phases and oxygen vacancies insertion. Calculations were correlated with experimental electrical properties. This work expands the understanding of doped lanthanum chromites and paves the way for the development of materials suitable for demanding high-temperature applications.

20 FOSSIL-FUELED POWER PLANTS↗