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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Clostridium autoethanogenum alters cofactor synthesis, redox metabolism, and lysine-acetylation in response to elevated H 2 :CO feedstock ratios for enhancing carbon capture efficiency

Clostridium autoethanogenum is an acetogenic bacterium that autotrophically converts carbon monoxide (CO) and carbon dioxide (CO 2 ) gases into bioproducts and fuels via the Wood–Ljungdahl pathway (WLP). To facilitate overall carbon capture efficiency, the reaction stoichiometry requires supplementation of hydrogen at an increased ratio of H 2 :CO to maximize CO 2 utilization; however, the molecular details and thus the ability to understand the mechanism of this supplementation are largely unknown. In order to elucidate the microbial physiology and fermentation where at least 75% of the carbon in ethanol comes from CO 2 , we established controlled chemostats that facilitated a novel and high (11:1) H 2 :CO uptake ratio. We compared and contrasted proteomic and metabolomics profiles to replicate continuous stirred tank reactors (CSTRs) at the same growth rate from a lower (5:1) H 2 :CO condition where ~ 50% of the carbon in ethanol is derived from CO 2 . Our hypothesis was that major changes would be observed in the hydrogenases and/or redox-related proteins and the WLP to compensate for the elevated hydrogen feed gas. Our analyses did reveal protein abundance differences between the two conditions largely related to reduction–oxidation (redox) pathways and cofactor biosynthesis, but the changes were more minor than we would have expected. While the Wood–Ljungdahl pathway proteins remained consistent across the conditions, other post-translational regulatory processes, such as lysine-acetylation, were observed and appeared to be more important for fine-tuning this carbon metabolism pathway. Metabolomic analyses showed that the increase in H 2 :CO ratio drives the organism to higher carbon dioxide utilization resulting in lower carbon storages and accumulated fatty acid metabolite levels. This research delves into the intricate dynamics of carbon fixation in C. autoethanogenum, examining the influence of highly elevated H 2 :CO ratios on metabolic processes and product outcomes. The study underscores the significance of optimizing gas feed composition for enhanced industrial efficiency, shedding light on potential mechanisms, such as post-translational modifications (PTMs), to fine-tune enzymatic activities and improve desired product yields.

09 BIOMASS FUELS↗
DOE OSTI2024

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Demonstration of Novel Monitoring Techniques for a North Dakota Carbon Capture and Storage Project

Conference paper presented at 16th International Conference on Greenhouse Gas Control Technologies (GHGT-16), Lyon, France, October 23–27, 2022. We present an overview and initial results of the novel and sustainable monitoring techniques developed by the Energy & Environmental Research Center (EERC) and its commercial partners associated with the Red Trail Energy (RTE) carbon capture and storage (CCS) project. RTE is an investor-owned 64-million-gallon dry mill ethanol production plant in Richardton, North Dakota, USA. Currently in operation as of June 2022, the RTE facility will capture 180,000 tonnes of carbon dioxide (CO 2 ) annually (>99% CO 2 ) that is otherwise emitted during the ethanol production process and inject it into the Broom Creek Formation, a deep saline aquifer, for permanent on-site geologic storage.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Natural Gas Combined Cycle Carbon Capture Retrofit Database

This tool provides high-level analysis on the incremental costs for retrofitting natural gas combined cycle plants with carbon dioxide capture and/or compression systems. Options are available to include costs of other technological improvements that would be required to comply with various environmental regulations when installing carbon capture technology. This model is based on the NETL report "Cost and Performance of Retrofitting NGCC Units for Carbon Capture" (2023).

03 NATURAL GAS↗
DOE OSTI2026

Wyoming Carbon Blueprint A Roadmap for Statewide Carbon Capture and Storage

Wyoming stands at a pivotal point in defining its energy future, and the Wyoming Carbon Blueprint positions the state to lead the nation in carbon management through a coordinated buildout of carbon capture, transportation, utilization, and geologic storage. Building on Wyoming’s long history as a major producer of coal, oil, and natural gas, and supported by favorable policies, skilled labor, and robust subsurface resources, the Blueprint outlines a pathway to develop one of the world’s first open-access carbon hubs capable of capturing between 10 and 25 million tonnes of CO₂ per year within five years, and up to 45 million tonnes within a decade. In doing so, it advances the concept of “No Carbon Left Behind”: a statewide strategy that prevents stranded CO₂ by aligning capture, transport, and storage systems into a single coherent infrastructure network.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrated carbon capture and conversion: A review on C 2+ product mechanisms and mechanism-guided strategies

The need to reduce atmospheric CO 2 concentrations necessitates CO 2 capture technologies for conversion into stable products or long-term storage. A single pot solution that simultaneously captures and converts CO 2 could minimize additional costs and energy demands associated with CO 2 transport, compression, and transient storage. While a variety of reduction products exist, currently, only conversion to C 2+ products including ethanol and ethylene are economically advantageous. Cu-based catalysts have the best-known performance for CO 2 electroreduction to C 2+ products. Metal Organic Frameworks (MOFs) are touted for their carbon capture capacity. Thus, integrated Cu-based MOFs could be an ideal candidate for the one-pot capture and conversion. In this paper, we review Cu-based MOFs and MOF derivatives that have been used to synthesize C 2+ products with the objective of understanding the mechanisms that enable synergistic capture and conversion. Furthermore, we discuss strategies based on the mechanistic insights that can be used to further enhance production. Finally, we discuss some of the challenges hindering widespread use of Cu-based MOFs and MOF derivatives along with possible solutions to overcome the challenges.

42 ENGINEERING↗
DOE OSTI2024

Jacobian-based Model Diagnostics and Application to Equation Oriented Modeling of a Carbon Capture System

Equation-oriented (EO) modeling has the potential to enable the effective design and optimization of the operation of advanced energy systems. However, advanced modeling of energy systems results in a large number of variables and non-linear equations, and it can be difficult to search through these to identify the culprit(s) responsible for convergence issues. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms so they can be rescaled. A further singular value decomposition can be per-formed to identify degenerate sets of equations and remaining scaling issues. This work presents an EO model of a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. The IDAES diagnostics tools were successfully applied to this flowsheet to identify problems to improve model robustness and enable the optimization of process design and operating conditions of a carbon capture system.

Allan, Douglas↗
DOE OSTI2023

Incorporation of market signals for the optimal design of post combustion carbon capture systems

Recent studies have shown that fossil generators equipped with post-combustion carbon capture (PCC) systems are needed to reduce the cost of deep decarbonization. Such generators need to be flexible and responsive to grid conditions, particularly in a high variable renewable energy (VRE) environment. In this work, we evaluate the net present value (NPV) of retrofitting an existing natural gas combined cycle (NGCC) unit with a flexible PCC system while incorporating market signals from a high VRE grid. We use our industrial partner’s NGCC configuration as representative of existing NGCC units and Svante’s rapid-temperature swing adsorption (TSA) for PCC. Because of its ability to rapidly startup/shutdown and ramp-up/ramp-down, the chosen capture technology is very attractive for load-following operations. For a given set of market signals, we formulate a two-stage stochastic multi-period optimization problem, under the price-taker assumption, to simultaneously optimize the design of the capture system and operation of the entire plant. Rigorous models for the NGCC unit, PCC system, and compression system are developed using commercial process simulators and validated with either plant or vendor data. For computational tractability, we develop surrogate/reduced-order models for use in the optimization problem. The surrogate model for the NGCC plant is constructed by linearizing the rigorous dynamic model at 75% load, while data-driven nonlinear surrogate models for the capture and compression systems are constructed using simulation data from the rigorous models. The optimization problem, formulated as a mixed integer bilinear program, is implemented in the IDAES® integrated platform and solved to global optimality using Gurobi 9.5. Using this formulation, we determine the profitability of retrofitting an existing NGCC unit with the chosen capture system for multiple regions in the U.S. under two scenarios with different carbon prices. Importantly, the results show that the optimal decision strongly depends on the region and on the carbon price, thereby demonstrating the importance of the inclusion of market signals in the design process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Current Developments of Carbon Capture Storage and/or Utilization–Looking for Net-Zero Emissions Defined in the Paris Agreement

An essential line of worldwide research towards a sustainable energy future is the materials and processes for carbon dioxide capture and storage. Energy from fossil fuels combustion always generates carbon dioxide, leading to a considerable environmental concern with the values of CO2 produced in the world. The increase in emissions leads to a significant challenge in reducing the quantity of this gas in the atmosphere. Many research areas are involved solving this problem, such as process engineering, materials science, chemistry, waste management, and politics and public engagement. To decrease this problem, green and efficient solutions have been extensively studied, such as Carbon Capture Utilization and Storage (CCUS) processes. In 2015, the Paris Agreement was established, wherein the global temperature increase limit of 1.5 °C above pre-industrial levels was defined as maximum. To achieve this goal, a global balance between anthropogenic emissions and capture of greenhouse gases in the second half of the 21st century is imperative, i.e., net-zero emissions. Several projects and strategies have been implemented in the existing systems and facilities for greenhouse gas reduction, and new processes have been studied. This review starts with the current data of CO2 emissions to understand the need for drastic reduction. After that, the study reviews the recent progress of CCUS facilities and the implementation of climate-positive solutions, such as Bioenergy with Carbon Capture and Storage and Direct Air Capture. Future changes in industrial processes are also discussed.

Regufe, Maria João (ORCID:0000000327748302)↗
DOE OSTI2024

CFD and systems engineering to minimize membrane-based carbon capture costs

In this study, we propose a superstructure for membrane-based carbon capture, addressing these challenges through the formulation of an optimization problem. The case study considers a flue gas with a flowrate of 1000 mol/s and a 30 % CO2 (molar basis), which is representative of steel plants. Reduced models, derived from rigorous CFD simulations of membrane modules, are utilized to enhance the efficiency of the optimization process. The reduced model includes specific input variables such as inlet flow rate, retentate and permeate pressures, and inlet CO2 concentration; while the CO2 recovery and purity in the permeate side are considered as output variables. In this way, we exploit the information from the CFD simulations in the superstructure, aiding in the selection of the optimal configuration for the multi-stage membrane process. The results underscore the efficiency of a multi-stage membrane design, showing capture costs around 45 $/t-CO2.

Pedrozo, Hector A.↗
DOE OSTI2022

Large-Scale Commercial Carbon Capture Retrofit of the San Juan Generating Station

Enchant Energy, L.L.C. (Enchant) was selected to conduct a Front-End Engineering Design study (FEED) for the addition of a full-scale carbon capture system to remove carbon dioxide from the flue gas emissions of the two coal-fired generating units (total of 847 MW net) at San Juan Generating Station (SJGS), using Mitsubishi Heavy Industries Americas (MHIA) KM CDR Process™. Sargent & Lundy LLC (S&L) was selected as the primary technical lead, and along with various other organizations, were able to support Enchant’s completion of the following FEED tasks: Task 1 – Project Management & Planning; Task 2 – FEED Study; Task 3 - Final FEED Study Package. The FEED study period was between October 15, 2019 to September 30, 2022.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Optimizing Carbon Capture, Transport, and Storage: Overcoming Challenges with Machine Learning and Cost-Benefit Analysis

Carbon Capture, Utilization, and Storage (CCUS) is a critical strategy for reducing CO₂ emissions and mitigating climate change. However, its widespread deployment faces numerous challenges across the capture, transport, and storage phases. These challenges include the technical complexity of predicting subsurface behaviors during CO₂ injection, ensuring long-term storage integrity, optimizing transportation networks, and balancing the economic and environmental trade-offs. Addressing these issues requires an integrated approach combining advanced subsurface modeling with system-level analyses to assess costs, risks, and benefits. This presentation provides an overview of studies conducted by the National Energy Technology Laboratory (NETL) to tackle these challenges. NETL’s efforts encompass cutting-edge research in subsurface fluid behavior machine learning predictions, alongside the development of innovative tools for system optimization and economic evaluation. By bridging technical expertise and strategic analysis, NETL aims to advance the deployment of CCUS technologies to support global decarbonization efforts. Presented at the Carnegie Mellon University CEE IESS Student Seminar October 4, 2024.

Shih, Chung Yan↗