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At least 289 records · Page 16

Ubiquity and functional uniformity in CO2 concentrating mechanisms in multiple phyla of Bacteria is suggested by a diversity and prevalence of genes encoding candidate dissolved inorganic carbon transporters

ABSTRACT Autotrophic microorganisms catalyze the entry of dissolved inorganic carbon (DIC; = CO2 + HCO3− + CO32−) into the biological component of the global carbon cycle, despite dramatic differences in DIC abundance and composition in their sometimes extreme environments. “Cyanobacteria” are known to have CO2 concentrating mechanisms (CCMs) to facilitate growth under low CO2 conditions. These CCMs consist of carboxysomes, containing enzymes ribulose 1,5-bisphosphate oxygenase and carbonic anhydrase, partnered to DIC transporters. CCMs and their DIC transporters have been studied in a handful of other prokaryotes, but it was not known how common CCMs were beyond “Cyanobacteria”. Since it had previously been noted that genes encoding potential transporters were found neighboring carboxysome loci, α-carboxysome loci were gathered from bacterial genomes, and potential transporter genes neighboring these loci are described here. Members of transporter families whose members all transport DIC (CHC, MDT and Sbt) were common in these neighborhoods, as were members of the SulP transporter family, many of which transport DIC. 109 of 115 taxa with carboxysome loci have some form of DIC transporter encoded in their genomes, suggesting that CCMs consisting of carboxysomes and DIC transporters are widespread not only among “Cyanobacteria”, but also among members of “Proteobacteria” and “Actinobacteria”.

Scott, Kathleen M.↗

Modeling of Supercritical CO2 Shell-and-Tube Heat Exchangers Under Extreme Conditions: Part II: Heat Exchanger Model

Abstract Heat exchangers play a critical role in supercritical CO2 Brayton cycles by providing necessary waste heat recovery. Supercritical CO2 thermal cycles potentially achieve higher energy density and thermal efficiency operating at elevated temperatures and pressures. Accurate and computationally efficient estimation of heat exchanger performance metrics at these conditions is important for the design and optimization of sCO2 systems and thermal cycles. In this paper (Part II), a computationally efficient and accurate numerical model is developed to predict the performance of shell-and-tube heat exchangers (STHXs). Highly accurate correlations reported in Part I of this study are utilized to improve the accuracy of performance predictions, and the concept of volume averaging is used to abstract the geometry and reduce computation time. The numerical model is validated by comparison with computational fluid dynamics (CFD) simulations and provides high accuracy and significantly lower computation time compared to existing numerical models. A preliminary optimization study is conducted and the advantage of using supercritical CO2 as a working fluid for energy systems is demonstrated.

Engineering↗

Multimodal CO2 Transportation Cost Model

This model provides a cost estimate for transporting CO2 via truck or rail in the United States using commercially available equipment and technologies. The model includes an analysis of direct and indirect CO2 emissions to determine costs per net tonne of CO2 transported. Publicly available data and methods published in the peer-reviewed literature are used to the extent possible; references are available at the bottom of the "Calculations" sheet. Upstream (i.e., liquefaction, buffer storage) and downstream (i.e., buffer storage, reconditioning) activities are included in the model of emissions and costs. All capital and operating expenditures are estimated in the "Calculations" sheet. The cost of financing the project is determined in the "FINEX" sheet. All user inputs are done via drop down menus in on the "User Interface" sheet. Summary results are also provided on the "User Interface" sheet.

Myers, CoreyA↗

Levelized Cost of Avoided CO2 tool (LCAC tool) v0.9

The levelized cost of avoided CO2 (LCAC) tool facilitates techno-economic calculations for various decarbonization technologies. It comprises two components to accomplish this: an input workbook based on MS Excel and a web-based output tool. The workbook acts as the input sheet, using the user-provided decarbonization measures and their impacts on lifetime energy consumption, energy costs, and CO2 emissions to compute LCAC (expressed in $/metric ton of avoided CO2). The workbook is uploaded to a web-based tool to generate an LCAC curve. This graphical representation illustrates the economic comparison of all decarbonization measures for purposes such as implementation prioritization. We are unaware of any publicly available tools with similar capabilities.

Ali Qamar, Muhammad↗

The feasibility of MT tipper data to monitor CO2 storage sites

Monitoring carbon storage sites using geophysical techniques is a critical component to the success and safety of storage programs. Currently, the primary methods of monitoring such sites are seismic and, to a much lesser extent, electromagnetics using active sources. The cost of such methods, especially seismic, can be prohibitively expensive. Natural source lectromagnetics, or magnetotellurics (MT), represents a low-cost, and underutilized method with the potential to aid CO2 monitoring efforts. Specifically, the tipper of MT data gives insight to the dimensionality of the subsurface and is able to detect the expansion front of a CO2 plume in a saline reservoir. We analyze the feasibility of using the tipper to monitor two shallow CO2 plumes and conclude that the tipper may be a suitable method for long-term monitoring.<br>

Kohnke, Colton↗

Stable isotopes of carbon dioxide (CO2), dissolved inorganic carbon (DIC), water and concentrations of greenhouse gases in a mountainous watershed (Crested Butte, Colorado)

Temperature and precipitation variability in response to climate change affects water cycling of watersheds and can potentially impact water quality, water availability, elemental and molecular fluxes, and biogeochemical processes. Here we report the measurements of light stable isotopes and greenhouse gases analysis to a large multidisciplinary project addressing watershed function. Water hydrogen (H) and oxygen (O) stable isotopes of snow and water saturated soil were measured to constrain source contributions to groundwater and streamflow. In an effort to better constrain existing nitrogen and carbon cycling models in high-altitude ecosystems, carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) greenhouse gases concentrations and stable isotope signatures of CO2, dissolved inorganic carbon (DIC), and water were measured. This dataset includes one CSV file and one Excel file containing data including stable isotopes of carbon dioxide (CO2), dissolved inorganic carbon (DIC), water and concentrations of greenhouse gases. The CSV and Excel files contain the same information/data but have included both versions for accessibility.

54 ENVIRONMENTAL SCIENCES↗

Carbon dioxide (CO2) flux, amplicon sequencing and liquid chromatography–mass spectrometry (LC-MS) data for pyrogenic organic matter (PYOM) amended soil incubations in lab, Madison, WI, 2021-22

This dataset comprises soil-pyrogenic organic matter (PyOM) incubation experiments conducted in a laboratory setting. The focus was on measuring carbon dioxide (CO2) flux emitted in the headspace of incubation jars over a one-month period. The aim was to estimate carbon mineralization from two different carbon fractions in PyOM, as well as carbon mineralization from soil organic carbon (SOC). Bacterial and fungal community profiles were tracked at various time points during the incubation to observe changes in the overall community and specific responders to PyOM. LC-MS analysis of soil samples was also performed to monitor changes in the soil's chemical composition.The provided dataset includes processed CO2 flux measurements obtained from the Picarro-multiplexer measurement setup for each sample incubation jar. This data is available in the "CO2Flux.csv" file. Additionally, the dataset includes CO2 flux data partitioned to estimate mean carbon mineralization from the two PyOM carbon fractions, bulk PyOM, and SOC. The cumulative carbon mineralization data is provided in "cml_respired.csv," while the rate of carbon mineralization data is in "rate_respired.csv." The file "cml_respired_lastcyc.csv" contains cumulative carbon mineralization data for the PyOM carbon fractions, bulk PyOM, and SOC throughout the entire incubation period.For the LC-MS analysis, the raw data can be found in the "20220721_NZ_PeakHeights_FinalAnalysis.csv" file, while sample metadata is provided in "20220721_NZpilot_Metadata.csv". Processed LC-MS data is available in the file "20220721_NZ_FINAL_msdat_sub3.csv". Lastly, the dataset includes the relative abundance data for bacterial genera that exhibited a significant positive response to the addition of PyOM produced at 350 degrees Celsius, which can be found in "resp_genera_350.csv".

54 ENVIRONMENTAL SCIENCES↗

Diurnal observations of basal stem CO2 efflux in three canopy dominant trees in the central Amazon

We explored mechanisms responsible for disturbance-induced shifts in carbon metabolism in forests north of Manaus, Brazil (S2º 38' 17", W60º 09' 25"), using data from a selective logging experiment (BIONTE). BIONTE (the BIOmass and NuTrient Experiment) selective logging treatments were initiated in the mid-1980s and comprised variable removal of commercial tree (not total) species basal area (T1 =32%, T2 = 42%, T3 = 69%), and control plots with no logging (T0). Raw data including tree base diameter (Db), CO2 concentration versus time inside a static stem chamber, and wood density were used to derive stem respiration rates (Rw), wood production rates (Pw), and wood carbon use efficiency (CUE). Changes in tree base diameter (Db; measured at 1.3 m height, or above the buttresses) was used to calculate wood production for individual trees (Pw). Wood density enabled calculation of stem growth rate in the same units as stem respiration (mmol C m-2 s-1). Four trees were randomly selected from five tree growth rate classes for each treatment block, for a total of 20 trees per treatment block, or 80 trees total from the BIONTE plots. Each of the 80 trees selected for the Rw study were outfitted with dendrometer bands (da Silva et al. 2002). Stem respiration was measured using the method described in Chambers et al. (2004). Briefly, an infra-red gas analyzer (LiCor 820) was operated as a closed dynamic chamber with a flow rate of 1.0 L min-1. Polyvinyl chloride (PVC) semi-cylindrical chambers (250–400 mL) were secured to the tree stem near the dendrometer bands using nylon straps. The measurement interval spanned 1–2 min, and the CO2 flux from the stem of each tree was quantified using the enclosed stem area and slope of the stem CO2 concentration versus time.

54 ENVIRONMENTAL SCIENCES↗

CO2 Transport Planning Database

The CO2 Transport Planning Database v3.0 is a geospatial resource, containing over 70 gigabytes of data representing critical considerations for the spatial routing of pipelines and transport of CO2, from source to sink. Considerations include state-specific legislation, land use requirements, existing infrastructure, and hazard prevention areas. Built to support strategic domestic energy transport planning and development, more than 60 layers of this database have been weighted (Weight fields) according to current legislation and pipeline construction recommendations. Weighted values range from zero to one, where zero represents potentially more acceptable areas for transport based on the various considerations, and a value of one represents areas that should be avoided. This geospatial database provides a baseline for the Smart CO2 Transport Planning Tool.

Boundaries↗

CO2 Sweep Based on Geochemical, and Reservoir Characterization of the Residual Oil Zone of Hess's Seminole San Andres Unit (Final Report)

The study of the ROZ (residual oil zone) versus the MPZ (main pay zone) of the Seminole Field provided a unique insight into the nature of ROZ. This is because we had access to an order of magnitude of core available in other fields. We were also able to use an extensive petrophysical data base with one-foot sampling interval made available by the operator. We also obtained an extensive and unique data base containing the entire production history for the field at full resolution. The data set also includes a unique complete, highly-granular information on volumes of CO2 injection and CO2 production. We created a unique high-resolution model of the reservoir. This is the first such model that has been created. Using this very high-resolution data base we were able to make high resolution, multiphase fluid flow simulations. These fluid flow simulations have enabled our team to evaluate CO2 sweep in the ROZ in comparison with the MPZ.

01 COAL, LIGNITE, AND PEAT↗

Enrichment of H2 to CO2 ratio using formic acid as a hydrogen carrier - CRADA 581 (Abstract)

The collaboration between PNNL and OCOchem will investigate approaches to enhance the hydrogen (H2) content from a stream of H2 and carbon dioxide (CO2) gases produced in the catalytic decomposition of aqueous formic acid (FA85). OCOchem uses an electrochemical process to generate FA85 from captured CO2 using electricity from renewable resources. The FA85 is a liquid organic hydrogen carrier (LOHC) that provides the opportunity to transport and store hydrogen, in liquid form, at volumetric densities significantly greater than compressed H2 gas, i.e., 50 grams H2/liter FA. PNNL has developed and tested catalytic reactors to release H2 from LOHCs like FA and aqueous formate salts (FS). The H2 released from the LOHC can be oxidized in a proton-exchange membrane fuel cell (PEM-FC) to generate electricity with water as the only by-product. The purpose of the proposed project is to increase the purity of the hydrogen released from the LOHC to enhance the operation efficiency of the PEM FC. The PEM FC in combination with the LOHC provides an approach to demonstrate a portable generator that utilizes hydrogen as the energy carrier instead of conventional diesel generator. The ‘hydrogen generator’ can be used to supply emergency backup power and significantly reduce CO2 emissions relative to a diesel generator.

30 DIRECT ENERGY CONVERSION↗

CO2 Saline Storage: Costs and Storage Capacity

This document provides a brief description of the current state of CO2 saline storage. It then discusses factors determining the cost of storage and provides examples for two storage formations. It concludes by discussing the factors influencing CO2 storage capacity and provides cost supply cirves for CO2 storage.

Morgan, David↗

Assessing the Value of Seismic Amplitude Versus Offset (AVO) Attributes for CO2 Storage Project Using a Bayesian Network Model for Decision Support

Attributes versus offset (AVO) are a set of measurements to analyze how the characteristics of reflected seismic waves change as a function of the offset. It can be useful for monitoring CO2 storage sites because the presence of leaked CO2 into the overlying aquifer can change the properties of the rocks and pore fluids composition that can alter the way seismic waves reflect and their amplitudes. The time-lapse changes in AVO attributes derived from repeat seismic surveys can help identify anomalies or shifts in the subsurface that could potentially be used as an indicator for CO2 leak detection. Our study leverages multiple seismic attributes derived from synthetic seismic data and Bayesian network model to quantify the probability of leak detection in the overlying aquifer above the storage reservoir. It helps to quantify the value of individual seismic attributes at multiple monitoring periods based upon their sensitivities.

Kumar, Abhash↗

How is Rational Design of Electrocatalysts Crucial for Maximizing CO2 Electroreduction Performance?

The invited talk &quot;How is Rational Design of Electrocatalysts Crucial for Maximizing CO2 Electroreduction Performance?” was presented in the symposium &quot;Electrocatalysis for Sustainable Energy and Biomass Conversion: Fundamentals, Applications, and Perspectives”, Division of Catalysis Science and Technology, 2024 ACS Fall Meeting. The presentation briefly introduces NETL facilities and our Electrochemical Carbon Conversion portfolio to audiences. The talk primarily discusses how the geometry and surface composition of copper- and tin-based electrocatalysts would maximize the CO2 conversion to sustainable, carbon-neutral gas and liquid products in different device configurations. Ex situ and in situ characterization results are additionally discussed to correlate the structural, physico-chemical, and electronic properties with CO2 reduction activity and selectivity.

Nguyen Phan, Thuy Duong↗

Final Report: Enhanced Algal Production of CA for Improved Atmospheric Delivery of CO2 To Ponds

Technologies that enable direct-air-capture (DAC) of CO2 and eliminate the need for a CO2 capture, storage, and distribution system would significantly reduce the cost of algal production, and greatly increase the volume of algae biomass that can be produced by enabling algae farms to be located anywhere. Such technologies include cultivation under high alkalinity/high pH conditions, which increase the driving force for CO2 absorption, and development of genetic tools and genetically engineered strains to decorate the surface of the algae with carbonic anhydrase (CA), enable secretion of CA by the algae, or more generally boost the performance of the carbon concentrating mechanism (CCM).

09 BIOMASS FUELS↗

Assessing Ground State Energy of Molecules and Energy Profile of the NH3 Capturing CO2 System Using the Quantum Computing Algorithms

Molecule size correlates with the number of electrons on electronic energies and strength of anharmonicity on vibrational properties, however, it is challenging to address using classical computing. In this study, variational quantum eigensolver (VQE) algorithm was implemented on a quantum simulator to quantify electronic and vibrational energies and reaction pathways of CO2 + NH3 = NH2COOH. The VQE-based Hartree-Fock-Embedding algorithm was adopted to benchmark electronic energies for a series of molecules (doi.org/10.1063/5.0188249) and quantify the reaction energy profile of the CO2 capture reaction (doi.org/10.1116/5.0137750). The generated reaction profile is in good agreement with the classical high-level Coupled-Cluster-Singles-and-Doubles (CCSD) results. The quantum computing algorithm also helps enhance the calculation of vibrational ground-state energies by considering the many-body coupling using the Vibrational Self-Consistent Field method, providing results for CO2 and NH3 molecules with accuracy comparable to the direct diagonalization method. Our approach indicates quantum computing can be applied to solve practical problems.

Lee, Yueh-Lin↗

Multi-Parameter Optical Fiber for Distributed Sensing of Humidity, CH4, CO2, and Corrosion

This work describes the use of the optical fiber sensor (OFS) for successful monitoring of humidity, CH4, and CO2 based on the strain produced along the single-mode fiber (SMF) sensor. This is enabled by absorption of H2O/gases onto the commercially available polyacrylate coated jacketed portion of the fiber resulting in a change in strain. Under equilibrium, a differential microstrain was observed along the jacketed portion of the SMF with N2, CH4, and CO2 at different relative humidity (RH) conditions. The strain response of the SMF under various mixed gas composition and different RH conditions were also measured and made calibration curves accordingly. Linear regression and principal component analysis of the strain datasets provided deconvolution of the impact of strain from H2O, N2, CH4, and CO2. Additionally, modified OFS comprised of the Fe coated fiber section was employed to monitor corrosion based on the increase in backscattered intensity amplitude of the light being passed once corrosion of Fe occurs. Also, corrosion of Fe was studied under soil by installing the fiber in soil with protective measures which would prevent the sensor being mechanically disturbed or broken during installation. The corrosion rates were studied by monitoring the rate at which the intensity of backscattered light amplitude attains a steady state value when complete corrosion of Fe with a specific coating thickness occurs.

Mainali, Badri↗

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗