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32 results for “carbon capture”
Dataset and code: One-tenth of EU's biomethane potential combined with carbon capture and storage can shift the region's ammonia production to net-zero
<h2>Overview</h2> <p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. One-tenth of EU’s biomethane potential combined with carbon capture and storage can shift the region’s ammonia production to net-zero. One Earth (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p> <div> <h2>Repository structure</h2> </div> <p>The data folder includes:</p> <ul> <li><code>inventories.xlsx</code> contains the LCI datasets for biomethane and ammonia production formatted for use with <a href="https://github.com/brightway-lca">Brightway</a>.</li> <li><code>sustainable_biomethane_potential_Europe.xlsx</code> contains data on the sustainable biomethane potential in Europe disaggregated by feedstock and country.</li> <li><code>ammonia_production_europe.xlsx</code> contains ammonia production levels in the EU in 2021.</li> <li><code>SA_methane leakage_for presample.xlsx</code> contains data to perform sensitivity analysis on the methane leakage with <a href="https://github.com/PascalLesage/presamples">presamples</a></li> <li><code>SA_upgrading technology_presamples.xlsx</code> contains data to perform sensitivity analysis on upgrading technologies with <a href="https://github.com/PascalLesage/presamples">presamples</a></li> <li><code>results</code> folder within data contains csv files with the results, which are used in <code>05_visualization.ipynb</code> for analysis and visualization purposes.</li> </ul> <p>The notebooks folder includes:</p> <ul> <li><code>01_project_setup.ipynb</code> sets up a new Brightway project and imports the ecoinvent database.</li> <li><code>02_lci.ipynb</code> imports the LCIs and regionalize some datasets (e.g., biomethane supply based on the bimethane potential).</li> <li><code>03_lcia.ipynb</code> calculates life cycle impacts and all the additional results presented in the paper (e.g., calculation of blending ratios).</li> <li><code>04_sensitivity_analysis.ipynb</code> performs the sensitivity analysis.</li> <li><code>05_visualization.ipynb</code> imports all results and generates the figures presented in the scientific article.</li> </ul> <p>The src folder contains supporting functions required to regionalize LCIs and perform the calculations.</p> <div> <h2>How to get propertary data</h2> </div> <p>Some of the LCI datasets in the <code>inventories.xlsx</code> file are partially based on data from the ecoinvent LCI database. To comply with licensing requirements, the file shared in this repository does not include these data points. If you hold a valid ecoinvent license, please contact me directly to receive the full input files containing all ecoinvent data points.</p> <h2>Contact</h2> <p>Robert Istrate: i.r.istrate@cml.leidenuniv.nl</p>
Navigating within the Safe Operating Space with Carbon Capture On-Board
<p>This file contains the data used to plot Figure 3 and Figure 4 in the manuscript Navigating within the Safe Operating Space with Carbon Capture On-Board published in ACS Sustainable Chemistry and Engineering.</p>
Supplementary data for "Deep learning for industrial processes: Forecasting amine emissions from a carbon capture plant"
<p>A preliminary analysis of the data already has been discussed in <a href="https://dx.doi.org/10.2139/ssrn.3812299">10.2139/ssrn.3812299</a>.</p> <p><strong>Raw data</strong></p> <p>Raw measurement data is in the Excel files `day*_raw.xlsx`.</p> <p><strong>Model</strong></p> <p>Covariate and label scaler objects are serialized in joblib format in the following files:</p> <ul> <li>20210812_y_transformer_co2_ammonia_reduced_feature_set</li> <li>20210812_y_transformer__reduced_feature_set</li> <li>20210812_x_scaler_reduced_feature_set</li> </ul> <p>Checkpoints of the models are in the `*.pth.tar` files. An example for loading the models is:</p> <pre><code class="language-python">from pyprocessta.model.tcn import TCNModelDropout model_cov = TCNModelDropout( input_chunk_length=8, output_chunk_length=1, num_layers=5, num_filters=16, kernel_size=6, dropout=0.3, weight_norm=True, batch_size=32, n_epochs=100, log_tensorboard=True, optimizer_kwargs={"lr": 2e-4}, ) model_cov.load_from_checkpoint('20210814_2amp_pip_model_reduced_feature_set_darts')</code></pre> <p>which assumes that the checkpoints are placed as `model_best.pth.tar` in a folder called `20210812_2amp_pip_model_reduced_feature_set_darts`.</p> <p> </p>
N-containing carbons derived from microporous coordination polymers for use in post-combustion flue gas capture
<p>Datasets for the plots shown in the article entitled: N-containing carbons derived from microporous coordination polymers for use in post-combustion flue gas capture </p>
Webinar: Reducing Industrial Carbon Emissions. Carbon capture, utilisation, and storage technologies
<p>The EU recently set unprecedented goals in terms of reducing emissions (-55% by 2030, climate neutrality by 2050). Solutions such as carbon capture, utilisation and storage, or “CCUS”, involve capturing CO2 from industrial plants or installations, transporting it to designated sites, and injecting it into geological formations, making it extremely relevant to face this ambitious, but necessary challenge.</p> <p>This webinar shared some the latest advances in CCUS. The research project cluster on <strong>Better Carbon Capture for Industrial Emissions</strong> is formed by three EU-funded projects: <strong><a href="http://www.cleanker.eu/">CLEANKER</a></strong>, <strong><a href="http://www.realiseccus.eu/">REALISE CCUS</a></strong>, and <strong><a href="http://www.c4u-project.eu/">C4U</a></strong>, and are advancing the state of the art and reducing emissions from industrial plants directly focussing on three different sectors: cement, refineries and steel.</p> <p>Experts from these EU-backed projects showcased the technological approaches, their financial feasibility, and how their approaches in implementing CCUS have shown up to 90% reduction in CO2 emissions in certain plants. Policy aspects that have an effect on the implementation of CCUS were discussed.</p>
Trade-offs between Sustainable Development Goals in carbon capture and utilisation
<p>Dataset associated with the publication "Trade-offs between Sustainable Development Goals in carbon capture and utilisation" by Iasonas Ioannou, Ángel Galán-Martín, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1039/D2EE01153K">https://doi.org/10.1039/D2EE01153K</a>. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript.</p>
Data and code for: Combining eddy covariance towers, field measurements, and the MEMS 2 ecosystem model improves confidence in the climate impacts of bioenergy with carbon capture and storage
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Data from: Natural tree colonisation of organo-mineral soils does not provide a net carbon capture benefit at decadal timescales
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Seagrass ecosystem metabolic carbon capture in response to green turtle grazing across Caribbean meadows, 2016 - 2018
This dataset contains ecosystem metabolism and seagrass meadow data from five locations in the Greater Caribbean and Gulf of Mexico regions at which green turtle populations had established foraging areas. Ecosystem metabolic rates were compared between grazed and adjacent ungrazed areas of seagrass (Thalassia testudinum) to investigate the effects of green turtle grazing on metabolic carbon capture rates in seagrass meadows across a wide geographic area. Seagrass data are provided for site descriptions and drivers of variation in metabolic rates. Ecosystem metabolic rates are also included for meadows of the invasive seagrass Halophila stipulacea from two locations for comparison to rates in the native seagrass meadows where this invasive seagrass is encroaching upon green turtle foraging areas. Data were collected from one location (Little Cayman) in 2016, and from the remaining four locations (Bonaire; St. Croix; Eleuthera, Bahamas; west coast of Florida) in 2018.
CAPITAN - Carbon cAPture vIa cemenT cArbonationN
<p>LCA framework to assess the supply chain of CO2 minerlization in recycled concrete aggregate.</p> <p>Deliverable of the WP2 of the DemoUpCarma research project (http://www.demoupcarma.ethz.ch/en/home/).</p> <p>DemoUpCARMA is funded and supported by the Swiss Federal Office of Energy (SFOE) and the Federal Office for the Environment (FOEN).</p>
Cost reductions in renewables can substantially erode the value of carbon capture and storage in mitigation pathways: supporting data
<p>This provides supporting data for the paper, <em><strong>Cost reductions in renewables can substantially erode the value of carbon capture and storage in mitigation pathways</strong></em>, published in <em>One Earth</em>. The full paper can be accessed at the following DOI: https://doi.org/10.1016/j.oneear.2021.10.024, published on the 19th November 2021.</p>
Raw dataset for Can bioenergy with carbon capture and storage result in carbon negative steel?
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer, S.E., Blok, K., Ramírez, A., 2020. Can bioenergy with carbon capture and storage result in carbon negative steel? International Journal of Greenhouse Gas Control, 100. doi:10.1016/j.ijggc.2020.103104.</p> <p>also published as chapter 5 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</p>
Supplementary data as part of the article "The role of carbon capture, utilization and storage for economic pathways that limit global warming to below 1.5 °C" (https://doi.org/10.1016/j.isci.2022.104237).
<p>Data of the plots in Figure 1a and Figure 1b, showing, respectively, the incremental and the integrated global CO<sub>2</sub> emissions for the 1.5 °C-committed decarbonization pathways from IPCC (P1, P2, P3, and P4 pathways) and the one originally obtained in this work (Q pathway).</p>
A Review on Emerging Organic-containing Microporous Material Membranes for Carbon Capture and Separation
<p><strong>Published article:</strong> Prasetya, N., Himma, N. F., Doddy Sutrisna, P., Wenten, I. G. & Ladewig, B. P. <em>Chem. Eng. J.</em> 123575 (2019). <a href="https://doi.org/10.1016/j.cej.2019.123575">https://doi.org/10.1016/j.cej.2019.123575</a></p> <p><strong>Abstract</strong></p> <p>Membrane technology has gained great attention as one of the promising strategies for carbon capture and separation. Intended for such application, membrane fabrication from various materials has been attempted. While gas separation membranes based on dense polymeric materials have been long developed, there is a growing interest to use porous materials as the membrane material. This review then focuses on emerging porous materials to be used for the fabrication of membranes that are designed for CO<sub>2</sub>separation. Criteria for selecting microporous material are first discussed, including physical and chemical properties, and parameters in membrane fabrication. Membranes based on emerging porous materials,such as metal-organic frameworks, porous organic frameworks, and microporous polymers,are then reviewed. Finally, special attention is given to recent advances, challenges, and perspectives in the development of such membranes for carbon capture and separation. </p>
Data set for "Stabilization of nesquehonite for application in carbon capture utilization and storage"
<p>The dataset contains all relavent raw data from the study titile, "Stabilization of nesquehonite for application in carbon capture utilization and storage". </p>
Dataset: Carbonate Regeneration Using a Membrane Electrochemical Cell for Efficient CO2 Capture
<p>Dataset for journal publication "Carbonate Regeneration Using a Membrane Electrochemical Cell for Efficient CO2 Capture," published in ACS Sustainable Chemistry & Engineering (DOI: 10.1021/acssuschemeng.2c04175). Data are organized according to figure number.</p>
Eindhoven - Carbon sequestration and air pollutants capture
<p>Results of the i-Tree simulations of carbon sequestration, total amount of carbon stored in vegetation and air pollutants capture in three NBS locations in Eindhoven.</p>
Data for Process Design and Energy Assessment of an Onboard Carbon Capture System with Boilers or Heat Pumps for Additional Steam Generation
<p>1. Supporting file includes main stream information used in Aspen HYSYS model, process simulations of boiler and heat pump for model construction.<br> 2. Supporting file also includes main information used in ProMAX model, process simulations of carbon capture process for model construction.</p>
Carbon capture potential and environmental impact of concrete weathering in soil
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Solid-state NMR data for: Sequential pore functionalization in MOFs for enhanced carbon dioxide capture
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International Brain Laboratory public data
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