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13 results for “refinery”
Deep decarbonisation for refineries starts in Ireland: introducing the REALISE pilot campaigns
<p>This webinar shared the cutting-edge research on CO<sub>2</sub> capture solvent stability and solvent management being carried out in a real-life refinery setting in order to support industry’s decarbonisation ambitions. The event introduced the scientists engaged in solvent testing campaigns at Irving Oil Whitegate Refinery in Ireland and SINTEF’s CO<sub>2</sub> laboratories at Tiller in Norway. It included a short film and a panel Q&A. </p> <p><strong>Programme</strong></p> <ul> <li>Welcome, introduction & REALISE CCUS project overview – Peter van Os, TNO</li> <li>Solvent testing in a real-life refinery setting: introduction – Juliana Monteiro, TNO</li> <li>Demonstration at Irving Oil Whitegate Refinery: video premiere and insights – Eirini Skylogianni, TNO</li> <li>Demonstration goals at SINTEF’s Tiller CO<sub>2</sub> labs – Thor Mejdell, SINTEF</li> <li>Panel Q&A</li> </ul>
REALISE: Enabling full-chain CCUS for refineries through cluster-based strategies
<p>Refineries play a major role in our modern lifestyles, creating an almost infinite range of everyday products. Alongside other industries, they face the challenge to decarbonise as part of Europe’s wider efforts to meet climate targets by 2030. </p> <p>This webinar introduced the REALISE CCUS project, an international partnership of industry and scientists working to support the delivery of carbon capture, utilisation and storage (CCUS) technology for the refinery sector. </p> <p>Our research, funded by the European Union's Horizon 2020 programme, focuses on the full CCUS chain – from CO<sub>2</sub> capture, transport and geological CO<sub>2</sub> storage to CO<sub>2</sub> reuse – for clusters which include refineries and other industries. </p> <p>Specifically, we aim to demonstrate CCUS technology, enable sizeable cost-reductions, undertake public engagement and assess financial, political and regulatory barriers. </p> <p>Programme:</p> <ul> <li>Welcome & intro – Inna Kim, SINTEF (5 mins) </li> <li>Optimising and validating technologies for refineries (WP1) – Solrun Vevelstad, SINTEF (10 mins) </li> <li>Demonstrating pilot-scale CO<sub>2</sub> capture with optimised solvent (WP2) – Juliana Monteiro, TNO (10 mins) </li> <li>Assessing potential for CCUS at oil refineries within clusters (WP3) – Pádraig Fleming, Ervia (10 mins) </li> <li>Social, political and commercial context for CCS deployment (WP4) – Niall Dunphy, UCC (10 mins) </li> <li>Q&A (15 mins) </li> </ul>
Exposure and fragility of a virtual oil refinery testbed for seismic risk assessment
<p><span>Α</span><span> </span><span>virtual mid-size oil refinery, located in a high-seismicity region of Greece, is offered as a testbed for developing and testing system-level assessment methods. The dataset includes (a) a full geolocated exposure model with all pertinent critical assets, namely tanks, pressure vessels, process towers, chimneys, equipment-supporting buildings, and a flare; (b) the corresponding record-wise asset demands and summarized fragilities derived via nonlinear dynamic analyses on reduced-order numerical models.</span></p>
Economic and Environmental Performance of an Integrated CO2 Refinery
<p>Dataset associated with the publication "Economic and Environmental Performance of an Integrated CO<sub>2</sub> Refinery", available at <a href="https://doi.org/10.1021/acssuschemeng.2c06724">https://doi.org/10.1021/acssuschemeng.2c06724</a>. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript.</p>
Mapping of samples – Fuels from Reliable Bio-based Refinery Intermediates: BioMates, Schulzke et al., 2020, DOI:10.1007/s12649-019-00625-w
<table> <tbody> <tr> <td>In the H2020-project BioMates (www.biomates.eu, Grant Agreement No. 727463), Fraunhofer UMSICHT produced samples from ablative fast pyrolysis (AFP) of herbaceous biomass in a TRL 4-plant. A dedicated document provides identifiers for relevant liquid samples and their blends (DOI: 10.24406/fordatis/156). The document at hand maps it to the AFP-derived substances reported to be used in the article "T. Schulzke, S. Conrad, B. Shumeiko, M. Auersvald, D. Kubička, L. F. J. M. Raymakers; Fuels from Reliable Bio-based Refinery Intermediates: BioMates; Waste and Biomass Valorization (2020) 11:579–598; DOI:10.1007/s12649-019-00625-w", and provides further identifiers for samples not indexed earlier.</td> </tr> </tbody> </table>
Standardizing ID-Labels for seaweed samples used for chemical composition analyses and refinery processes in Nordic and European research projects
<p><strong>Introduction</strong></p> <p>Seaweed samples can be divided into two groups:</p> <ol> <li>Small samples (½-3 kg wet weight (ww)) often used for chemical content analyses including seasonal variation and testing different cultivation conditions or preliminary lab scale experiments on storage, extraction, separation, fermentation, etc.</li> <li>Larger samples (>3 kg ww) for lab- or pilot scale experiments on storage, extraction, separation, fermentation, etc.</li> </ol> <p>Seaweed samples will always have the following information-tracks:</p> <ol> <li><strong>Sample Code: </strong>A ID containing the most important information and the sample code will follow the sampled biomass from harvest to final research results.</li> <li><strong>Seaweed Processing Code:</strong> The sample code will be extended with 8 digits and 1 letter if processing of biomass occurs.</li> <li><strong>Batch Number:</strong> A code describing details about the harvest and origin of the seaweed.</li> <li><strong>Sample Overview:</strong> An Excel file describing all details about the sample: first <strong>sample code</strong>, then species, grinding, freezing/drying specifications, seeding and harvesting information, planed aim of the sample (e.g. polysaccharides), place stored, seaweed processing details, analyse results, etc. Maintained by the sample provider.</li> </ol>
Identifier Refinery Conversion Matrixes
<p><strong>identifier-refinery</strong></p> <p>Tools and assets for easy and reproducable gene identifier conversion.</p> <p><strong>Methods</strong></p> <p>This repository is used to build matricies which can convert between different gene identifiers.</p> <p>These conversion matricies are built by:</p> <ul> <li>Randomly choosing raw CEL files from NCBI GEO for a given platform accession code (in <code>/cels</code>)</li> <li>Reading the CEL header and joining Brainarray (e.g., <code>hgu133plus2hsensgprobe</code>) and Bioconductor (e.g., <code>hgu133plus2.db</code>) (x, y) coordinates</li> <li>Finding intersecting probe identifiers</li> <li>Extracting supported identifiers and probe IDs from the Bioconductor package</li> <li>Filtering on probe IDs and Ensembl Gene IDs in Brainarray</li> <li>Writing the output to a conversion TSV file</li> <li>Check that all output conversion TSV files have a shared SHA1</li> </ul> <p><strong>Repository Contents</strong></p> <p><strong>Source Files</strong></p> <p>The <code>cels</code> directory contains raw CEL files taken from GEO. The list of supported platforms is in <code>supported_microarray_platforms.csv</code>. Source files can be acquired by running the <code>acquire_cels.py</code> script.</p> <p><strong>Docker Image</strong></p> <p>The conversion scripts are run on custom Docker images.</p> <p>Two Dockerfiles are provided in this repository - <code>base</code> Docker image, which is used to install the quire R dependancies, and the <code>pd</code> image, which is used to build the required databases for a given platform.</p> <p><strong>Conversion Scripts</strong></p> <p>A <code>build_and_convert.py</code> script is provided, which build a unique Docker image for each package, mount the downloaded CEL files as a volume, and then run the gene conversion script <code>R/gene_convert.R</code> inside the image and output the master conversion matrix. Output TSV files live in <code>cels/out/</code>.</p> <p><strong>Reproducing</strong></p> <p>The entire process can be reproduced by running the following command script from a fresh checkout of this repository. It will take some time:</p> <pre><code>$ ./generate_matricies_from_scratch.sh </code></pre> <p>You can also choose to only build a specific platform, ex.,:</p> <pre><code>$ ./generate_matricies_from_scratch.sh celegans </code></pre> <p><strong>Identifiers</strong></p> <p>Released assets in this repository are availble under the DOI, <code>xyz:1.2.3.4</code>, which can be seen on Zenodo <a href="https://link.todo">here</a>.</p> <p><strong>Related Projects</strong></p> <ul> <li><a href="https://github.com/AlexsLemonade/refinebio">AlexsLemonade/refinebio</a></li> </ul> <p><strong>Copyright</strong></p> <p><code>identifier-refinery</code> output assets are released under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">CC0 1.0 Universal</a> license. All code is released under the BSD 3-clause license. Input assets are property of the original providers to NCBI GEO, but may be <a href="https://www.ncbi.nlm.nih.gov/geo/info/disclaimer.html">freely downloaded and redistributed</a> unless otherwise noted.</p> <p> </p> <p>https://github.com/AlexsLemonade/identifier-refinery</p>
Dataset - Global site-specific health impacts of fossil energy, steel mills, oil refineries and cement plants
<div> <div> <p>Climate change and particulate matter air pollution present major threats to human well-being by causing impacts on human health. Both are connected to key air pollutants such as carbon dioxide (CO<span><span><span>2</span></span></span>), primary fine particulate matter (PM<span><span><span>2.5</span></span></span>), sulfur dioxide (SO<span><span><span>2</span></span></span>), nitrogen oxides (NO<span><span><span>x</span></span></span>) and ammonia (NH<span><span><span>3</span></span></span>), which are primarily emitted from energy-intensive industrial sectors. We present the first study to consistently link a broad range of emission measurements for these substances with site-specific technical data, emission models, and atmospheric fate and effect models to quantify health impacts caused by nearly all global fossil power plants, steel mills, oil refineries and cement plants. The resulting health impact patterns differ substantially from far less detailed earlier studies due to the high resolution of included data, highlighting in particular the key role of emission abatement at individual coal-consuming industrial sites in densely populated areas of Asia (Northern and North-Eastern India, Java in Indonesia, Eastern China), Western Europe (Germany, Belgium, Netherlands) as well as in the US. Of greatest health concern are the high SO<span><span><span>2</span></span></span> emissions in India, which stand out due to missing flue gas treatment and cause a particularly high share of local health impacts despite a limited number of emission sites. At the same time, the massive infrastructure and export capacity build-up in China in recent years is taking a substantial toll on regional and global health and requires more stringent regulation than in the rest of the world due to unfavorable environmental conditions and high population densities. The current phase-out of highly emitting industries in Europe is found not to have started with sites having the greatest health impacts. Our detailed site-specific emission and impact inventory is able to highlight more effective alternatives and to track future progress.</p> </div> </div>
Supplementary material 1 from: Walton S, Livermore L, Bánki O, Cubey RWN, Drinkwater R, Englund M, Goble C, Groom Q, Kermorvant C, Rey I, Santos CM, Scott B, Williams AR, Wu Z (2020) Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes 6: e57602. https://doi.org/10.3897/rio.6.e57602
Tools and services evaluation speadsheet
Figure 1 from: Walton S, Livermore L, Bánki O, Cubey RWN, Drinkwater R, Englund M, Goble C, Groom Q, Kermorvant C, Rey I, Santos CM, Scott B, Williams AR, Wu Z (2020) Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes 6: e57602. https://doi.org/10.3897/rio.6.e57602
Figure 1 An overview of potential Specimen Data Refinery workflows based on image inputs and their derivatives, datasets and services.
Figure 3 from: Walton S, Livermore L, Bánki O, Cubey RWN, Drinkwater R, Englund M, Goble C, Groom Q, Kermorvant C, Rey I, Santos CM, Scott B, Williams AR, Wu Z (2020) Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes 6: e57602. https://doi.org/10.3897/rio.6.e57602
Figure 3 The proposed workflow technology stack for the SDR.
Figure 2 from: Walton S, Livermore L, Bánki O, Cubey RWN, Drinkwater R, Englund M, Goble C, Groom Q, Kermorvant C, Rey I, Santos CM, Scott B, Williams AR, Wu Z (2020) Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes 6: e57602. https://doi.org/10.3897/rio.6.e57602
Figure 2 Traffic-light results of gap analysis applied to overall proposed workflow.
Gene expression of PBMC of chinese nickel refinery workers when compared to the gene expression profile of PBMCs from referent subjects
GEO Series GSE40392. Homo sapiens. 18 samples. Type: Expression profiling by array.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.