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33 results for “Pyrolysis”
Ramped Pyrolysis Oxidation (RPO) coupled radiocarbon (14C-DOC) and stable carbon (13C-DOC), high-resolution molecular composition (FT-ICR MS), and biodegradable dissolved organic carbon (BDOC) of groundwater, river water, and lagoon water in northeast Alaska, 2017
Supra-permafrost groundwater (SPGW), river water, and lagoon water were sampled near Kaktovik, AK to assess the reactivity and origin of dissolved organic matter (DOM) across interconnected hydrologic systems during late summer. Water samples were collected on August 17th 2017 from SPGW along the beach of Jago Lagoon (Jago GW), surface water from the Jago River’s main channel above tidal influence (Jago R), and from the water column of Kaktovik Lagoon at 2–3 m depth (KA LW). Measurements were made from grab samples for river and lagoon water, and from a composite sample for SPGW gathered from 10 individual shoreline locations. Data include dissolved organic carbon concentration (DOC, mg C L-1), Ramped Pyrolysis Oxidation (RPO) derived fraction compositions of 13C-DOC (δ13C ‰), 14C-DOC (in fraction modern), and method/instrumental error in the 14C and 13C results, and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) molecular composition and summarized compound classes. Biodegradable DOC (BDOC) bottle experiments were performed using all three sample types, where DOC concentration was subsequently measured at 2, 7, 14, and 28 days. FT-ICR MS composition was subsequently measured at the 28-day timepoint to track changes in molecular formulae and compound class relative abundance following biodegradation. Data from RPO serial thermal oxidation include temperature and normalized CO2 profiles for each background sample. Thermal-oxidation profiles of CO2 were transformed into non-parametric activation energy (E) distributions using an inverse model. Model output includes C mass of oxidized CO2 (µg C), Tmax (K), Emax (kJ mol-1), Emean (kJ mol-1), Estd (kJ mol-1), and p(0,E)max of user-defined sample fractions. FT-ICR MS results include a summary table of the relative abundance of compound classes (e.g., unsaturated phenolic, polyphenolic, aliphatic, condensed aromatics, peptide-like) and elemental groupings (e.g., CHO-type, CHON-type, CHOS-type, CHON
Mapping of samples – Deliverables in WP3: Novel pyrolysis oil from non-food/feed biomass
<p>In the H2020-project BioMates (www.biomates.eu, see chapter 4 “Funding and disclaimer”), RISE produced samples from ablative fast pyrolysis of herbaceous biomass in a TRL 5-plant. A dedicated document provides identifiers for relevant liquid samples and their blends [1]. The document at hand maps it to the substances reported to be used in the public deliverables and public deliverable summaries connected to Work Package 3 “Technology scale up and validation” of the BioMates-project, as far as mapping is not provided in the documents themselves.</p>
Data for: Hydrogen production via methane pyrolysis
<p>This dataset contains information regarding patented methods for the production of hydrogen via methane pyrolysis.</p> <p>The processes described in scientific literature can be divided into three categories: thermal, plasma and catalytic decomposition. [1] The same categories are found in the patent literature.</p> <p>The most popular and currently used methods for making hydrogen are coal gasification, steam methane reforming (SMR) and water electrolysis. </p> <p>Methane pyrolysis can be seen as a suitable alternative and environmentally friendly technology; It consists in the thermal decomposition of methane, with the formation of solid carbon (instead of CO/CO<sub>2</sub>) as reaction by-product.</p> <p>The catalytic methane pyrolysis has been widely investigated. Iron and carbon-based (carbon black, graphite, carbon nanotubes) catalysts are considered the best candidates for industrial implementation.[2]</p> <p>The following patent databases used have been used for data mining: </p> <p>- Espacenet (a free of charge database provided by the European Patent Office), accessed on Sept. 10, 2022</p> <p>- Orbit Intelligence (FamPat database) (a fee-based platform provided by Questel), accessed on Sept. 10, 2022</p> <p>The file titled “<em>Methane pyrolysis _ Espacenet</em>” contains information related to Title, Inventors, Applicants, Publication number, Earliest priority, IPC, CPC, Publication date, Earliest publication, and Family number.</p> <p>The file titled “<em>Methane pyrolysis _ Orbit</em>” contains information related to Priority numbers, Application numbers, Publication numbers, Priority dates, Application dates, Publication dates, Title, Abstract, and Current assignees.</p> <p>Patent searches were carried out by means of keywords and classification/indexing codes. [3, 4]</p> <p>Both IPC (International Patent Classification) and CPC (Cooperative Patent Classification) systems were used.</p>
Ab Initio and Kinetic Modelling of β-D-xylopyranose under Fast Pyrolysis Conditions
<p>Zip file containing all the IRC connecting transition states to minima in our paper published in The Journal of Physical Chemistry A, <a title="DOI URL" href="https://doi.org/10.1021/acs.jpca.3c07063">https://doi.org/10.1021/acs.jpca.3c07063</a></p>
Pyrolysis process in SCENARIOS
<p>A very brief explanation of the concept of vermichar for decontaminating PFAS-contaminated soils.</p>
BioMates - Bio-oil from ablative fast pyrolysis: Identifiers for WP3- samples and blends
<p>Ablative Fast Pyrolysis (AFP) is the first step in the BioMates-concept to convert herbaceous biomass into co-feed with reliable properties for conventional refineries (www.biomates.eu). The document provides the coding behind the identifiers used for samples and sample blends produced by RISE via AFP within the H2020-project BioMates.</p>
Quantum calculation results for "Butyl Acetate Pyrolysis and Combustion Chemistry: Mechanism Generation and Shock Tube Experiments"
<p>This repository contains the quantum calculation results associated with the paper "Butyl Acetate Pyrolysis and Combustion Chemistry: Mechanism Generation and Shock Tube Experiments" by Xiaorui Dong, Gianmaria Pio, Farhan Arafin, Andrew Laich, Jessica Baker, Erik Ninnemann, Subith S. Vasu, and William H. Green.</p> <p>In the BA_QM.zip, there are five subfolders:</p> <ul> <li>The "BA_Habs" folder has 18 entries related to the calculations of butyl acetate H abstraction reactions</li> <li>The "BA_Retroene" folder has 6 entries related to the calculations of butyl acetate retro-ene reactions</li> <li>The "Species" folder has 606 entries related to the calculations of non-TS species included in the kinetic mechanisms.</li> <li>The "TS_XYZ" and "XYZ" folders contain the XYZ files of the calculated TS and non-TS geometries, respectively.</li> </ul> <p>For each species and TS calculation, the CBS-QB3 optimization/single point energy calculation is stored in the "composite" folder, the frequency calculation is stored in the 'freq' folder, and scan jobs for torsional modes (if available) are stored in the 'scan_XXXX' folders. For reaction and TS entries, folders are named in a user-readable way. For species, folders are named according to their SMILES representation.</p>
Data - Co-feeding of VGO and pine-wood derived hydrogenated pyrolysis oils in an Fluid Catalytic Cracking pilot plant to generate olefins and gasoline
<p>Dataset to the corresponding research article with the same title. Research article is submitted to the open research europe platform.</p>
PMMA Pyrolysis Simulation – from Micro- to Real-Scale -- Dataset
<p>Dataset to the article "PMMA Pyrolysis Simulation – from Micro- to Real-Scale". It contains experiment data, FDS simulation and PROPTI input files, as well as the Jupyter Notebooks used to process the different data series.</p> <p> </p> <p>Published with the Fire Safety Journal: <a href="https://doi.org/10.1016/j.firesaf.2023.103926">https://doi.org/10.1016/j.firesaf.2023.103926</a><br> Preprint on arXiv: <a href="https://arxiv.org/abs/2303.17446">https://arxiv.org/abs/2303.17446</a><br> </p> <p>A video series explaining the different parts of this dataset is available on YouTube: <a href="https://www.youtube.com/playlist?list=PLWziITJoPnJKtVuaVudyO2Lz-OWaCE9Co">https://www.youtube.com/playlist?list=PLWziITJoPnJKtVuaVudyO2Lz-OWaCE9Co</a></p> <p>Recommended minimum download: "RunReports", "SmallerDirectories", "PMMA_MicroScale" and "PMMA_Cone_Aalto".</p> <p>Extract all files into the same directory, such that all the relative paths point to the correct locations.</p> <p>The data of the inverse modelling processes are stored in the "PMMA_MicroScale" and "PMMA_Cone_Aalto" archives.</p> <p>The "RunReports" archive contains all the data processing and analysis. This is, the notebooks to generate the plots from the IMP results, extracting the stoichiometry from the CHID.out files, writing the best material parameter sets into the parallel panel simulation templates and so forth.</p> <p>The data of the parallel panel simulations is distributed over a couple of *.rar archives, labelled "ParallelPanelTest_A" to "ParallelPanelTest_H". Their content should be extracted into a directory labelled "ParallelPanelTest", to allow the Python scripts to work as intended. Please refer to the video series for further information.</p> <p>The archive "SmallerDirectories" contains a couple of smaller assessments (compared to the parallel panel simulations), like the cone calorimeter GEOM to generate the flux map, flame height as well as the "GeneralInformation" directory. It is recommended to download this archive together with the "RunReports".</p> <p> </p> <p>Note: the data series labels used in the article differ from the ones used for the simulations.</p> <p>Simulations: Aalto_01, Aalto_02, Aalto_03, Aalto_04, Aalto_05, Aalto_06, Aalto_06b, Aalto_07</p> <p>Article: Cone_01, Cone_02, Cone_03, Cone_04, Cone_05, Cone_06, Cone_07, Cone_08</p> <p> </p> <p>Acknowledgments:<br> We gratefully acknowledge the computing time granted through JARA (project jjsc27) on the supercomputer JURECA at Forschungszentrum Jülich and through the project on the CoBra-system, funded by the German Federal Ministry of Education and Research with the grant number 13N15497. This research was partially funded by the German Federal Ministry of Education and Research with the grant number 13N15497.</p>
Pyrolysis Model Data Set Contribution for the MaCFP Workshop April 2021 - Dataset
<p>This is a contribution of material parameters for pyolysis modelling, to the <a href="https://iafss.org/macfp/">MaCFP Workshop in April 2021</a>.</p> <p> </p> <p>This repository contains the input files for the inverse modelling process (IMP), the data bases with the IMP results, the analysis scripts and the simulation data of the requested model predictions.</p> <p>Different approaches were followed and all their data is stored here. Only two of them were presented to the MaCFP Workshop and were labelled "Approach A" and "Approach B" in the submitted report. Note however, that the labelling of the conducted IMP's is different.</p> <ul> <li>Approach A: R2_CAPAII_UMD_60_from_TGA_LCPP (Case A2)</li> <li>Approach B: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_03 (Case B2)</li> </ul> <p> </p> <p>An intermediate naming convention was introduced as follows:</p> <ul> <li>Case A1: TGA: R2_CAPAII_UMD_25_60_from_TGA_LCPP, CAPA II: R2_CAPAII_UMD_25_60_from_TGA_LCPP</li> <li>Case A2: TGA: R2_CAPAII_UMD_25_60_from_TGA_LCPP, CAPA II: R2_CAPAII_UMD_60_from_TGA_LCPP</li> <li>Case A3: TGA: R2_TGA_UMET_1_10_50_Methane_Scen_00_neu, CAPA II: R2_CAPAII_UMD_25_60_from_TGA_UMET</li> <li>Case A4: TGA: R2_TGA_UMET_1_10_50_Methane_Scen_00_neu, CAPA II: R2_CAPAII_UMD_60_from_TGA_UMET</li> <li>Case B1: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_02</li> <li>Case B2: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_03</li> <li>Case B3: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_rep_02</li> </ul> <p>Note: The two archives mimic the directory structure of the project, where both are at the same lavel next to each other. If you would want to run the analysis scripts the relative file paths should work out of the box.</p> <p> </p> <p>Version 1.1: Added description and README.</p> <p> </p> <p>Version 1.2: Added archive containing the FDS input and output of approaches A and B directly (one does not need to download the full IMP_Runs21.rar), as well as the files contributed to the MaCFP-2 workshop.</p>
Pyrolysis Model Data Set Contribution for the MaCFP-3 Workshop October 2023
<p>Pyrolysis model data set contribution for the <a href="https://iafss.org/macfp/">MaCFP-3 workshop October 2023</a></p> <p>The contributions are also available via the materials database <a href="https://github.com/MaCFP/matl-db">MaCFP git repository.</a></p> <p>For the inverse modelling in general, please see the manuscript <a href="https://doi.org/10.48550/arXiv.2303.17446">"PMMA Pyrolysis Simulation -- from Micro- to Real-Scale"</a></p> <p> </p> <p>There are three different ZIP archives provided:</p> <ul> <li>"MaCFP_Gasification" contains the simulations reproducing the NIST Gasification Apparatus for validation of the parameter sets, as requested per MaCFP. There are three different runs conducted, two for the originally submitted parameter sets (BUW-FZJ Approach A/B). No changes to the parameter sets are conducted. A new parameter set is provided to the MaCFP repository (BUW-FZJ PMMA01) and its validation simulations are provided as well. Furthermore, Jupyter notebooks are provided to process the simulation data.</li> <li>"PyrolysisKinetics_PMMA01" contains the inverse modelling run to determine the pyrolysis reaction kinetics.</li> <li>"Thermophysical_PMMA01" contains the inverse modelling run to determine the thermophysical parameters.</li> </ul>
Techno-economic model of Methane pyrolysis in liquid metal bubble column reactor for hydrogen direct reduction of iron ore
<p>Reducing emissions from the iron and steel industry is essential to achieve the Paris climate goals.A new system to reduce the carbon footprint of steel production is proposed in this article by coupling hydrogen direct reduction of iron ore (H-DRI) and natural gas pyrolysis on the liquid metal surface inside a bubble column reactor. If grid electricity from the EU is used, the emissions would be 435 kgCO\textsubscript{2}/tls without considering methane leakage from the extraction, storage, and transport of natural gas. Solid carbon, produced as a by-product of natural gas decomposition, finds applications in many industrial sectors, including as a replacement for coal in coke ovens. The specific energy consumption (SEC) of the proposed system is approximately 6.3 MWh per ton of liquid steel(tls).It is higher than other competing technologies,3.48 MWh/tls for water electrolysis based DRI, and 4.3-4.5 MWh/tls for natural gas-based DRI and blast furnace-basic oxygen furnace (BF-BOF) respectively. The utilization of large quantities of natural gas, where the carbon remains unused, is the major reason for the high SEC. Preliminary analysis of the system revealed that it has the potential to compete with existing technologies to produce CO\textsubscript{2}free steel, if renewable electricity is used. Further studies on the kinetics of the bubble column reactor, H-DRI shaft furnace, design, and sizing of components, along with the building of industrial prototypes are required to improve the understanding of the system performance.</p>
Intrusive and Non-Intrusive Uncertainty Quantification Methodologies for Pyrolysis Modeling
<p>This repository contains python scripts and results for uncertainty analysis of Arrhenius equation with kinetic parameters as uncertain. The data set contains folders for each PMMA variant (1,2 and 3) used for uncertainty quantification (UQ) and 'Misc' folder containing miscellaneous files and scripts used in the study. </p> <p>Each PMMA variant folder has further sub-folders for the UQ methods implemented:</p> <ol> <li>Intrusive polynomial chaos (IPC)</li> <li>Monte Carlo (MC)</li> <li>Non-intrusive polynomial chaos (NIPC)</li> </ol> <p>Additionally convergence and comparison for the UQ methods is available in sub-folders of the same name respectively.</p> <p>The python file 'UoWu_noLatex.mplstyle' for plotting style used by us is attached to ease the rerunning of the scripts.</p>
BioMates - WP1: Novel pyrolysis oil from non-food/feed biomass - Data sheet: Identifiers for samples and blends
<p>Ablative fast pyrolysis (AFP) is the first step in the BioMates-concept for producing bio-based intermediates (BioMates) with reliable properties from stalk-type biomass. These intermediates are designed for co-processing within conventional refineries. The H2020-project BioMates validates the process concept (<a href="http://www.biomates.eu">www.biomates.eu</a>).</p> <p>AFP produces bio-oil to be mildly hydrotreated towards said intermediates afterwards. This document provides identifiers for samples and blends produced in TRL4 via AFP from wheat-barley-straw and Miscanthus within the BioMates-project.</p>
Data Repository for Peatland Organic Matter Quality Varies with Latitude as Confirmed from FTIR and Ramped Pyrolysis Oxidation
<p><strong>Data information:</strong></p> <p>This is the dataset for a study by Sparrow et al. comparing the organic matter quality of peat cores in four climates (tropical, subtropical, boreal, and polar) using multiple analytical approaches. Data for 16 samples (samples from 4 depths in each of the 4 cores) are presented in the study and all relevant and necessary data are published in this repository:</p> <ul> <li>14C content and d13C results for 1) bulk and 2) ramped pyrolysis oxidation splits and standard materials</li> <li>Elemental analysis results (%C, %N, C:N)</li> <li>FTIR spectra from 650-4000 cm-1</li> <li>Ramped pyrolysis oxidation run data (time, sample oven temperature, baseline-corrected CO2 concentration)</li> </ul> <p><strong>Sample information:</strong></p> <ul> <li>The Tropical peat samples were collected from a core obtained in November 2011, “MDM11-2A,” (4.3727°N, 114.3550°E) in the Ulu Mendaram Conservation Area in the Belait District of Brunei Darussalem, northwest Borneo.</li> <li>The Subtropical peat samples were collected from the “Lox3” core (26.597°N, 80.357°W) in October 2015 from the Loxahatchee National Wildlife Refuge, Florida.</li> <li>The Boreal peat samples were collected from the “T3F” core (47.5063°N, 93.4527°W) from S1 Bog in July 2012 from the Marcell Experimental Forest, near Grand Rapids, Minnesota.</li> <li>The Polar peat samples were collected from the “CPP” core (68.3531°N, 19.0473°E) in June 2012 from Stordalen Mire, a peat plateau underlain by discontinuous permafrost near Abisko, Sweden.</li> </ul>
Kinetic parameters for the pyrolysis of hardwood and softwood
<p>This a supporting document presenting the values of the kinetic parameters for pyrolysis of hardwood and softwood. The parameters were derived elsewhere for nine reduced kinetic schemes using multi-objective optimisation to both isothermal and non-isothermal experiments.</p>
Dataset: Catalytic pyrolysis mechanism of lignin moieties driven by aldehyde, hydroxyl, methoxy, and allyl functionalization: the role of reactive quinone methide and ketene intermediates
<p>The derived data is part of a manuscript submitted to the journal Green Chemistry, entitled "Catalytic pyrolysis mechanism of lignin moieties driven by aldehyde, hydroxyl, methoxy, and allyl functionalization: the role of reactive quinone methide and ketene intermediates". [DOI https://doi.org/10.1039/d4gc03143a. This work is supported by the Swiss National Science Foundation (project number 200021_178952)</p> <p>Raw data can be found: https://doi.org/10.16907/1ac46f03-6e1b-4230-b5c0-3df2cec87b5e</p> <p>The raw Photoelectron Photoion Coincidence (PEPICO) data were obtained during catalytic fast pyrolysis of eugenol, vanillin, syringol over H-ZSM-5 and HFAU. The analysis software can be obtained from: https://www.psi.ch/en/sls/vuv/pepico. The folders contain mass spectra and threshold photoelectron raw data at different reactor temperatures.</p>
Data from: Chemically distinct particle phase emissions from highly controlled pyrolysis of three wood types
<p>Wood pyrolysis is a distinct process that precedes combustion and contributes to biomass and biofuel burning gas phase and particle phase emissions. Pyrolysis is defined as the thermochemical degradation of wood, the products of which can be released directly or undergo further reaction during gas-phase combustion. To isolate and study the processes and emissions of pyrolysis, a custom-made reactor was used to uniformly heat small blocks of wood in a nitrogen atmosphere. Pieces of maple, Douglas fir and oak wood (maximum 155 cm<sup>3</sup>) were pyrolyzed in a temperature-controlled chamber set to 400, 500, or 600°C. Real time particle phase emissions were measured with a Soot Particle Aerosol Mass Spectrometer (SP-AMS) and correlated with simultaneous gas phase emission measurements of CO. Particle and gas emissions increased rapidly after inserting a wood sample, remained high for tens of minutes, and then dropped rapidly leaving behind char. The particulate mass loading profiles varied with elapsed experiment time, wood type and size, and pyrolysis chamber temperature. The chemical composition of the emitted particles was organic (C, H, O), with negligible black carbon or nitrogen. The emitted particles displayed chemical signatures unique to pyrolysis and notably different from flaming or smoldering wood combustion. The most abundant fragment ions in the mass spectrum were CO<sup>+</sup> and CHO<sup>+</sup>, which together made up 23 % of the total aerosol mass on average, whereas CO<sub>2</sub><sup>+</sup> accounted for less than 4 %, in sharp contrast with ambient aerosol where CO<sub>2</sub><sup>+ </sup>is often a dominant contributor. The mass spectra also showed signatures of levoglucosan and other anhydrous sugars. The fractional contribution of m/z 60, traditionally a tracer for anhydrous sugars including levoglucosan, to total loading (<em>f</em>60) was observed to be between 0.002 and 0.039, similar to previous observations from wild and controlled wood fires. Atomic ratios of oxygen and hydrogen to carbon, O : C and H : C as calculated from AMS mass spectra, varied between 0.41–0.81 and 1.06–1.57, respectively, with individual conditions lying within a continuum of O : C and H : C for wood's primary constituents: cellulose, hemicellulose, and lignin. This work identifies the mass spectral signatures of particle emissions directly from pyrolysis, including f60 and CO<sup>+</sup>/CO<sub>2</sub><sup>+</sup> ratio, through controlled laboratory experiments in order to help understand the importance of pyrolysis emissions in the broader context of wild and controlled wood fires.</p>
Dataset from "A one-pot chitosan pyrolysis in the presence of a ZnCl 2 /NaCl salts for carbons with electrocatalytic activity"
<p>Dataset from manuscript entitled: "A one-pot chitosan pyrolysis in the presence of a ZnCl 2 /NaCl salts for carbons with electrocatalytic activity"</p>
Data from: Chemically distinct particle phase emissions from highly controlled pyrolysis of three wood types
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