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7 results for “material flow analysis”
Prospective dynamic and probabilistic material flow analysis of graphene-based materials in Europe from 2004 to 2030
<p>This dataset is related to the following publication:</p> <p>Title: Prospective dynamic and probabilistic material flow analysis of graphene-based materials in Europe from 2004 to 2030</p> <p>Authors: Hyunjoo Hong, Florian Part, Bernd Nowack </p> <p>Submitted to the journal Environmental Science & Technology in June 2022.</p> <p>The files contain an input file and final codes and raw results of the DMFA model for graphene-based materials.</p>
ss-PDMFA: size-specific, probabilistic, dynamic material flow analysis
<p><strong>Description of the dataset:</strong></p> <p>This dataset is related to the following publication:</p> <p>Title: Size-specific, dynamic, probabilistic material flow analysis of titanium dioxide releases into the environment</p> <p>Authors: Yuanfang Zheng, Bernd Nowack </p> <p>Submitted to the journal Environmental Science & Technology in November 2020.</p> <p>The files contain input files, final codes and raw results of the ss-PDMFA model.</p> <p> </p>
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
Material flow analysis of asphalt used in roads for an Austrian municipality
<p>Circular economy gain increasing popularity to ensure a sustainable development. The road construction sector has a major contribution to the circular approach as asphalt has high recycling rates and waste materials have potential application in the production of asphalt mixtures.</p> <p>A study on the asphalt flow in an Austrian municipality was conducted and the underlying material flow analysis model and primary data construction and demolition data of the study are presented. In addition, transfer coefficients and the particle emission model are included. </p>
Development of a flow chamber system for the reproducible in vitro analysis of biofilm formation on implant materials
<p>The data provided are the original data from the microscopic investigation of oral bacterial biofilms. Bacteria were stained with a life/dead staining. Viable cells are represented in red, non-viable cells are shown in green. The biofilms were gained by 50 stacks each with a CLSM. The biofilms were formed in a flow chamber system with a flow velocity of 100µL/min over 24-72 hours. The biofilms were grown on tintanium discs.</p>
Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019)
<p>Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019) by </p> <p><span>Adam R. Mason <sup>1,a</sup>, Tom Bide <sup>2,b</sup>, Junyang Wang <sup>3,c</sup>, John Morley <sup>4,d</sup>, Mohit Arora <sup>5,e</sup>, Alperen Yayla<sup>1,f</sup>, Julia A. Stegemann <sup>5,g</sup>, Rupert J. Myers <sup>1,h,*</sup></span></p> <p><span> </span></p> <p><sup><span>1</span></sup><span> Department of Civil and Environmental Engineering, Imperial College London, UK</span></p> <p><sup><span>2</span></sup><span> British Geological Survey, UK</span></p> <p><sup><span>3 </span></sup><span>Department of Mathematics, Imperial College London, UK</span></p> <p><sup><span>4</span></sup><sub><span> </span></sub><span>Department of Earth Science and Engineering, Imperial College London, UK</span></p> <p><sup><span>5</span></sup><span> School of Engineering, King’s College London, UK</span></p> <p><sup><span>6</span></sup><span> Department of Civil, Environmental and Geomatic Engineering, University College London, UK</span></p> <p><span> </span></p> <p><span>Author e-mails: <sup>a </sup></span><a href="mailto:a.mason19@imperial.ac.uk"><span>a.mason19@imperial.ac.uk</span></a><span>,<sup> b </sup></span><a href="mailto:tode@bgs.ac.uk"><span>tode@bgs.ac.uk</span></a><span>,<sup> c </sup></span><a href="mailto:junyang.wang21@imperial.ac.uk"><span>junyang.wang21@imperial.ac.uk</span></a><span>,<sup> d </sup></span><a href="mailto:john.morley18@imperial.ac.uk"><span>john.morley18@imperial.ac.uk</span></a><span>,<sup> e </sup></span><a href="mailto:mohit.arora@kcl.ac.uk"><span>mohit.arora@kcl.ac.uk</span></a><span>,<sup> f </sup></span><a href="mailto:a.yayla22@imperial.ac.uk"><span>a.yayla22@imperial.ac.uk</span></a><span>,<sup> g </sup></span><a href="mailto:j.stegemann@ucl.ac.uk"><span>j.stegemann@ucl.ac.uk</span></a><span>; *corresponding author:<sup> h</sup> </span><a href="mailto:r.myers@imperial.ac.uk"><span>r.myers@imperial.ac.uk</span></a></p> <p> </p>
Supplementary Material for "Integrating Security-Enriched Data Flow Diagrams Into Architecture-Based Confidentiality Analysis"
<p>Supplementary material for the paper "Integrating Security-Enriched Data Flow Diagrams Into Architecture-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://dataflowanalysis.org</p>
ScienceDex guides
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