Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery
According to the European Commission’s Report on Critical Raw Materials and the Circular Economy, the raw materials, such as rare earths, have a high economic importance for the EU, and are essential for the production of a broad range of goods and applications used in everyday life, as well as they...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/16/e3sconf_lcm2022_01013.pdf |
| _version_ | 1818534045765599232 |
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| author | Sala Dariusz Bieda Bogusław |
| author_facet | Sala Dariusz Bieda Bogusław |
| author_sort | Sala Dariusz |
| collection | DOAJ |
| description | According to the European Commission’s Report on Critical Raw Materials and the Circular Economy, the raw materials, such as rare earths, have a high economic importance for the EU, and are essential for the production of a broad range of goods and applications used in everyday life, as well as they are crucial for a strong European industrial base. Uncertainty plays an important role in the real world used Life Cycle Assessment (LCA) approach. The validity of LCA depends strongly on the significance of the input data. Data uncertainty is often mentioned as a crucial limitation for a clear interpretation of LCA results. The stochastic modelling used for Monte Carlo (MC) analysis simulation was reported in order to assess uncertainty in life cycle inventory (LCI) of rare earth elements (REEs) recovery. The purpose of this study was REEs recovery from secondary sources analysed in the ENVIREE ERA-NET ERA-MIN-funded research project. The software Crystal Ball® (CB) program, associated with Microsoft® Excel, was used for the uncertainties analysis. Uncertainty of data can be expressed through a definition of probability distribution of those data. The output report provided by CB, after 10000 runs is reflected in the frequency charts and summary statistics. The analysed parameters were assigned with lognormal distribution. The uncertainty analysis offers a well-defined procedure for LCI studies, and provides the basis for defining the data needs for full LCA of the REEs beneficiation process. Results can improve current procedures in the REEs beneficiation process management and bring closer to industrial application through the involvement of end users. |
| first_indexed | 2024-12-11T18:06:39Z |
| format | Article |
| id | doaj.art-9fe31b5c8d6241ae97e1f08e018de06a |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-11T18:06:39Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-9fe31b5c8d6241ae97e1f08e018de06a2022-12-22T00:55:43ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013490101310.1051/e3sconf/202234901013e3sconf_lcm2022_01013Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recoverySala Dariusz0Bieda Bogusław1AGH University of Science and Technology in Kraków, Faculty of ManagementAGH University of Science and Technology in Kraków, Faculty of ManagementAccording to the European Commission’s Report on Critical Raw Materials and the Circular Economy, the raw materials, such as rare earths, have a high economic importance for the EU, and are essential for the production of a broad range of goods and applications used in everyday life, as well as they are crucial for a strong European industrial base. Uncertainty plays an important role in the real world used Life Cycle Assessment (LCA) approach. The validity of LCA depends strongly on the significance of the input data. Data uncertainty is often mentioned as a crucial limitation for a clear interpretation of LCA results. The stochastic modelling used for Monte Carlo (MC) analysis simulation was reported in order to assess uncertainty in life cycle inventory (LCI) of rare earth elements (REEs) recovery. The purpose of this study was REEs recovery from secondary sources analysed in the ENVIREE ERA-NET ERA-MIN-funded research project. The software Crystal Ball® (CB) program, associated with Microsoft® Excel, was used for the uncertainties analysis. Uncertainty of data can be expressed through a definition of probability distribution of those data. The output report provided by CB, after 10000 runs is reflected in the frequency charts and summary statistics. The analysed parameters were assigned with lognormal distribution. The uncertainty analysis offers a well-defined procedure for LCI studies, and provides the basis for defining the data needs for full LCA of the REEs beneficiation process. Results can improve current procedures in the REEs beneficiation process management and bring closer to industrial application through the involvement of end users.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/16/e3sconf_lcm2022_01013.pdf |
| spellingShingle | Sala Dariusz Bieda Bogusław Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery E3S Web of Conferences |
| title | Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery |
| title_full | Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery |
| title_fullStr | Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery |
| title_full_unstemmed | Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery |
| title_short | Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery |
| title_sort | stochastic approach based on monte carlo mc simulation used for life cycle inventory lci uncertainty analysis in rare earth elements rees recovery |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/16/e3sconf_lcm2022_01013.pdf |
| work_keys_str_mv | AT saladariusz stochasticapproachbasedonmontecarlomcsimulationusedforlifecycleinventorylciuncertaintyanalysisinrareearthelementsreesrecovery AT biedabogusław stochasticapproachbasedonmontecarlomcsimulationusedforlifecycleinventorylciuncertaintyanalysisinrareearthelementsreesrecovery |