Szczegóły publikacji
Opis bibliograficzny
Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery / Dariusz SALA, Bogusław BIEDA // E3S Web of Conferences [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2267-1242. — 2022 — vol. 349 art. no. 01013, s. 1–6. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 6, Abstr. — Publikacja dostępna online od: 2022-05-20. — 10th international conference on Life Cycle Management (LCM 2021) : 1–8 September 2021, Stuttgart, Germany
Autorzy (2)
Dane bibliometryczne
| ID BaDAP | 140240 |
|---|---|
| Data dodania do BaDAP | 2022-05-25 |
| Tekst źródłowy | URL |
| DOI | 10.1051/e3sconf/202234901013 |
| Rok publikacji | 2022 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | E3S Web of Conferences |
Abstract
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.
