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首页> 外文期刊>E3S Web of Conferences >Application of stochastic approach based on Monte Carlo (MC) simulation for life cycle inventory (LCI) of the rare earth elements (REEs) in beneficiation rare earth waste from the gold processing: case study
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Application of stochastic approach based on Monte Carlo (MC) simulation for life cycle inventory (LCI) of the rare earth elements (REEs) in beneficiation rare earth waste from the gold processing: case study

机译：基于Monte Carlo（MC）模拟的随机方法在稀土元素（REES）中的生命周期库存（LCI）的应用，稀土稀土废物中的稀土稀土废料：案例研究

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摘要

The study proposes an stochastic approach based on Monte Carlo (MC) simulation for life cycle assessment (LCA) method limited to life cycle inventory (LCI) study for rare earth elements (REEs) recovery from the secondary materials processes production applied to the New Krankberg Mine in Sweden. The MC method is recognizes as an important tool in science and can be considered the most effective quantification approach for uncertainties. The use of stochastic approach helps to characterize the uncertainties better than deterministic method. Uncertainty of data can be expressed through a definition of probability distribution of that data (e.g. through standard deviation or variance). The data used in this study are obtained from: (i) site-specific measured or calculated data, (ii) values based on literature, (iii) the ecoinvent process ?rare earth concentrate, 70% REO, from bastn?site, at beneficiation”. Environmental emissions (e.g, particulates, uranium-238, thorium-232), energy and REE (La, Ce, Nd, Pr, Sm, Dy, Eu, Tb, Y, Sc, Yb, Lu, Tm, Y, Gd) have been inventoried. The study is based on a reference case for the year 2016. The combination of MC analysis with sensitivity analysis is the best solution for quantified the uncertainty in the LCI/LCA. The reliability of LCA results may be uncertain, to a certain degree, but this uncertainty can be noticed with the help of MC method.
机译：该研究提出了一种基于Monte Carlo（MC）模拟的随机方法，用于生命周期评估（LCA）方法限于生命周期库存（LCI）的稀土元素（RCI）研究，从二级材料的过程中恢复应用于新的Krankberg我在瑞典。 MC方法被认为是科学中的重要工具，可以被认为是最有效的不确定性的量化方法。随机方法的使用有助于表征比确定性方法更好的不确定性。数据的不确定性可以通过该数据的概率分布的定义来表示（例如，通过标准偏差或方差）。本研究中使用的数据从：（i）特定于现场测量或计算的数据，（ii）基于文献的价值，（iii）生态工艺？稀土浓缩物，70％reo，来自bastn？网站，在受益者“。环境排放（例如，颗粒，铀-238，钍-232），能量和ree（la，ce，nd，pr，sm，dy，eu，tb，y，sc，yb，lu，tm，y，gd）已被清点。该研究基于2016年的参考案例。MC分析与敏感性分析的组合是用于量化LCI / LCA中的不确定性的最佳解决方案。 LCA结果的可靠性可能不确定，到一定程度，但可以在MC方法的帮助下注意到这种不确定性。

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