Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Life Cycle Inventory (LCI) Stochastic Approach Used for Rare Earth Elements (REEs), Considering Uncertainty

Sala Dariusz, Bieda Bogusław

Inżynieria Mineralna

2021

no. 2

283--291

The purpose of the paper is to present the results of the stochastic modelling with uncertainty performed with the use of Monte Carlo (MC) simulation with 10,000 cycles and a confidence interval of 95 %, as recommended. Analysed REEs were fitted by lognormal distributions by using the Crystal Ball® (CB) spreadsheet-based software after defining the geometric mean value (μg) and the standard deviation (σg), automatically calculated (matches) the lower, as well as, upper boundaries of lognormal distribution. The number of replications of a simulation affects the quality of the results. The principal output report provided by CB and presented in this study consists of the graphical representation in the form of the frequency chart, percentiles summary, and statistics summary. Additional CB options provide a sensitivity analysis with tornado diagrams. The data that was used for MC simulation of the LCI model includes available and published data concerning associated with the REEs. This paper discusses the results and show that the adopted approach is applicable for any REEs used in the LCI studies under uncertainty. The results obtained from this study can be used as the first step in performing a full LCA analysis and help practitioners as well as decision-makers in the environmental engineering and management.

Application of uncertainty analysis based on Monte Carlo (MC) simulation for life cycle inventory (LCI)

Sala Dariusz, Bieda Bogusław

Inżynieria Mineralna

2019

R. 21, nr 2/2

264--269

The use of Monte Carlo (MC) simulation was presented in order to assess uncertainty in life cycle inventory (LCI) studies. The MC method is finded as an important tool in environmental science and can be considered the most effective quantification approach for uncertainties. Uncertainty of data can be expressed through a definition of probability distribution of that data (e.g. through standard deviation or variance). The presented case in this study is based on the example of the emission of SO2, generated during energy production in Integrated Steel Power Plant (ISPP) in Kraków, Poland. MC simulation using software Crystal Ball® (CB), software, associated with Microsoft® Excel, was used for the uncertainties analysis. The MC approach for assessing parameter uncertainty is described. Analysed parameter (SO2,) performed in MC simulation were assigned with log-normal distribution. Finally, the results obtained using MC simulation, after 10,000 runs, more reliable than the deterministic approach, is presented in form of the frequency charts and summary statistics. Thanks to uncertainty analysis, a final result is obtained in the form of value range. The results of this study will encourage other researchers to consider this approach in their projects, and the results of this study will encourage other LCA researchers to consider the uncertainty in their projects and bring closer to industrial application.