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

Abstract

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.