Konferenzbeitrag
Stochastic Assessment by Monte Carlo Simulation for LCI applied to steel process chain: The ArcelorMittal Steel Poland S.A. in Krakow, Poland case study
Vorschaubild nicht verfügbar
Volltext URI
Dokumententyp
Text/Conference Paper
Zusatzinformation
Datum
2011
Autor:innen
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Verlag
Shaker Verlag
Zusammenfassung
The aim of the paper is stochastic approach for LCA/LCI probabilistic conception with uncorrelated input/output data in steel process chain with six processes (including Coke Plant, Iron Blast Furnace, Sintering Plant, BOF, Continuous Steel Casting and Hot Rolling Mill) applied to ArcelorMittal Steel Poland (AMSP) S.A. in Krakow, Poland case study. Uncertainty assessment in LCI is based on a Monte Carlo (MC) simulation with the Excel spreadsheet and CrystalBall® (CB) software was used to develop scenarios for uncertainty inputs. The economic and social criteria and indicators will not further be discussed in this paper. The framework of the study was originally carried out for 2005 data because important statistics are available for this year and also because it represents the data, which are the foundation for the Environmental Impact Report of the AMSP, annually collected (2005) and evaluated. The study comprises the inventory corresponding to the all process stages including the Coke Plant, Iron Blast Furnace, Sintering Plant, BOF, Continuous Steel Casting and Hot Rolling Mill. The complete inventory was integrated by main environmental loads (inputs, outputs): energy and raw materials consumed, wastes produced, and emissions to air, water and soil. The functional unit in this study is defined as “steel process chain includes all activities linked with steel production from Coke Plant and Sinter Plant to Hot Rolling Mill in 2005”. In this study only the following substances: hard coal, blast furnace gas, coke oven gas, natural gas, lubricant oil and the atmospheric emission of sulfur (S), cadmium (Cd), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), chloridric acid (HCL), chromium (Cr) nickel (Ni), sulfur dioxide (SO2), manganese (Mn), cooper (Cu), lead (Pb) have been taken in account. LCA/LCI data are full of uncertain numbers. The benefits of Monte Carlo simulation are saving in time and resources. CB eliminates the need to run, test, and present multiple spreadsheets. Simulation models are generally easier to understand than many analytical approaches. Monte Carlo analysis generates a mean value and upper and lower boundary value for each LCI exchange. The created inventories using the probabilistic approach facilitate the environmental damage estimations for industrial process chains with complex number of industrial processes (e.g. steel production). Consequently, MC analysis is a power full method for quantifying parameter uncertainty in LCA studies