Stochastic Assessment by Monte Carlo Simulation for LCI applied to steel process chain: The Mittal Steel Poland (MSP) S.A. in Kraków, Poland case study
|The aim of the study is to use of a stochastic assessment by MC Simulation for LCI applied to steel process chain of the MSP S.A. in Kraków, Poland case study and to promote the use of uncertainty estimation as routine in environmental science. The functional unit in this study, central concept in LCA, 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”. The economic and social criteria and indicators will not further be discussed in this paper. The goals of this study is also: • produce national et regional LCI data for energy generating industry, • promote the development of LCI and /or LCA research and application in Poland. The study comprises the inventory corresponding to the all process stages including the Coke Plant, Iron Blast Furnace, Sintering Plant, Blast Oxygen Furnace (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, central concept in LCA, 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”. System boundaries of this study does not include the manufacture of downstream products, their use, end of life. For MSP power plant, mining and transportation of raw coal, crude oil and natural gas were not included. In this study only the following substances: dolomite, limestone, ferroalloys, pig iron (raw material) , blast furnace gas, electric energy, hard coal, water, land using, blast furnace gas, slag, pig iron (main product), steel, slabs, coke, and the atmospheric emission of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), cadmium (Cd), lead (Pb), carbon dioxide (CO2) have been taken in account for the simulation. The probability distributions for the hard coal, blast furnace gas, coke oven gas and natural gas were considered to be normal with coefficient of variation (CV) of 0.10. The probability distributions for the lubricant oil was considered to be normal with CV of 0.1. The proper determination of the log-normal probability distributions in the case of SO2 (emissions), CO (emissions), NO2 (emissions), Cr, Cd, data with a geometric standard deviation (σg) between 1.5 and 2.2. The probability distributions had to be derived from Crystall Ball® (CB) analysis experimental results. Confidence level is specify to 95%. The use of stochastic model helps to characterize the uncertainties better, rather than pure analytical mathematical approach. 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).
|EnviroInfo Dessau 2012, Part 2: Open Data and Industrial Ecological Management
|Stochastic Assessment by Monte Carlo Simulation for LCI applied to steel process chain: The Mittal Steel Poland (MSP) S.A. in Kraków, Poland case study
|ICT for Life Cycle Assessment