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Auflistung nach Autor:in "Bieda, Boguslaw"

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  • 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
    (Innovations in Sharing Environmental Observations and Information, 2011) Bieda, Boguslaw
    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
  • Konferenzbeitrag
    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
    (EnviroInfo Dessau 2012, Part 2: Open Data and Industrial Ecological Management, 2012) Bieda, Boguslaw
    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).