title: Application of stochastic approach based on Monte Carlo (MC) simulation for life cycle inventory (LCI) to the steel process chain: case study.
abstract: The purpose of the paper is to present the results of application of stochastic approach based on Monte Carlo (MC) simulation for life cycle inventory (LCI) data of Mittal Steel Poland (MSP) complex in Kraków, Poland. In order to assess the uncertainty, the software CrystalBall® (CB), which is associated with Microsoft® Excel spreadsheet model, is used. The framework of the study was originally carried out for 2005. The total production of steel, coke, pig iron, sinter, slabs from continuous steel casting (CSC), sheets from hot rolling mill (HRM) and blast furnace gas, collected in 2005 from MSP was analyzed and used for MC simulation of the LCI model. In order to describe random nature of all main products used in this study, normal distribution has been applied. The results of the simulation (10,000 trials) performed with the use of CB consist of frequency charts and statistical reports. The results of this study can be used as the first step in performing a full LCA analysis in the steel industry. Further, it is concluded that the stochastic approach is a powerful method for quantifying parameter uncertainty in LCA/LCI studies and it can be applied to any steel industry. The results obtained from this study can help practitioners and decision-makers in the steel production management.
authors.affiliation: AGH University of Science and Technology, Faculty of Management, al. Mickiewicza 30, 30-059 Kraków, Poland.
authors.forename: Bogusław
authors.initials: B
authors.lastname: Bieda
mesh_terms: D003247:Conservation of Natural Resources / Q000379:methods*; D003657:Decision Making; D004784:Environmental Monitoring / Q000379:methods*; D008669:Metallurgy / Q000379:methods*; D015233:Models, Statistical; D009010:Monte Carlo Method; D011044:Poland; D012984:Software; D013232:Steel; D035501:Uncertainty
chemical_list: D013232:Steel
keywords: Crystal Ball®; Life cycle assessment (LCA); Life cycle inventory (LCI); Monte Carlo simulation; Poland; Stochastic approach