AGH University of Science and Technology, Faculty of Management, al. Mickiewicza 30, 30-059 Kraków, Poland.
Sci Total Environ. 2014 May 15;481:649-55. doi: 10.1016/j.scitotenv.2013.10.123. Epub 2013 Nov 28.
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.
本文旨在介绍基于蒙特卡罗（MC）模拟的随机方法在波兰克拉科夫米塔尔钢铁波兰（MSP）综合设施生命周期清单（LCI）数据中的应用结果。为了评估不确定性，使用了与 Microsoft Excel 电子表格模型相关联的 CrystalBall®（CB）软件。该研究的框架最初是在 2005 年进行的。对 MSP 2005 年收集的钢、焦炭、生铁、烧结矿、连铸板坯（CSC）、热轧机（HRM）和高炉煤气的总产量进行了分析，并用于 LCI 模型的 MC 模拟。为了描述本研究中使用的所有主要产品的随机性质，应用了正态分布。使用 CB 进行的模拟（10,000 次试验）的结果包括频率图表和统计报告。本研究的结果可用作钢铁行业进行全生命周期分析（LCA）的第一步。此外，研究结论表明，随机方法是量化 LCA/LCI 研究中参数不确定性的有力方法，可应用于任何钢铁行业。本研究的结果可以帮助钢铁生产管理人员中的从业者和决策者。
