5.12Summary and Conclusion

during the analysis are not adequate to Polish, but to European conditions, which may generate inaccurate or even incorrect results, should be taken into consideration here (this is the case, for example, when it comes to the emission of radon222, which is practically absent in the coal burned in MSP Power Plant. This coal, as is mentioned in Chap. 4.13, which comes from the mines that belong to Katowicki Holding We˛glowy, a coal producer based in Katowice, should not contain radon-222. Yet, the electric energy produced in MSP Power Plant does not cover the total needs of the mentioned industrial complex. The missing energy is bought elsewhere – see Chap. 5.2).

The performed life cycle analysis of waste management has, in addition, made it possible to:

•Identify waste, whose contribution to the total level of influence is the biggest. The greatest potential environmental strain (approximately 63%) is caused by the storage of slags. The less straining impacts are caused by the storage of hazardous waste (27.7%) as well as coal fly-ashes and slag-ash mixtures (5.4%). The influence of the remaining types of waste does not exceed 3.5% (Fig. 5.9),

•Identify waste, whose contribution to the 11 impact categories is the biggest. In the majority of cases, the factor that is the main potential environmental strain is the produced hazardous waste,

•Identify environmental benefits – by analysing the land use impact category (see Fig. 5.1 and Table 5.4), the negative level of environmental influence (the darker cells in Table 5.4) that deals with the remaining waste from iron, steel, scrap and silt, paper and cardboard, as well as water decarbonisation sediments, may be noticed. This is caused by the utilisation and recovery of the products that bring environmental benefits.

The results of the LCA analysis, presented in Table 5.5, which include the total life cycle impact of waste management in an annual cycle, for the examined functional unit, expressed in eco-points and amounting to 24.3 Mpt, are employed here with a view to presenting the stochastic analysis of life cycle waste management in MSP.

The stochastic analysis, used to calculate uncertainty of the six impact categories (carcinogenic agents, respiratory system – organic compounds, respiratory system – non-organic compounds, climate change, radiation, and ozone layer), whose sum, 232.26 DALY, forms the human health damage category, has been performed on the basis of data included in Table 5.8. The results of the analysis, presented in graphic form and in the form of reports, are shown in Figs. 5.11, 5.12, 5.13, 5.14, 5.15, 5.16 and 5.17 and in Figs. 5.20, 5.21, 5.22, 5.23, 5.24 and 5.25.

The LCA analysis has been performed, for the purposes of the postdoctoral thesis, by the Mineral and Energy Economy Research Institute of the Polish Academy of Sciences (PAN), in Krako´w (Ocena 2009). It contains the result expressed in the form of: characterisation, normalisation, and measurement stage results – values of which are given in eco-points Pt for individual impact categories. The analysis has been performed in accordance with the international standards PN-EN ISO 14040:2006 (Environmental management – Life cycle assessment –

Principles and framework) and PN-EN ISO 14044:2006 (Environmental management – Life cycle assessment – Requirements and guidelines). The material-energy balance (LCI) is based on data provided by the AMPSAK industrial complex (Wniosek 2006).

After a detailed study of the available subject literature (see Chap. 4.4), an assumption has been made, based on the Distribution Gallery tab found in Crystal Ball program, that the simulation should employ log-normal probability distributions with a geometric standard deviation of sg ¼ 1.2 for all parameters of the analysis.

To summarise, normalisation indicates the relative extent of the influences’ impact; however, if the gravity of the influences is to be presented, it is necessary to perform measurements – this means that the results need to be converted by applying weight coefficients that are equivalent to the severity of the influence (Adamczyk 2004). The measurement procedure has always been considered as controversial not only due to its subjectivity, but also because it involves social, political, and ethical values (Finnvenden 1997). Nevertheless, it is widely applied in practice, despite the controversies (Hansen 1999). The wider debate on the subject of measurement can be found in Finnvenden et al. (2002), Kowalski et al. (2007), Simapro (2007).

more interest. The reliability of LCA results may be uncertain, to a certain degree, but this uncertainty can be noticed with the help of Monte Carlo method, for instance. This methodology has not yet been used in Polish steel industry and this is one of the reasons why this monograph discusses the LCA method so extensively (Chaps. 4 and 5). As is mentioned in the introduction to Chap. 4 (4.1), the International Iron and Steel Institute in Brussels, Belgium, in 2002 undertook a study focusing on data inventory, based on the material-energy balance in the Life Cycle Inventory (LCI) procedure, on the basis of data gathered from 28 steel plants, excluding the steel power plants from the Mittal Steel Group – a fact that is mentioned in the supplementary paper to the study (IISI 2002).

It has been proven that (1) log-normal distribution can be practically applied in the assessment of impacts on the quality of natural environment of manufacturing processes, e.g. in steel industry, (2) uniform distribution can be practically applied in the analysis or risk investment, and, finally, (3) log-normal and uniform distribution can be used in modelling of waste propagation in the management of environmental quality (the transport model of polluting substances in homogeneous porous media). When analysing the subject literature describing the application of uncertainty analysis, the work of Pappenberger and Beven (2006), on the modelling of hydraulic and hydrological phenomena under uncertainty, can attract one’s attention for the authors provide seven reasons as to why uncertainty analysis should not be used. First of all, a group of modellers exist who believe that their physical models are (or will be) of static nature, i.e. time is of no relevance. They claim that all parameters, boundary conditions, etc., can be defined a priori, and so, uncertainty analysis is not necessary. Moreover, another group of researchers state that it is enough to alter the model’s parameters in the strictly defined range. Second of all, the testing of models can be conducted using non-statistical methods, by eliminating the models that cannot deliver satisfactory results. Thirdly, many researchers claim that the decision-makers and the management personnel are not properly mathematically prepared to carry out their duties, as the notions of risk and uncertainty are understood differently and are oftentimes confused. Furthermore, uncertainty cannot be integrated into decision-making processes that on many occasions are binary. In addition, uncertainty analysis should be disregarded due to its excessive subjectivity and the difficulty with which it is performed. Lastly, the seventh, and final reason emphasises the lack of real impact of uncertainty on the process of reaching the final decision.

The author hopes that the monograph will be helpful in explaining the problems of stochastic analysis to students (at postgraduate level), scientific researchers, and to industry managers. This thesis is of methodical nature, and the simulation results presented here are of cognitive and applied importance. It is the intention of the author to continue the cooperation with other domestic and international scientific centres (e.g. with the EU Joint Research Centre, in Italy, where the discussion regarding SimLab® program took place, in which the author participated during his visit).

It may seem that the proposed problems concerning the application of computer simulation techniques in stochastic analyses, alongside the cognitive values, can also constitute a practical tool, which may make it possible to explain, for example,