- •Summary
- •Introduction
- •Contents
- •List of Main Symbols
- •2.1 Introduction
- •2.2 Aim and Scope of the Project
- •2.3 Activating the Model: Simulation
- •2.4 The Results of the Simulation
- •2.4.1 Sensitivity Analysis
- •2.5 The Results
- •2.6 Summary and Conclusion
- •3.1 Introduction
- •3.2 Risk in Waste Management (Environmental Protection) in European Union and International Legislation
- •3.4 Developing the Model
- •3.6 Activating the Model: The Results of the Simulation
- •3.7 Summary and Conclusion
- •4.1 Introduction
- •4.2 Origin and Development of the LCA Method
- •4.4 Uncertainty and Random Variables in LCA Research
- •4.5 Types of Random Variables in Uncertainty Analysis in LCA Studies
- •4.6.1 Aim and Scope of the Project
- •4.8 Description of the Functional Unit of the Boundary System of the Performed Analysis: Inventory Analysis
- •4.9 The Life Cycle Impact Assessment LCA
- •4.12 The Results of the Simulation
- •4.13 Sensitivity Analysis
- •4.13.1 Tornado Chart
- •4.13.2 Spider Chart
- •4.14 Summary and Conclusion
- •5.1 Introduction
- •5.2 Characterisation of Waste Management in the Discussed Facilities
- •5.2.1 The Coke Production Facility: Coke Plant
- •5.2.2 The Ore Sintering Facility: Sintering Plant
- •5.2.3 The Pig Iron Melting Facility: Blast Furnaces
- •5.2.4 The Steel Melting Facility: Converter Plant
- •5.2.5 The Continuous Steel Casting Facility: CSC
- •5.2.6 The Facility for Hot Rolling of Ferrous Metals: Hot Strip Mill
- •5.3 Aim and Scope of the Analysis
- •5.4 Waste Management Balance, Analysis Assumptions
- •5.5 The Life Cycle Impact Assessment: Interpretation
- •5.6 The Analysis of the Results
- •5.7 Stochastic Analysis as an Uncertainty Calculation Tool in the LCA Study
- •5.8 The Results of the Simulation
- •5.9 Sensitivity Analysis
- •5.10 The Results of the Simulation
- •5.11 Sensitivity Analysis
- •5.12 Summary and Conclusion
- •6.1 General Conclusion
- •Bibliography
6.1 General Conclusion |
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the problem of uncertainty in LCA studies. The author of this monograph expects this kind of practical and creative application in other technological processes to take place.
Numerical stochastic analysis has been rapidly developing as well, as more powerful computers become available. The focus here has been on the more general, constructive methods of obtaining information regarding stochastic processes with log-normal distributions.
To sum up, a statement may be made, to quote after Snopkowski (2007), that stochastic simulation allows to answer the question of what happens to a process (and its chosen features) if different conditions in its course do occur? Many a time a situation occurs when stochastic simulation is the only research method that makes it possible to find an answer to such a formed question.
It needs to be said that this monograph would have been impossible to complete without the help of, and the fruitful collaboration with, the MSP’s Department of Environmental Protection.
The conclusions have been included in the summaries of individual chapters of this thesis.
6.1General Conclusion
The aim of the thesis was to present and emphasise the versatility of Monte Carlo method in the assessment of uncertainty in stochastic analysis of chosen manufacturing processes and ecology. The interdisciplinary nature of the monograph means that the following aspects need to be linked together:
•The technical aspects – the stochastic model of the diffusion of polluting substances applied in the management of landfills, by using MC simulation, makes the simulation of contaminant transport more detailed in comparison to the simulation based on transportation models available to date, resulting in a better, more practical, assessment of the current state. This is of practical importance in the case of measuring the range of safety zones surrounding industrial plants, landfills, or ground water intakes.
•The ecological aspects – the application of LCA techniques offers important and notable benefits (e.g. of financial nature) to industrial companies or service providers who are interested in limiting the negative environmental impact caused by their activity.
•The economic aspects – the stochastic analysis of investment decisions is a valuable addition to the process of searching for solutions to financial questions regarding investment management, in situations where typical assessment methods cannot provide explicit answers.
The connection made between the manufacturing processes and the management of the LCA technique may be perceived as a methodological goal that has been achieved. The fact that the application of LCA, a technique that is still under
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6 Summary |
development, in the assessment of impact of manufacturing processes on natural environment, which has been included in the research methodology, constitutes a significant progress in relation to the analyses that have been used so far.
The data and parameter values present in the analysis have been determined mainly on the basis of in situ measurements.
The stochastic analyses of manufacturing processes, based on the steel industry case study, and ecology, using Monte Carlo method, presented in this monograph, can be, according to the author, an effective tool supporting not only the environmental management under uncertainty, but also the interpretation of results in environmental economy and engineering.