Title: Stochastic Analysis In Production Process And Ecology Under Uncertainty
Description: The monograph addresses a problem of stochastic analysis based on the uncertainty assessment by simulation and application of this method in ecology and steel industry under uncertainty. The first chapter defines the Monte Carlo (MC) method and random variables in stochastic models. Chapter two deals with the contamination transport in porous media. Stochastic approach for Municipal Solid Waste transit time contaminants modeling using MC simulation has been worked out. The third chapter describes the risk analysis of the waste to energy facility proposal for Konin city, including the financial aspects. Environmental impact assessment of the ArcelorMittal Steel Power Plant, in Kraków – in the chapter four – is given. Thus, four scenarios of the energy mix production processes were studied. Chapter five contains examples of using ecological Life Cycle Assessment (LCA) – a relatively new method of environmental impact assessment – which help in preparing pro-ecological strategy, and which can lead to reducing the amount of wastes produced in the ArcelorMittal Steel Plant production processes. Moreover, real input and output data of selected processes under uncertainty, mainly used in the LCA technique, have been examined. The last chapter of this monograph contains final summary. The log-normal probability distribution, widely used in risk analysis and environmental management, in order to develop a stochastic analysis of the LCA, as well as uniform distribution for stochastic approach of pollution transport in porous media has been proposed. The distributions employed in this monograph are assembled from site-specific data, data existing in the most current literature, and professional judgment.
Subjects: Stochastic analysis, Ecology, mathematical models, Manufacturing processes, Mathematical models, Ecology, Environmental management, Euthenics, Nature and nurture, Adaptation (Biology), Electrical engineering, Environmental sciences, Statistics, Statistics for Life Sciences, Medicine, Health Sciences, Energy Technology, Economics and Management Energy Policy, Mathematical Modeling and Industrial Mathematics