Bogusław Bieda

Application of stochastic approach based on Monte Carlo (MC) simulation for life cycle inventory (LCI) to the steel process chain: Case study

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.

Stochastic approach to municipal solid waste landfill life based on the contaminant transit time modeling using the Monte Carlo (MC) simulation

The paper is concerned with application and benefits of MC simulation proposed for estimating the life of a modern municipal solid waste (MSW) landfill. The software Crystal Ball® (CB), simulation program that helps analyze the uncertainties associated with Microsoft® Excel models by MC simulation, was proposed to calculate the transit time contaminants in porous media. The transport of contaminants in soil is represented by the one-dimensional (1D) form of the advection-dispersion equation (ADE). The computer program CONTRANS written in MATLAB language is foundation to simulate and estimate the thickness of landfill compacted clay liner. In order to simplify the task of determining the uncertainty of parameters by the MC simulation, the parameters corresponding to the expression Z2 taken from this program were used for the study. The tested parameters are: hydraulic gradient (HG), hydraulic conductivity (HC), porosity (POROS), linear thickness (TH) and diffusion coefficient (EDC). The principal output report provided by CB and presented in the study consists of the frequency chart, percentiles summary and statistics summary. Additional CB options provide a sensitivity analysis with tornado diagrams. The data that was used include available published figures as well as data concerning the Mittal Steel Poland (MSP) S.A. in Kraków, Poland. This paper discusses the results and show that the presented approach is applicable for any MSW landfill compacted clay liner thickness design.

Decision support systems based on the Life Cycle Inventory for Municipal Solid Waste management under uncertainty

Life Cycle Inventory (LCI) – part of a Life Cycle Assessment (LCA) method – was used to evaluate two scenarios for waste to energy gasification plants based on the American (design at 200 T/D) and Australian (design at 240 T/D) gasification technologies including a 15-year income statement projection. The paper uses stochastic modeling based on the internal rate of return (IRR) and net present value (NPV) values of the new and actual equipment. The Monte Carlo simulation with the Excel spreadsheet and Crystal Ball® software was used to develop scenarios for uncertainty inputs. The sensitivity analysis and frequency charts represent the Crystal Ball® output and simulation results.

Linear Programming and Risk Analysis Methods for Municipal Solid Waste Decision Support System

Abstract Mathematical modelling is used to manage the Municipal Solid Waste (MSW) This paper develops a linear programming (LP) model for solid waste management systems. The integration of the Mathematical Programming Model and Risk Analysis Methods is demonstrated through application to MSW management systems in Niepolomice town, not far from Krakow, Poland. Experimental results and the simulation values are obtained through using Cris tall Ball®, a forecasting and risk analysis program for effective remediation of contaminated soil and waste landfill.

Stochastic Analysis of the Environmental Impact of Energy Production Processes, Based on the Example of MSP Power Plant

This chapter deals with the application of the stochastic method, used to analyse the environmental impact of the manufacturing processes, namely the energy production in the AMPSAK Power Plant. The quantitative analysis of uncertainty of this kind has been proposed, based on the case of comparative analysis of four scenarios of the power plant’s annual work cycle, taking into consideration that the scenarios differ only in the change of proportioning ratios of the two types of fuels: hard coal and blast furnace gas (the remaining fuels, such as natural gas and coke oven gas are left at their current levels – they are used as start-up gas, owing to their higher heating value). The MC methodology, because of its stochastic nature, has been applied for the quantitative analysis (Heermann 1997). There is little mention, in the subject literature, of research carried out in the area of the application of stochastic analysis in the manufacturing industry, let alone steel industry. In the work of Marice et al. (2000) an effort is made to apply the stochastic method in the Life Cycle Inventory analysis (LCI) in order to evaluate uncertainty of cumulated emissions and necessary materials to conduct the assessment of, e.g., the influence of the energy produced in coal power plants.

Stochastic Analysis, Using Monte Carlo (MC) Simulation, of the Life Cycle Management of Waste, from an Annual Perspective, Generated by MSP

This chapter deals with the application of the MC technique, of stochastic nature, in the description of negative effects of waste produced by AMPSAK facilities on the environment. The ecological life cycle assessment of waste management from an annual perspective has been conducted on the basis of the computer-assisted LCA method. 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 Krakow (Ocena 2009). The analysis has been compiled by using the generated waste’s balance. The findings are expressed in the form of: characterisation, normalisation, and measurement stage results. The analysis has been conducted, similarly to what is described in Chap. 4, in accordance with PN-EN ISO 14040:2006 and PN-EN ISO 14044:2006 series.

The Role of Risk Assessment in Investment Costs Management, Based on the Example of Waste Treatment (Gasification) Facility in the City of Konin

The technology behind converting, disposal, and destruction of waste is constantly being modernised. The United States Environmental Protection Agency (EPA) sponsors competitions and finances a considerable number of innovative scientific-research studies in this field. As a result, various project ideas can be realised and the most interesting solutions can be turned into real technology, thanks to EPA funding. In Poland, the Article 1 of the Waste Management Act of 27 April (Official Journal ‘Dz. U.’ No. 62 2001) and the Directives 91/156/EEC, 91/689/EEC, and 94/67/EEC of the European Parliament and of the Council (Dyrektywa 2010), state the rules regarding waste procedures which ensure human life and health safety, as well as environmental protection, in accordance with the rules of sustainable development, and especially the rules establishing how waste production can be avoided, or rules limiting the amount of waste and its negative impact on the environment, as well as the waste recovery or waste neutralisation rules.

Stochastic Model of the Diffusion of Pollutants in Landfill Management Using Monte Carlo Simulation

Hazardous waste landfills, as well as landfills for other than hazardous or inert waste, require the application of technical solutions that comply with the Regulation of the Minister of Environment of 24 March 2003 on the detailed requirements regarding the location, construction, operation and closure, that should to be met by the particular types of landfills (D.U. 2003) (Official Journal “Dz. U.” No. 61, item 549). In line with the requirements of the abovementioned regulation, it is necessary to isolate the deposited waste from the subsoil with a natural geological barrier. This applies to the other than hazardous or inert waste with the thickness no less than 1 m (for the hazardous waste it is 5 m) and the filtration coefficient (diffusion) k ≤ 1.0 × 109 m/s. If artificial geological barrier is to be used, its thickness cannot be less than 0.5 m and the permeability cannot be greater than that of the natural barrier. Synthetic isolation needs to supplement the natural or artificial geological barrier, depending on which one is used. The shape of the basin needs to make it impossible for the precipitation water from the surrounding area to flow into the basin. A drainage system should be built at the bottom and on the slopes of the landfill that would ensure its reliable functioning during the service life of the landfill and during the period of 30 years after its closure. Uncertainty can be described with the help of parameters such as variance (informing about the distribution of a random variable value) or standard deviation, or with the help of other statistical methods, e.g. the MC method. The employment of MC simulation for the modelling of propagation delay of waste in porous media is a very useful tool that can be used to assess the life cycle of a modern landfill.

Introduction to Monte Carlo (MC) Method: Random Variables in Stochastic Models

According to its definition, stochastic simulation model should contain at least one random variable (Snopkowski 2007). Random variable, being a numerical representation of the outcome of a random experiment, is a key term in statistical analysis (Baranska 2008) and, as observed by Snopkowski (2007), is an essential element of every stochastic simulation. In literature, there are a number of different definitions of a random variable. Stanisz (2006) defines random variable as a “function determined on an elementary event space, which assigns a real number with defined probability to every elementary event. Therefore, this value cannot be predicted in advance, as it depends on a random event.” A similar definition is provided by Baranska (2008). As claimed by Benjamin and Cornell (1977), random variable is “a variable that assumes numerical values whose outcome cannot be predicted with complete certainty.” Bobrowski (1980), on the other hand, defines random variable as “a variable that, as a result of an experiment, can assume, with defined probability, one of the values of a certain set of real numbers”, and Aczel (2000) states that “random variable is a variable whose assumed values depend on chance”. Sokolowski (2004), however, apart from quoting a popular definition of random variable, analyses the instances of carelessness and errors that he has encountered in many other studies, regarding random variables.