3rd International Conference on Life Cycle Management Zurich, University of Zurich at Irchel August 27 to 29, 2007 Switzerland
3rd International Conference on Life Cycle Management Application of Life Cycle Inventory (LCI) and Life Cycle Management (LCM) methods as useful tools for Municipal Solid Waste (MSW) Management scenario analysis under Uncertainty Dr. Boguslaw Bieda AGH-University of Science and Technology Management Department Krakow - Poland
Introduction Solid waste management is developing into a complex task. New or modified treatment technologies are appearing. During the past two decades, thermal wastes management followed heavily disparate trends. In the 1980s, the focus was on new market players, and then in the 1990s on new technologies (especially pyrolysis and melting processes). Today the trend in thermal waste treatment technology is to pressure for cost-effectiveness for toward greater facility efficiency [1] [1] Brunner M., 2005. What are today's trends in waste management?. Flash von Roll INOVA, 23 February 2005. Von Roll Environmental Technology Ltd, Communication, Zurich, Switzerland
Introduction The looking back at major trends
Introduction Poland has became the European Union (EU) Member State by the year 2005. Among the many EU requirements are those regarding municipal waste disposal. In summary, EU strategic directive 91/156/EC states if waste is generated, then recovery, including recycling (or composting) as a source material, and energy recovery (depending on cost benefits and practicality), should be implemented by the year 2002.
Process Descriptions The purpose of this paper is to assess the economic feasibility of the waste to energy pyrolysis facility based on the internal rate of return (IRR) and net present value (NPV) measures to evaluate two new gasification plant projects (two scenarios) based on the American and Australian gasification technologies including a fifteen-year income statement projection, and applied LCI substitution principle. LCI substitution approach is based on the basic concepts of inventory to consider that inventories can also be financial resources
Description of LCA methodology The concept of LCA first emerged in the late 1960s, but did not receive much attention until the mid- 1980s. In 1989, the Society of Environmental Toxicology and Chemistry (SETAC) became the first international organization to begin oversight of the advancement of LCA. In 1994, the International Standards Organization (ISO) began developing standards for the LCA as part of its 14000 series standards on environmental management. The standards address both the technical details and conceptual organization of LCA
Description of LCA methodology The International Organization for Standarization (ISO) has adopted work carried out by SETAC and has incorporated it into a series of standards focusing on Environmental Management. These standards include the ISO 14040 series on LCA as follows: ISO 14040, Environmental Management - Life Cycle Assessment, Principles and Framework (ISO 1997) ISO 14041, Environmental Management - Life Cycle Assessment, Goal and Scope Definition and Life Cycle Inventory Analysis (ISO 1998) ISO 14042, Environmental Management - Life Cycle Assessment, Life Cycle Impact Assessment (ISO/FDIS 1999) ISO 14043, Environmental Management - Life Cycle Assessment, Life Cycle Interpretation (ISO/FDIS 1999)
Description of LCA methodology As defined by the U.S. Environmental Protection Agency: LCA is a technique to assess the environmental aspects and potential impacts associated with a product, process, or service by compiling an inventory of relevant energy and material inputs and environmental releases; evaluating the potential environmental impacts associated with identified inputs and releases; and interpreting the results to help make a more informed decision
Analytical Framework And Data Collection The investment profitability analysis based on the discounted cash-flow analysis (DCF) - net present value (NPV) and internal rate of return (IRR) have been used to study the operational total costs for new incinerating plant project Decision rule is as follows: a- Investment acceptance in case of positive net present value b- Investment rejection in case of negative net present value c- Net present value equals zero is inconclusive
The Benefis Of Monte Carlo Simulation The benefits of a simulation modeling approach are: savethetimeandtheresources an understanding of the probability of specific outcomes test the driving variables within a model (e.g. what factors most affect the NPV or IRR?) a far more flexible model clear summary charts and reports
The Benefis Of Monte Carlo Simulation One of the problems associated with traditional spreadsheet models is that for variables that are uncertain. With Crystal Ball risk assessment software, we have the ability to replace each uncertain variable with a probability distribution, a function that represents a range of values and the likelihood of occurrence over that the range. Monte Carlo simulation uses these distributions, referred to as 'assumptions', to automate the complex "what-if" process and generate realistic random values
The Benefis Of Monte Carlo Simulation Crystal Ball generates random numbers for a probability distribution over the entire range of possible values, based on the assumption variables. For this reason, a large number of trials are required to obtain accurate results for the true shape of the distribution. With Latin Hypercube sampling, an assumption's probability distribution is divided into intervals of equal probability. Compared with conventional Monte Carlo sampling, Latin Hypercube sampling is more precise because the entire range of the distribution is sampled in a more even
Examples Of Distributions
The Benefis Of Monte Carlo Simulation The Monte Carlo sampling was done using an Excel spreadsheet modified to develop scenarios for inputs given the probability distributions, means values, etc. and Crystal Ball, a software package offered by Decisionnering, generates random numbers for a probability distribution Monte Carlo analysis-simulation is the only acceptable approach for U.S. Environmental Protection Agency (EPA) risk assessments
Model Development Process Determine the relationships between variable Build Excel spreadsheet to model the relationships Add Crystal Ball Analyze Forecasts Simulate the model and analyze the outputs Report results and make decisions
The Financial Assessment of the Project Pro Forma cash flow statements, the heart of the financial model, for waste to energy gasification plants based on the American and Australian gasification technologies provides adequate financial data, including a fifteen-year income statement projection are shown in Tables 1 and 2, respectively.
Proposed American Renewable Energy System Utilizing MSW-biomass Fuel (Design At 200 T/Day) The two systems are designed to meet the specification for a plant with 200 TPD capacity of Municipal Solid Waste (MSW). Each system is rated at 100 TPD of Municipal Solid waste and will include an gasifier, one boiler, and one dry air pollution control to meet European standards. The two systems are coupled to a single generator system common to both systems
Proposed Australian Renewable Energy System Utilizing MSW-biomass Fuel (Design At 250 T/Day) The Australian world leading-advanced technology is based upon advanced technology of MSW biomass fuel pyrolytic gasification SYN-GAS production and energy generation. The Pyrolytic Gasification Chamber receives the MSW-biomass fuel and heats it to the required ignition temperature in an oxygen depleted, substiochiometric environment. This converts the organic materials within the solid feed into a volatile synthesis gas state, which is referred to as SYN-gas. The SYN-gas consists primarily of H2, CO, CO2 and hydrocarbons and has similar properties to methane gas, thus is utilized downstream in the process for energy generation. Solid residue remaining after gasification is extracted from the process and deposited directly into a container
Pro Forma Cash Flow Statements Data Input The ecological grant from the National Fund for Protection of the Natural Environment and Water Management for the project will be obtained in the amount of 25,000,000 PLN and based on the contracting agreement Australian partner arranges finance for balance of 50,000,000 PLN The outside resources (credit) will be borrowed at 10% interest for the duration of the 14 years
Pro Forma Cash Flow Statements Data Input Depreciation is applicable in this financial assessment Discount rate of 10% to represent the cost of bank credit (interest) is selected for calculation NPV The choice of discount rate is important and should represent at least the cost of capital, i.e. the interest rate charged on money borrowed for investment
Pro Forma Cash Flow Statements Proposed American Gasification System NPV = 7,382,155.86 PLN IRR=14%
Crystall Ball Output and Simulation Results Cristal Ball Forecast Chart for NPV after 10 000 replications Forecast: NPV_AMERICAN_PROJECT 10 000 Trials Frequency Chart 9 914 Displayed,022 216,016 162,011 108,005 54,000 0-6067243 597151 7261545 13925939 20590333 Certainty is 92,24% from 0 to +Infinity PLN
Pro Forma Cash Flow Statements Proposed Australian Gasification System NPV = 14,753,015.98 PLN IRR=18%
Crystall Ball Output and Simulation Results Cristal Ball Forecast Chart for NPV after 10 000 replications Forecast: NPV_AUSTRALIAN_PROJECT 10 000 Trials Frequency Chart 9 921 Displayed,023 229,017 171,7,011 114,5,006 57,25,000 0-10678731 2051560 14781851 27512142 40242433 Certainty is 93,98% from 0 to +Infinity PLN
Crystall Ball Output And Simulation Results The following important information can be obtained from the charts: The mean of the NPV is 7,420,886 PLN with a 92,24% confidence interval (certainty level) around the mean value of 597,151 PLN to 20,590,333 PLN for the First Scenario, and the mean of the NPV is 14,811,196 PLN with a 93,98% confidence interval around the mean value of 2051560 PLN to 20,590,333 PLN for the Second Scenario, respectively. These intervals provide a high degree of confidence that the mean NPV values are positives.
Crystall Ball Output And Simulation Results The mean NPV values for the American Gasification System and Australian Gasification System coming above the projected NPV values - 7,382,156 PLN, and 14,753,016 PLN, respectively. In other words, the most likely NPV values are about 7,420,886 PLN and 14,811,196 PLN for the First and Second Scenarios, respectively
CONCLUSIONS Life Cycle Inventory (LCI) decision support systems using Monte Carlo simulation with the Cristal Ball analysis tool, spreadsheet add-in software, is a practical methodology for Municipal Solid Waste (MSW) management under uncertainty. An important benefit of the approach is that it permit use of the stochastic models to predict the sensitivity of the NPV of the new waste to energy gasification plants based on the American and Australian gasification technologies including a fifteen-year income statement projection
CONCLUSIONS Several LCI models for waste management are in advanced stages of development. LCI models do not make the decisions. Present study using LCI substitution approach based on the basic concepts of inventory to consider that inventories can also be financial resources, can provide an approach to waste management Decision Support Systems
CONCLUSIONS The research described in this paper can also serve as the basis for future work. The potential directions for future research is to continue using risk assessment for analysis in LCI models for waste management decision support systems under uncertainty, because this technique accounts for uncertainties in the assumptions, and to introduce the sensitivity analysis in LCI data collection to aid in the optimization of design aspects in the waste management systems
CONCLUSIONS The benefits of Monte Carlo simulation are saving in time and resources. Crystal Ball eliminates the need to run, test, and present multiple spreadsheets. With Crystal Ball analysis we can show the benefit of investing more on a monthly basis. Cristal Ball can handle dozen assumptions simultaneously, and can establish correlation coefficients among variables
CONCLUSIONS The benefits of Monte Carlo simulation are saving in time and resources. Crystal Ball eliminates the need to run, test, and present multiple spreadsheets. With Crystal Ball analysis we can show the benefit of investing more on a monthly basis. Cristal Ball can handle dozen assumptions simultaneously, and can establish correlation coefficients among variables
CONCLUSIONS The purpose of a large number of Monte Carlo simulations is to provide an understanding of the uncertainty of the LCA/LCI results. Most stochastic investment project models show that projects request more resources than expected. Traditional methods cannot answer the important questions of: how likely are we to overrun? and where is the risk in the project?
CONCLUSIONS The research described in this paper can also serve as the basis for future work. The potential directions for future research is to continue using risk assessment for analysis in LCI models for waste management decision support systems under uncertainty, because this technique accounts for uncertainties in the assumptions, and to introduce the sensitivity analysis in LCI data collection to aid in the optimization of design aspects in the waste management systems. A sensitivity analysis of different substitution option and of substitution as compared to allocation option (steps 2 and 3 in ISO 14041) is advised by ISO where more methods are applicable. This would be very useful as a check
Acknowledgments This research scientific is granted by science financial support for 2007-2009 years
3rd International Conference on Life Cycle Management Zurich, August 27 to 29, 2007 Thank you for allowing to present my paper