System wspomagania decyzji oparty na LCI – czesci analizy LCA dla studium przypadku gospodarki stałymi odpadami komunalnymi

and Environmental Protection

http://ago.helion.pl ISSN 1733-4381, Vol. 4 (2006), p-13-32

Decision support systems based on the Life Cycle Inventory (LCI) – part of a Life Cycle Assessment (LCA) for Municipal Solid Waste (MSW)

Management Case Study

Bieda B.

AGH, 30-067 Krakow, POLAND ul. Gramatyka 10

tel: (012)-6174326, fax: (012)-6367005 bbieda@wzn6.zarz.agh.edu.pl

Abstract

The LCIs for pyrolysis method was based on the pyrolysis facility designs for City of Konin, in Poland. Two different scenarios of the economic feasibility were studied for the municipal solid waste process via incineration based on the pyrolysis. First scenario used the American technology of the pyrolysis-based process, and the other the Australian pyrolysis process equipment. The economic analysis methods used to study the operational total costs of the actually, and new equipments, was discounted cash flow rate of return (IRR) and net present value (NPV) of the new and actually investments.

In this paper, the Monte Carlo (MC) simulation method was used for the sensitivity analysis. 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 over the entire range of possible values, based on the assumption variables.

The principal output reports provides by Crystal Ball® are forecast chart, percentiles

summary, and statistics summary.

Streszczenie

System wspomagania decyzji oparty na LCI – części analizy LCA dla studium przypadku gospodarki stałymi odpadami komunalnymi

W artykule omówiono szczegółowo wiodącą w tej grupie normę ISO 14041 - Life Cycle Inventory (LCI) użytą do oceny dwóch scenariuszy projektu inwestycji Zakładu Utylizacji Termicznej Odpadów metoda pirolizy opartą na technologii australijskiej oraz amerykańskiej, stosując metodę zdyskontowanych sald przepływów NPV (net present value) oraz wewnętrznej stopy zwrotu IRR (internal rate of return). Dane do analizy wzięto z ofert dla projektu Zakładu Utylizacji Termicznej Odpadów dla miasta Konina.

Dane wejściowe dwóch analizowanych scenariuszy - (modeli) zostały dobrane w oparciu o symulację Monte Carlo z użyciem programu Cristal Ball® opracowanego przez amerykańską firmę Decisioneering Inc. z Denver w oparciu o model zbudowany z wykorzystaniem funkcji arkusza kalkulacyjnego. Podstawą użycia programu Cristal Ball® jest stworzenie modelu symulacyjnego, który jest modelem probabilistycznym. Wyniki symulacji zostały przedstawione i poddane analizie.

1. Introduction

Life Cycle Assessment (LCA) is an analytical tool of increasing importance for supply chain management. It aims to provide the basis for decisions which will promote sustainable development of economies [7]. LCA according to ISO 14040 allows to efficiently analyze, aggregate and assess environmental impact and resource intensity along the whole life cycle (“cradle - to – grave) of products, processes and services, and is an analytical tool of increasing importance for supply chain management.

LCA methodology follows four phases, each phase can consist of one or more steps [1]: Goal and scope definition (ISO 14040)

• Definition of the objectives of the study • Choice of the functional unit

• Delimitation of the system boundaries • Data quality requirements

• Cut-off rules

Life Cycle Inventory Analysis (ISO 14041)

• The system: construction of the life cycle tree • Data collection

• Use of data

• Application of cut-off rules, taking into account of co-products • Computation of the inventory

• Identification of the most represented stages Impact assessment (ISO 14042)

• Selection of impacts categories

• Determination of the flows that are taken into account for the impact assessment

• Determination of their contribution to the impacts • Determination of the impacts

• Computation of the impacts

• Identification of the main flows contributing to the impacts Interpretation of results (ISO 14043)

• Identification of the strong and weak points of the studied cases • Meeting the goals set during the first stage

• Validation of the solution if necessary by the way of: • additional data collection

• sensitivity analysis, scenarios

• Detail of the applications and boundaries of the study • Leading to other possible studies

The adoption of LCA has grown rapidly, however the high level of expert knowledge, high data demands and high costs may impede widerspread use of LCA, especially by SMEs. International Organisation for Standarization (ISO) has standarized an LCA framework that consists of four elements [6]:

Goal and scope - define the intended use of LCA

Inventory analysis - collect inputs and outputs data for all the processes in the product system

Life cycle impact assessment (LCIA) - translates inventory data on input (resources and materials) and output (emissions and waste) into information about the product system's impacts on the environment, human health, and resources

Interpretation - evaluates all the LCA results according to the study's goal. Sensitivity nd uncertainty are also analyzed to qualify the results and the conclusions.

The uses of LCA is recommended by the Canadian Standards Association, because include [6]:

1. Evaluation and policy-making 2. Public education

3. Internal decision-making. 4. Public disclosure of information

Life Cycle Inventory (LCI) is the inventory of materials, energy requirements, and environmental emissions associated with a product or process from the time of the original recovery of raw materials used to build the product (“cradle”) to the time of its ultimate disposal to earth (“grave”) [14].

LCI - part of a LCA - is used in this paper, because the LCA can help in preparing pro-ecological investment strategy and include also internal decision-making to:

• compare alternative materials, products, processes, or activities within an organization

• compare resource use and pollution information with those of other manufacturers

2. Analytical Framework And Data Collection

The LCIs for pyrolysis method was based on the pyrolysis facility designs for City of Konin, in Poland. Two different scenarios of the economic feasibility were studied for the municipal solid waste process via incineration based on the different pyrolysis processing systems, to demonstrate the LCA/LCI methodology however traditional LCA does not take account of the financial aspects but they can be a valuable additional factor in investment decisions. First scenario used the American technology of the pyrolysis-based process, and the other the Australian pyrolysis process equipment. The economic analysis methods used to study the operational total costs of the actually, and new equipments, was discounted cash flow rate of return (IRR) and net present value (NPV) of the two new incinerating plant projects.

Each of the two technologies involves only differerent project cost. Income from sales and operating expense are the same for two options to pyrolysis processing systems.

The implementation of the case study using LCI will be done by designed the dynamic model and implemented using the Monte Carlo simulation based on the Excel® spreadsheet modified to predict the NPV of the two 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 and predicts which variables have the most influence on the study outcome.

3. Uncertainty in LCI

Usually the overall uncertainty of a LCI is dominated by a few major uncertainties. Likewise, the overall uncertainty of a specific process is typically dominated by one source of uncertainty and other sources of uncertainty may be ignored [4]. The uncertainties in the investment projects can include:

• input data required to make an economic evaluation (publications on environmental process data are often incomlete or inacucurate). The data are subject to obsolescence - there are many cases where processing industries have cut emissions by 90% during the last 10 years - the use of obsolescence data can therefore cause distortions (PRé Consultants, 2005)

• probability of success • capital and operating costs

• waste and gas/electric energy prices

Information about uncertainty in LCI results cannot be fully captured within the LCI database, because a significant share of this uncertainty arises in practice, based on relationship between the data [10]. When the main determining parametres of an

uncertainty is known, it can be eliminated or at least reduced to the uncertainty by modelling.

Three types of process modelling can be identified in LCA studies [7]:

• black box models of processess. This is the mostly used type in LCA because this is the easiest way of process modelling.

• models of processess with linear functional relations. In this concept linear relations (functions) between each input and output as well as between the different inputs are defined.

• models of processess with non-linear and linear functional relations. In this concept linear or non-linear relations (functions) between each input and output as well as between the different inputs are defined.

The overall uncertainty of the assessment includes [6]: • uncertainty of models and parameters • uncertainty of the indicators interpretation

The Office of Management and Budget’s (OMB's) Circular A-4 [11] defines uncertainty as both:

• Statistical variability of key elements underlying estimates of benefits and costs. • Incomplete knowledge about relevant relationships.

4. Description of Case Study

Net present value (NPV) method is one of the DCF (Discounted Cash Flow) methods and is based on the concept of the compound interest. This involves both the timing of returns and costs and an appropriate rate of inerest. Cash flow items are multiplied by a factor less than one (discounted) according to their distance from the present and interest rate. Appropriate rate, in case of net present value, is the cost of capital. If CFi means the cash flow (positive or negative) where the subscript i is the timing of such cash flow, then net present value can be written as: NPV= (1 ) 0 r CF i n i i + ∑ − = (1) And in case when the outlay (IO) is required only in year 0:

NPV=

(

1

)

0

r

CF

i n i i

+

∑

− = - IO (2) Decision rule is as follows:

• Investment acceptance in case of positive net present value, • Investment rejection in case of negative net present value,

• Net present value equals zero is inconclusive [15], [16].

Two Scenarios of the Proposed Gasification System for City of Konin, Poland

Two systems are designed to meet the specification for a plant with 200 MTPD capacity of Municipal Solid Waste (MSW). Each system is rated at 100 TPD of 4500 btu/lb Municipal Solid waste and will include an gasifier, one boiler, and one dry air pollution control to meet European standards. Two systems are coupled to a single generator system common to both systems. The Pro Forma Cash Flow Statements for the First and Second Scenarios are given in Tables 6.1. and 6.2., respectively.

5. The Benefits of Monte Carlo Simulation

The benefits of a simulation modeling approach are: (1) an understanding of the probability of specific outcomes (2) the ability to pinpoint and test the driving variables within a model (e.g. what factors most affect the NPV?); (3) a far more flexible model; and (4) clear summary charts and reports [15], [16]. One of the problems associated with traditional spreadsheet models is that for variables that are uncertain. With Crystal Ball®, 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 paper study uses stochastic modeling to predict the sensitivity of the IRR and NPV values to changes in various analysis parameters.

A practical uncertainty analysis method for determining input parameter uncertainties is based on sensitivity analysis, which should indicate how sensitive the results of the study are to data include. The results may be further used in different types of statistical analysis answering questions like: is the difference between actually and new pyrolysis process equipments in relation to parameter X statistically significant?. In this paper, the Monte Carlo (MC) simulation method was used for the sensitivity analysis. The Monte Carlo sensitivity analysis consist of drawing a random value from a defined probability distribution for all model inputs. 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 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. results and probabilities for those results. Monte Carlo analysis-simulation is the only acceptable approach for U.S. Environmental Protection Agency (EPA) risk assessments.

6. Monte Carlo Simulation with Crystal Ball®

The first task is to create a veritable deterministic model that represents the most likely scenarios cash flow statements. To use the Crystal Ball®, we must perform the following steps:

define assumption for probabilistic variables - manufacturing costs define the forecast cell, that is, the output variable - NPV

set the number of replication run the simulation

simulate the model and analyze the outputs report results and make decisions [3].

An Excel® model for this problem is shown in Figures 1 and 2, respectively.

In Crystal Ball®, the assumptions or input range for each parameter was defined by choosing a probability distribution that describes the uncertainty of the data. Input distribution may be normal, uniform, triangular, skewed, or any shape that reflects the nature of the measurement being assessed. A normal distribution was used for variables where the most likely value was known, where there was an equal chance that the variable could be above or below the mean, and where the variable was apt to be in the proximity of the mean [13].

Table 6.1. Pro Forma Cash Flow Statement for First Scenario – Proposed American Gasification System

Income from sales 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 15 Year Tipping fee (120 PLN/Mg) 10 050 000 10 050 000 10 050 000 10 050 000 10 050 000 10 050 000 10 050 000 10 050 000 Income from Energy sales (0,16 PLN/kWh) 8 928 000 8 928 000 8 928 000 8 928 000 8 928 000 8 928 000 8 928 000 8 928 000 Other Cash from Sales 2 000 000 2 000 000 2 000 000 2 000 000 2 000 000 2 000 000 2 000 000 2 000 000 Total Cash from Sales 20 978 000 20 978 000 20 978 000 20 978 000 20 978 000 20 978 000 20 978 000 20 978 000 Operating expences Salaries 25 persons 600 000 600 000 600 000 600 000 600 000 600 000 600 000 600 000 Tipping fee 422 100 422 100 422 100 422 100 422 100 422 100 422 100 422 100 Electricity 1 260 000 1 260 000 1 260 000 1 260 000 1 260 000 1 260 000 1 260 000 1 260 000 Water, etc. legal costs 81 000 81 000 81 000 81 000 81 000 81 000 81 000 81 000 Maintenance 400 000 400 000 400 000 400 000 400 000 400 000 400 000 400 000 Fuel 754 000 754 000 754 000 754 000 754 000 754 000 754 000 754 000 Insurance 270 000 270 000 270 000 270 000 270 000 270 000 270 000 270 000 Office maintenance 96 000 96 000 96 000 96 000 96 000 96 000 96 000 96 000 Communications-telephones 12 000 12 000 12 000 12 000 12 000 12 000 12 000 12 000 Environmental fee 565 000 565 000 565 000 565 000 565 000 565 000 565 000 565 000 Taxes 338 200 338 200 338 200 338 200 338 200 338 200 338 200 338 200 Administration 74 400 74 400 74 400 74 400 74 400 74 400 74 400 74 400

Environmenta education 35 000 35 000 35 000 35 000 35 000 35 000 35 000 35 000 Bank credit costs 7 470 000 6 972 000 6 474 000 5 976 000 5 478 000 4 980 000 4 482 000 3 984 000 Depreciation 423 200 423 200 423 200 423 200 423 200 423 200 423 200 423 200 Total costs 12 800 900 12 302 900 11 804 900 11 306 900 10 808 900 10 310 900 9 812 900 9 314 900 Net Income before Taxes 8 177 100 8 675 100 9 173 100 9 671 100 10 169 100 10 667 100 11 165 100 15 149 100 Net Income 7 441 161 7 894 341 8 347 521 8 800 701 9 253 881 9 707 061 10 160 241 13 785 681 Depreciation 423 200 423 200 423 200 423 200 423 200 423 200 423 200 423 200 Dotation 25 000 000 000 TOTAL 32 864 361 8 317 541 8 770 721 9 223 901 9 677 081 10 130 261 10 583 441 14 208 881 Manufacturing cost 7 952 500 64 000 000 14 420 100

Ending Cash Balance 24 911 861 -56 482 459 -5 649 379 9 223 901 9 677 081 10 130 261 10 583 441 14 208 881

NPV = 17 767 512.62 PLN IRR = 30%

Table 6.2. Pro Forma Cash Flow Statement for Second Scenario – Proposed Australian Gasification System

Income from sales 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 15 Year Tipping fee (120 PLN/Mg) 10 050 000 10 050 000 10 050 000 10 050 000 10 050 000 10 050 000 10 050 000 10 050 000 Income from Energy sales (0,16 PLN/kWh) 8 928 000 8 928 000 8 928 000 8 928 000 8 928 000 8 928 000 8 928 000 8 928 000 Other Cash from Sales 2 000 000 2 000 000 2 000 000 2 000 000 2 000 000 2 000 000 2 000 000 2 000 000 Total Cash from Sales 20 978 000 20 978 000 20 978 000 20 978 000 20 978 000 20 978 000 20 978 000 20 978 000 Operating expences Salaries 25 persons 600 000 600 000 600 000 600 000 600 000 600 000 600 000 600 000 Tipping 422 100 422 100 422 100 422 100 422 100 422 100 422 100 422 100 Electricity 1 260 000 1 260 000 1 260 000 1 260 000 1 260 000 1 260 000 1 260 000 1 260 000 Water, etc. legal costs 81 000 81 000 81 000 81 000 81 000 81 000 81 000 81 000 Maintenance 400 000 400 000 400 000 400 000 400 000 400 000 400 000 400 000 Fuel 754 000 754 000 754 000 754 000 754 000 754 000 754 000 754 000 Insurance 270 000 270 000 270 000 270 000 270 000 270 000 270 000 270 000 Office maintenance 96 000 96 000 96 000 96 000 96 000 96 000 96 000 96 000 Communications-telephones 12 000 12 000 12 000 12 000 12 000 12 000 12 000 12 000 Environmental fee 565 000 565 000 565 000 565 000 565 000 565 000 565 000 565 000

Taxes 338 200 338 200 338 200 338 200 338 200 338 200 338 200 338 200 Administration 74 400 74 400 74 400 74 400 74 400 74 400 74 400 74 400 Environmenta education 35 000 35 000 35 000 35 000 35 000 35 000 35 000 35 000 Bank credit costs 7 470 000 6 972 000 6 474 000 5 976 000 5 478 000 4 980 000 4 482 000 3 984 000 Depreciation 423 200 423 200 423 200 423 200 423 200 423 200 423 200 423 200 Total costs 12 800 900 12 302 900 11 804 900 11 306 900 10 808 900 10 310 900 9 812 900 9 314 900 Net Income before Taxes 8 177 100 8 675 100 9 173 100 9 671 100 10 169 100 10 667 100 11 165 100 15 149 100

Net Income 7 441 161 7 894 341 8 347 521 8 800 701 9 253 881 9 707 061 10 160 241 13 785 681 Depreciation 423 200 423 200 423 200 423 200 423 200 423 200 423 200 423 200 Dotation 25 000 000 000 TOTAL 32 864 361 8 317 541 8 770 721 9 223 901 9 677 081 10 130 261 10 583 441 14 208 881 Manufacturing cost 49 449 348 792 500 24 056 100

Ending Cash Balance -16 584 987 7 525 041 -15 285 379 9 223 901 9 677 081 10 130 261 10 583 441 14 208 881

NPV = 17 104 299.28 PLN IRR=29

6.1. Crystal Ball® Output And Simulation Results

The principal output reports provides by Crystal Ball® are forecast chart, percentiles

summary, and statistics summary [3]. When the 1000 trials are completed, we can analyze results using Crystal Ball® for the the forecast chart, an interactive histogram that contains all of the statistics for the 1000 trials [6]. Figures 6.1. through 6.2. show the results of 1000 replications of the spreadsheet using Crystal Ball®) for the two scenarios.

The Statistics Reports (Figures 6.3 and 6.4.) can help gain additional insight and provide a summary of key descriptive statistical measures. The "Mean", "Median" and "Mode" values form the basis of starting points for the analysis. The following important information can be obtained from the charts:

• the ranges of possible NPVs for two scenarios are; 4 883 014.06 PLN to 24211580.69 PLN and 8774600.16 PLN to 24513967.80 PLN, respectively

• the original NPV value for the American Gasification System equal 17767512.62 PLN is not more than the projected mean of the NPV (177770240.96 PLN). The NPV forecat value for the Australian Gasification System = 17104577.84 PLN exceeding the net present value of the 15 years cash flows computed using the Excel® spreadsheet NPV function (17104299.28 PLN).

Frequency Chart ,000 ,006 ,011 ,017 ,022 0 5,5 11 16,5 22 4883014,06 z³ 11325869,60 z³ 17768725,14 z³ 24211580,69 z³ 30654436,23 z³ 1 000 Trials 991 Displayed Forecast: NPV

Figure 6.1. First Scenario - Cristal Ball Forecast Chart for NPV after 1000 replications.

Frequency Chart

,000 ,007 ,014 ,021 ,028 0 7 14 21 28 8774600,16 z³12709442,07 z³16644283,98 z³20579125,89 z³24513967,80 z³

1 000 Trials

993 Displayed

Forecast: NPV

Target Forecast: NPV

Gasifiers generation system -,99

Gasifier Building -,11

Contingency reserve -,09

Project engineering -,08

Civil & Site/Design work -,07

Rolling stock (forks, lifts, etc.) -,07

Continuous emission control, monitoring -,06

Program development costs ,04

Automatic loading systems ,04

Office furniture & computer system -,03

Construction management -,03

-1 -0,5 0 0,5 1

Measured by Rank Correlation Sensitivity Chart

Target Forecast: NPV Pyrolytic gasification chamber -,87

Instrumentation & control -,32

Thermal reactor -,22

Multi-stage steam turbine -,20

Steam boiler -,15

Project management -,07

Continous emission monitoring -,06 Fan & Flow control -,05 Instrumentation & Commissioning supervis -,03

-1 -0,5 0 0,5 1

Measured by Rank Correlation Sensitivity Chart

NPV

56495545,89 4594622,327 3487379,376 3170027,852 2908997,506 1754587,748 1394951,75 1095037,124 584136,0454 369662,2138 146034,0114 73104454,11 5945377,673 4512620,624 4101972,148 3764202,494 2270412,252 1805048,25 1416962,876 755863,9546 478337,7862 188965,9886 5 000 000,00 zł 10 000 000,00 zł 15 000 000,00 zł 20 000 000,00 zł 25 000 000,00 zł 30 000 000,00 zł Gasifiers generation system Gasifier Building Contingency reserve Project engineering Continuous emission control, monitoring Construction management Program development costs

Rolling stock (forks, lifts, etc.)

Civil & Site/Design work

Automatic loading systems Office furniture & computer system

Downside Upside

Figure 6.7. Crystal Ball® output as a "tornado" diagram - First Scenario. NPV 26012602,52 13077672,66 7036886,415 6504428,973 6336829,879 3558241,181 812559,3945 690937,0388 746124,8174 33659947,48 16922327,34 9105633,585 8416641,027 8199770,121 4604314,819 1051440,605 894062,9612 965475,1826 12 000 000,00 zł 14 000 000,00 zł 16 000 000,00 zł 18 000 000,00 zł 20 000 000,00 zł 22 000 000,00 zł 24 000 000,00 zł Pyrolytic gasification chamber Instrumentation & control

Steam boiler

Thermal reactor

Multi-stage steam turbine

Project management Continous emission

monitoring Fan & Flow control Instrumentation & Commissioning supervis

Dow nside Upside Figure 6.8. Crystal Ball® output as a "tornado" diagram - Second Scenario.

Figures 6.5. and 6.6. shown the results from the sensitivity analysis. Positive coefficients indicate that an increase in assumption is associated with an increase in the forecast, negative coefficients imply the reverse [3]. In the Sensitivity Charts abow (Figures 6.5. and 6.6.), we can see that the main influences of NPV are Gasifiers Generation System (99%) and Pyrolytic Gasification Chamber (87%). This would imply that City of Konin, a buyer, must closely manage even small changes in Gasifiers Generation System or Pyrolytic Gasification Chamber , to Australian and American Companies, the sellers of the pyrolysis technologies.

Outputs from Crystal Ball® are displayed also as a "tornado" diagrams such as that shown in Figures 6.7. and 6.8., First and Second Scenarios, respectively.

7. Conclusions

1. 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.

2. To my knowledge this is the first time that the methodology of the Life Cycle Inventory (LCI) – part of a Life Cycle Assessment (LCA) has been used for Municipal Solid Waste (MSW) Management based on the pyrolysis technologies in Poland (compare alternative materials, products, processes, or activities within an organization; compare resource use and pollution information with those of other manufacturers). LCA techniques is still at an early stage of development, and some of the stages are in the relative infancy [9].

3. Traditional LCA does not take account of the financial aspects but they can be a valuable additional factor in investment decisions.

4. The case study will serve to illustrate the importance of a dynamic perspective in the LCA of environmental concerns. This will be accomplished by simulating the dynamics of pyrolysis technology development. The case study chosen is a comparison of two different scenarios of the economic feasibility were studied for the municipal solid waste process via incineration based on the different pyrolysis processing systems.

5. The Office of Management and Budget’s (OMB's) Circular A-4 [11] defines uncertainty as both:

• Statistical variability of key elements underlying estimates of benefits and costs • Incomplete knowledge about relevant relationships

and recommends analytical approaches that use Monte Carlo simulations to derive probability distributions of benefits and costs.

6. Engineers and managers have understood for many years that a statistical approach brings the opportunity for significant improvements in the quality and cost of manufactured product. In this paper the use of statistical tools, especially Monte Carlo simulation, is suggested as a way to gain better understanding of a new pyrolysis development processess. 7. The benefits of Monte Carlo simulation are saving in time and resources. Crystal Ball® eliminates the need to run, test, and present multiple spreadsheets [15], [16]. 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.

8. Use a numerical sensitivity analysis to examine how the results of your analysis vary with plausible changes in assumptions, choices of input data, and alternative analytical approaches.

Sensitivity analysis can be used to find "switch points" -- critical parameter values at which estimated net benefits change sign or the low cost alternative switches [11]. Sensitivity plots are not only fundamental to determining which are the prominent input variables, but can be invaluable indicators of whether a particular project should be pursued [8].

9. Most stochastic investment project models show that projects requrie more resources than expected.

10. The IRR values for the two scenarios are positive (30% and 29%, respectively), and exceed the IRR using in the Excel® spreadsheet NPV function (15%).

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[9] Kulczycka J (Eds), 2001. Ekologiczna ocena cyklu życia (LCA) nową techniką zarządzania środowiskiwego. Wydawnictwo Instytutu Gospodarki Surowcami Mineralnymi i Energią PAN, Krakow, Poland.

[10] Norris G. The Many Dimensions of Uncertainty Analysis in LCA, 2005. http://www.athenasmi.ca/papers/down_papers/UncertaintyAnalysis_in_LCA.pdf. [11] Office of Management and Budget (OMB) - The Executive Office of the President,

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[12] PRé Consultants, 2005, Plotterweg 12, 3821 BB Amersfoort, The Netherlands, http://www.pre.nl/life_cycle_assessment/life_cycle_inventory.htm

[13] Spath, P.L., Lane J.M., Mann, M.K., Armos, W.A., 2000. Update of Hydrogen from Biomass-Determination of the Delivered Cost of Hydrogen. National Renewable Energy Laboratory, Operated for the U.S. Dep. of Energy by Midwest Research Institute, Battelle, Bechtel, April, Golden, Colorado, USA.

[14] Vigon, B.W., Tolle, D.A., Cornaby, B.W., Latham, H.C., Harrison, C.L., Boguski, T.L., Hunt, R.G., Sellers, J.D.,1993. Life –cycle assessment: Inventory guidelines and principles.

[15] Wajs, W., Bieda, B., Tadeusiewicz, R., 2000. Project Cost Analysis for Niepolomice Municipal Solid Waste using the Monte Carlo Simulation. Int. Confrernce RISK ANALYSIS 2000, Bologna, Italy. Wessex Institute of Technology Publishers, pp. 225-234.

[16] Wajs, W., Bieda, B., Tadeusiewicz, R., 2000. Linear Programming And Risk Analysis Methods for Municipal Solid Waste Decision Support System, Int. Conference IFAC MMM2001, Tokyo, Japan.