Presentation at the 2003 AIChE Annual Meeting, 20 November 2003, San Francisco, California

Jechura, J. L.

Ruth, M. F.

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November 19, 2003Mark F. Ruth and John L. JechuraNational Renewable Energy LaboratoryIncorporating Monte Carlo AnalysisInto Techno-Economic Assessmentof Corn Stover to EthanolPresented at the 2003 AIChE Annual Meeting, November 20, 2003, San Francisco, CaliforniaNREL/PR-510-35313Corn Stover to Ethanol ProcessFeedstock HandlingCO2EthanolEnzymeCorn StoverLigninResidue SteamElectricitySteam & Acid LiquorPretreatment S/L SeparationConditioningSaccharification&FermentationDistillation &Ethanol PurificationWastewaterTreatmentBurner/BoilerTurbogeneratorLimeSteamGypsumProcess Analysis TechniqueConceptualProcess DesignMaterial & Energy BalanceCapital & Project Cost EstimatesEconomicAnalysisEnvironmentalAnalysisR&DASPENPlus™ Process Simulation Engine (Input Language)63 Components164 Unit Operation Blocks82 Control BlocksMicrosoft Excel™Process Analysis TechniqueConceptualProcess DesignMaterial & Energy BalanceCapital & Project Cost EstimatesEconomicAnalysisEnvironmentalAnalysisR&DASPENPlus™ Process Simulation Engine (Input Language)63 Components164 Unit Operation Blocks82 Control BlocksMicrosoft Excel™Batch ASPEN file:Runs faster than GUIEasier to search nd modify for a small group of engineersTechnoeconomic ModelIn 2002, NREL published an updated target-case design report– Greenfield corn stover to ethanol process– NREL/TP-510-32438– www.nrel.gov/docs/fy02osti/32438.pdfMinimum Ethanol Selling Price ($ per gallon ethanol) is the primary resultDesign Case Economic ResultsPlant Size:  2200 tons (2000 MT) Dry Corn Stover/Day (Greenfield Site)Corn Stover Cost:  $30/dry ton Economic Parameter (Units, $2001)  Value   Minimum Ethanol Selling Price ($/gal) $1.07  Ethanol Production (MM gal/yr) 69  Ethanol Yield (gal/dry ton stover) 90  Total Project Investment ($ MM) $197  TPI per Annual Gallon ($/gal) $2.86  Production Cost ($/gal) $0.58  Monte Carlo AnalysisUses random numbers within defined probability functions to predict the uncertainty of modeled systems– Packaged software (e.g., Crystal Ball) makes it easier with ExcelUsed in the environmental, safety, business and other fieldsUse of ASPENPlus™EXCEL& VBA .sum FileASPENEngineer Enters ParametersEngineer starts transfer routineInitial Monte Carlo TechniqueCrystal BallEXCEL& VBA.sum FileASPENEngineer Selects Parameter FunctionEngineer Transfers ParametersEngineer starts transfer routineMonte Carlo TechniqueCrystal BallEXCEL& VBAText File.sum FileASPENEngineer Selects Parameter FunctionVBA routinewrites text fileASPEN Fortran blockreads text fileMonte Carlo TechniqueCrystal BallEXCEL& VBAText File.sum FileDOS BatchFileASPENEngineer Selects Parameter FunctionVBA Routinelaunches batch fileDOS batch filelaunches ASPENMonte Carlo TechniqueCrystal BallEXCEL& VBAText File.sum FileDOS BatchFileASPENEngineer Selects Parameter FunctionVBA routine “senses”completion of ASPEN and reads “.sum” fileMonte Carlo TechniqueCrystal BallEXCEL& VBAText File.sum FileDOS BatchFileASPENEngineer Selects Parameter FunctionProbability DistributionsFeedstock variability– Measured dataYields– Estimated probability functionsCorn Stover Collection Locationsxxxxxxxxxxxxxxxx xxxxxxxxx xxx xxxxxxxxxxxx xxxxxxxxxxxxxxx738 SamplesStover Composition RangesStructural Glucan Xylan Lignin Protein AcetylUronicAcidsStructural Inorganics(Silica, Ash)SoilSolubleSolidsTotalMinimum(% dry wt.) 27.9 14.5 11.5 1.3 0.9 1.4 -1.2 0.9 2.0 90.0Maximum(% dry wt.) 39.6 25.5 20.4 7.0 3.9 3.9 10.2 1.7 19.6 101.9Span (% dry wt.) 11.7 11.0 8.9 5.7 3.0 2.5 11.3 0.8 17.5 11.9Mean(% dry wt.) 33.8 20.0 15.8 3.6 2.7 2.9 4.2 1.3 8.2 97.4Standard Deviation(% dry wt.)2.0 1.6 1.4 0.7 0.5 0.3 1.6 0.1 2.2 1.7738 SamplesFeedstock Probability FunctionsStructural glucanOther structural sugarsXylan fraction of other structural sugarsLigninConstant ratios– Galactan/mannan/arabinan– Ash/acetate/protein/soluble solidsGeneral Feedstock Composition35%26%20%15%4%Structural GlucanOther Structural SugarsLigninRemainingSoluble SolidsComponent Breakdown21%2%3%1%20%5%2% 3%5% 4%35%Structural GlucanXylanGalactanArabinanMannanLigninAshAcetateProteinSolsolidsExtractivesFeedstock Probability DistributionsYield Probability DistributionsMonte Carlo Analysis ResultsHistogram of MESPs for 1195 Monte Carlo Simulation Runs0102030405060708090100$0.96$0.98$1.00$1.02$1.04$1.06$1.08$1.10$1.12$1.14$1.16$1.18$1.20$1.22$1.24$1.26$1.28$1.30$1.32$1.34$1.36$1.38MESP ($/gal)Number of Occurances Mean:  $1.15/galMonte Carlo Analysis ResultsCumulative MESPs for 1195 Monte Carlo Simulation Runs0%10%20%30%40%50%60%70%80%90%100%$0.96$0.98$1.00$1.02$1.04$1.06$1.08$1.10$1.12$1.14$1.16$1.18$1.20$1.22$1.24$1.26$1.28$1.30$1.32$1.34$1.36$1.38MESP ($/gal)PercentileMean:  $1.15/gal90% Confidence Interval:$1.06-$1.25/galConclusionsMonte Carlo is useful for confidence interval estimates– A technique has been developed to use ASPEN batch techniques and Excel– Reams of data improve function definition but estimates can be useful – This analysis gave a 90% confidence interval of $1.06-$1.25/galMonte Carlo can also be used to estimate design constraintsAcknowledgementThis work supported by the Office of the Biomass Program of theU.S. Department of EnergyOffice of Energy Efficiency and Renewable Energy

Incorporating Monte Carlo Analysis into Techno-Economic Assessment of Corn Stover to Ethanol

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