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Economic risk assessment of advanced process technologies for bioethanol production in South Africa: Monte Carlo analysis

dc.contributor.authorAmigun B.
dc.contributor.authorPetrie D.
dc.contributor.authorGorgens J.
dc.date.accessioned2011-10-13T16:58:40Z
dc.date.available2011-10-13T16:58:40Z
dc.date.issued2011
dc.identifier.citationRenewable Energy
dc.identifier.citation36
dc.identifier.citation11
dc.identifier.citationhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79957834884&partnerID=40&md5=229871fbc3939cd84e0a6a9f1618387f
dc.identifier.issn9601481
dc.identifier.other10.1016/j.renene.2011.03.015
dc.identifier.urihttp://hdl.handle.net/10019.1/16809
dc.description.abstractThe development of ethanol industry for use as an alternative motor fuel has been steadily increasing around the world for several reasons. In South Africa, this industry is still in the early stages of development. In the National Biofuels Industrial Strategy, the South African government has made provision for support mechanisms to encourage investment in bioethanol production. There is thus an opportunity for grain-growing farmers to cultivate available or marginal lands for bioethanol crops, including triticale. This article examines the contribution of parametric uncertainty to economic feasibility studies for biomass-to-ethanol process plants. Monte Carlo (stochastic variable) simulation is employed as a tool to determine probability distributions for economic indicators (such as NPV and ROI) in the context of a proposed 200,000 tonnes per annum triticale grain ethanol plant located in the Western Cape province of South Africa. Three process technology scenarios are considered: a conventional starch-to-ethanol plant (Scenario I), an advanced starch-to-ethanol with grain fibre fractionation and energy recovery (Scenario II) and an integrated starch-cellulose plant where fractionated fibre is converted to fermentable sugars by pretreatment and enzymatic hydrolysis and then fermented to fuel alcohol (Scenario III). By modelling prices of raw materials and products stochastically, based on historical data, the concurrent fluctuations in prices are accounted for, incorporating a quantifiable measure of the associated financial risk to a typical (deterministic) economic prefeasibility analysis. Risk assessment of all processing options reveals that Scenario II is the most preferred fermentation process, achieving very high probability of economic success (98% probability of NPV > 0), suggesting that, under almost all conceivable circumstances of price fluctuation and plant availability, the investment will be successful. This is followed by Scenario III (96% probability of NPV > 0) while the least preferred option is Scenario I (93% probability of NPV > 0). The study also shows, however that without government subsidy, the plant exhibits only a 19% chance of economic success. Economic performance is shown to improve when fast-growing biomass is used to replace electricity as a fuel source for process heating. Monte Carlo simulation could assist energy planners, investors, and policy/decision makers to make a better management decision by identifying possible public policy that could be used to enhance the economic viability of the proposed ethanol plant. © 2011 Elsevier Ltd.
dc.subjectBioethanol
dc.subjectMonte Carlo
dc.subjectRisk analysis
dc.subjectTechno economic feasibility
dc.subjectTriticale
dc.subjectAdvanced process
dc.subjectAlternative motor fuels
dc.subjectBio-ethanol production
dc.subjectEconomic feasibilities
dc.subjectEconomic indicators
dc.subjectEconomic performance
dc.subjectEconomic success
dc.subjectEconomic viability
dc.subjectEnergy recovery
dc.subjectEthanol industry
dc.subjectEthanol plants
dc.subjectFermentable sugars
dc.subjectFermentation process
dc.subjectFibre fractionation
dc.subjectFinancial risks
dc.subjectFuel alcohols
dc.subjectFuel source
dc.subjectGovernment subsidies
dc.subjectGrain ethanols
dc.subjectHigh probability
dc.subjectHistorical data
dc.subjectIndustrial strategies
dc.subjectManagement decisions
dc.subjectMonte Carlo
dc.subjectMonte carlo analysis
dc.subjectMonte Carlo Simulation
dc.subjectParametric uncertainties
dc.subjectPlant availability
dc.subjectPre-Treatment
dc.subjectPrice fluctuation
dc.subjectProcess heating
dc.subjectProcess plants
dc.subjectProcess Technologies
dc.subjectQuantifiable measures
dc.subjectSouth Africa
dc.subjectSouth African government
dc.subjectStochastic variable
dc.subjectSupport mechanism
dc.subjectTechno-economic feasibility
dc.subjectTriticale
dc.subjectAutomotive fuels
dc.subjectComputer simulation
dc.subjectEconomic analysis
dc.subjectEnergy policy
dc.subjectEnzymatic hydrolysis
dc.subjectEthanol
dc.subjectFermentation
dc.subjectIndustry
dc.subjectInvestments
dc.subjectMonte Carlo methods
dc.subjectPlanning
dc.subjectPower quality
dc.subjectProbability distributions
dc.subjectPublic policy
dc.subjectRisk analysis
dc.subjectRisk assessment
dc.subjectRisk perception
dc.subjectStarch
dc.subjectSugars
dc.subjectBioethanol
dc.subjectagricultural worker
dc.subjectbiomass
dc.subjectcellulose
dc.subjectcultivation
dc.subjectdecision making
dc.subjecteconomic analysis
dc.subjectenzyme activity
dc.subjectethanol
dc.subjectfeasibility study
dc.subjectfermentation
dc.subjecthydrolysis
dc.subjectindustrial production
dc.subjectintegrated approach
dc.subjectMonte Carlo analysis
dc.subjectnumerical model
dc.subjectoil production
dc.subjectrisk assessment
dc.subjectstarch
dc.subjectstate role
dc.subjectstochasticity
dc.subjectsugar
dc.subjectuncertainty analysis
dc.subjectSouth Africa
dc.subjectWestern Cape
dc.subjectNucleopolyhedrovirus
dc.subjectTriticosecale
dc.titleEconomic risk assessment of advanced process technologies for bioethanol production in South Africa: Monte Carlo analysis
dc.typeArticle
dc.description.versionArticle


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