Economic risk assessment of advanced process technologies for bioethanol production in South Africa: Monte Carlo analysis
The 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.
Bioethanol, Monte Carlo, Risk analysis, Techno economic feasibility, Triticale, Advanced process, Alternative motor fuels, Bio-ethanol production, Economic feasibilities, Economic indicators, Economic performance, Economic success, Economic viability, Energy recovery, Ethanol industry, Ethanol plants, Fermentable sugars, Fermentation process, Fibre fractionation, Financial risks, Fuel alcohols, Fuel source, Government subsidies, Grain ethanols, High probability, Historical data, Industrial strategies, Management decisions, Monte Carlo, Monte carlo analysis, Monte Carlo Simulation, Parametric uncertainties, Plant availability, Pre-Treatment, Price fluctuation, Process heating, Process plants, Process Technologies, Quantifiable measures, South Africa, South African government, Stochastic variable, Support mechanism, Techno-economic feasibility, Triticale, Automotive fuels, Computer simulation, Economic analysis, Energy policy, Enzymatic hydrolysis, Ethanol, Fermentation, Industry, Investments, Monte Carlo methods, Planning, Power quality, Probability distributions, Public policy, Risk analysis, Risk assessment, Risk perception, Starch, Sugars, Bioethanol, agricultural worker, biomass, cellulose, cultivation, decision making, economic analysis, enzyme activity, ethanol, feasibility study, fermentation, hydrolysis, industrial production, integrated approach, Monte Carlo analysis, numerical model, oil production, risk assessment, starch, state role, stochasticity, sugar, uncertainty analysis, South Africa, Western Cape, Nucleopolyhedrovirus, Triticosecale