Understanding Uncertainties in the Economic Feasibility of Transportation Fuel Production using Biomass Gasification and Mixed Alcohol Synthesis. Issue 3 (29th January 2016)

• Record Type: Journal Article
• Authors: Ou, Longwen; Li, Boyan; Dang, Qi; Jones, Susanne; Brown, Robert; Wright, Mark M.
• Is Part Of: Energy technology. Volume 4:Issue 3(2016:Mar.)
• Page Start: 441
• Page End: 448
• Publication Date: 2016-01-29
• Subjects: biomass -- ethanol -- fuels -- techno-economic analysis -- uncertainty analysis; Energy development -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1002/ente.201500367
• Languages: English
• ISSNs: 2194-4288

Abstract

Abstract: This study evaluates uncertainties in the techno‐economic analysis of transportation fuel production from biomass gasification and mixed alcohol synthesis. Two scenarios are considered: a state‐of‐technology scenario and a target scenario with projected technological advances. Uncertainties of more than 10 parameters are investigated. The probability distributions of these parameters are estimated based on historical price data and experimental data. Data samples generated from the corresponding distribution are then utilized to run a Monte Carlo simulation. The results yield minimum fuel‐selling prices of $ 1.85 L −1 with a standard deviation of 0.13 for the state‐of‐technology scenario and $ 1.14 L −1 with a standard deviation of 0.11 for the target scenario, respectively. The feedstock price and internal rate of return (IRR) have significant impacts on the minimum fuel‐selling price in both scenarios. These findings are indicative of the reduction in biofuel cost and uncertainty achievable with increasing technology maturity. Abstract : Uncertainty matters ! Stochastic modeling is employed to evaluate uncertainty of previously conducted techno‐economic analyses of biofuel production from biomass gasification. The results indicate that fluctuations and variations of input parameters yield significant uncertainties in biofuel cost estimates. The inclusion of uncertainty analysis makes the model results more informative.