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      Stochastic techno-economic evaluation of cellulosic biofuel pathways.

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          Abstract

          This study evaluates the economic feasibility and stochastic dominance rank of eight cellulosic biofuel production pathways (including gasification, pyrolysis, liquefaction, and fermentation) under technological and economic uncertainty. A techno-economic assessment based financial analysis is employed to derive net present values and breakeven prices for each pathway. Uncertainty is investigated and incorporated into fuel prices and techno-economic variables: capital cost, conversion technology yield, hydrogen cost, natural gas price and feedstock cost using @Risk, a Palisade Corporation software. The results indicate that none of the eight pathways would be profitable at expected values under projected energy prices. Fast pyrolysis and hydroprocessing (FPH) has the lowest breakeven fuel price at 3.11$/gallon of gasoline equivalent (0.82$/liter of gasoline equivalent). With the projected energy prices, FPH investors could expect a 59% probability of loss. Stochastic dominance is done based on return on investment. Most risk-averse decision makers would prefer FPH to other pathways.

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          Author and article information

          Journal
          Bioresour. Technol.
          Bioresource technology
          Elsevier BV
          1873-2976
          0960-8524
          Dec 2015
          : 198
          Affiliations
          [1 ] Department of Agricultural Economics, Purdue University, West Lafayette, IN 47907, USA. Electronic address: zhao269@purdue.edu.
          [2 ] Department of Forest and Natural Resources Management, SUNY-ESF, Syracuse, NY 13210, USA.
          [3 ] Department of Agricultural Economics, Purdue University, West Lafayette, IN 47907, USA.
          Article
          S0960-8524(15)01335-8
          10.1016/j.biortech.2015.09.056
          26454041
          e56b960e-836b-47e9-9e77-d9fbd1b87d7c
          History

          Techno-economic analysis,Cellulosic biofuel,Stochastic dominance,Monte-Carlo simulation

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