Combining Monte Carlo simulations and experimental design for incorporating risk and uncertainty in investment decisions for cleantech: a fast pyrolysis ... View Full Text


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Article Info

DATE

2018-08

AUTHORS

Tom Kuppens, Parisa Rafiaani, Kenny Vanreppelen, Jan Yperman, Robert Carleer, Sonja Schreurs, Theo Thewys, Steven Van Passel

ABSTRACT

The value of phytoextracting crops (plants cultivated for soil remediation) depends on the profitability of the sequential investment in a conversion technology aimed at the economic valorization of the plants. However, the net present value (NPV) of an investment in such an innovative technology is risky due to technical and economic uncertainties. Therefore, decision makers want to dispose of information about the probability of a positive NPV, the largest possible loss, and the crucial economic and technical parameters influencing the NPV. This paper maps the total uncertainty in the NPV of an investment in fast pyrolysis for the production of combined heat and power from willow cultivated for phytoextraction in the Belgian Campine. The probability of a positive NPV has been calculated by performing Monte Carlo simulations. Information about possible losses has been provided by means of experimental design. Both methods are then combined in order to identify the key economic and technical parameters influencing the project’s profitability. It appears that the case study has a chance of 87% of generating a positive NPV with an expected value of 3 million euro (MEUR), while worst-case scenarios predict possible losses of 7 MEUR. The amount of arable land, the biomass yield, the purchase price of the crop, the policy support, and the product yield of fast pyrolysis are identified as the most influential parameters. It is concluded that both methods, i.e., Monte Carlo simulations and experimental design, provide decision makers with complementary information with regard to economic risk. More... »

PAGES

1195-1206

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10098-018-1543-1

DOI

http://dx.doi.org/10.1007/s10098-018-1543-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104240701


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This table displays all metadata directly associated to this object as RDF triples.

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