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Encontro de Energia no Meio Rural



Resumen

N0VAES, W. S., CAIXETA, S. H. M, SELVAM, P. V. P. et al. Fuel ethanol from Brazilian biomass: economic risk analysis based on monte carlo simulation techniques. In Anales del 4º Encontro de Energia no Meio Rural, 2002, Campinas (SP) [online]. 2002 [citado 30 Abril 2024]. Disponible en: <http://www.proceedings.scielo.br/scielo.php?script=sci_arttext&pid=MSC0000000022002000200003&lng=es&nrm=iso> .

At the present the fuel ethanol from Brazilian biomass is one of the most favorable energetic project in development to the Brazil mainly with respect to the rural employment, environment and energetic safety. In this context, a technological and economical study was developed applied to fuel ethanol production from sugar cane based on dynamic modeling, simulations and economical risk analysis. The objective of this work concern to develop an stochastic methodology and as well as the implementation of the economic risk analysis inherent to the ethanol process production by the changing of the process variables and parameters based on Monte Carlo simulation method. The mass balance and economic evaluation was obtained from simulations were carried out using the SuperPro DesigneTM v. 3.0 software. In addition to this, the economic risk analysis was carried out based on changing in the distribution of the selling price, yield of fermentation and feed flow rate the process stochastic variables. The stochastic analysis using Monte Carlo simulation was implemented in a spreadsheet model utilizing @Risk v. 4.5 software for Excel. Based on deterministic and stochastic simulations of the fuel ethanol production the results saveral techn economical parameters have been obtained and analyzed. The important advantage of the proposed method is the possibility to predict the economical risk involved in the ethanol production take in to account the aleatory and complex nature inherent to the process, with precision, reliability and very rapidly.

Palabras llave : Ethanol; Simulation; Stochastic; Monte Carlo; Risk Analysis.

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