(573u) Stochastic Modeling of the Biodiesel Production Process

This investigation will focus on using stochastic modeling techniques to study the uncertainties in the biodiesel production process and analyze their impact on the plant efficiency and process economics.

There are inherent uncertainties in the biodiesel production process arising out of feedstock compositions, operating parameters and mechanical equipment design and can have significant impact on the product quality and process economics. The uncertainties can be quantified in the form of probabilistic distribution function. Stochastic modeling capability can be implemented in the ASPEN process simulator to take into consideration these uncertainties and the output could be evaluated to determine plant efficiency.

Diwekar and Rubin [1] have used stochastic modeling to study complex integrated gasification combined cycle (IGCC) power systems and have showed the impact of uncertainties on the overall plant efficiency.

Biodiesel production involves a trans-esterification reaction where long chain fatty acids are converted to mono alkyl esters and glycerol. Sodium hydroxide is used as a catalyst. The glycerol and esters are separated out in a settling vessel. The alcohol is separated from the glycerol and ester by either evaporation or flashing. The ester is then neutralized, washed with slightly acidic water to remove methanol and then dried and stored. The uncertainties are in the biodiesel feedstock: soybean oil, canola oil and animal grease. As most of the biodiesel is produced in a batch process, there are time dependent uncertainties as well as variables in the operating parameters. The results of this study will show the impact of the uncertainties on biodiesel plant output and provide a useful simulation model which is more accurate representative of real world conditions.

1.Diweker, U.M. and E.S.Rubin (1990), Stochastic Modeling of Chemical Processes, Computers Chemical Engineering, Vol. 15, No. 2, pp 105-114, 1991