Probability density distribution in the prediction of reaction equilibria

Abstract

Reaction equilibria play an important role in chemical engineering. They can be calculated from substance properties of the compounds involved in the reaction. Since these substance properties usually come from measurements there is always an uncertainty with them. This uncertainty in the input data causes an uncertainty of the simulation results, even if the model itself is perfect. The effect of input uncertainty on uncertainty in equilibrium calculations has been studied. Monte Carlo sampling has been applied for accessing uncertainty and probability density distribution. Uncertainty in equilibrium conversion can be very high. However, the shape of the curve of equilibrium conversion over temperature can be predicted very precisely. As a consequence, simulations of reaction equilibria can still be useful, because temperatures required for achieving a certain conversion can be determined with comparatively high accuracy. The probability density distributions of the obtained equilibria can strongly deviate from those of the input data and in some cases can even become bimodal.