Analysis of Errors in Semi-Empirical Techniques for the Estimation of Microkinetic Model Parameters

Bio-oil has the potential to replace petroleum based feedstocks for fuels and chemicals, but new catalysts for the conversion processes need to be identified if this is to be successful. Traditionally, new catalysts have been identified primarily via trial and error. In an effort to more efficiently identify potential catalysts, there has been an increasing focus placed on detailed microkinetic modeling of reaction networks. For small networks, it is possible to calculate the needed kinetic parameters via, e.g., density functional theory. However, as the size of the reaction network increases, this approach quickly becomes computationally prohibitive. Because model results are usually insensitive to the vast majority of the parameters in a model, one way to deal with this challenge is to employ semi-empirical methods for the rapid estimation of the parameters and improve only the sensitive ones using more accurate methods.

A number of semi-empirical methods have been developed for this purpose. These include the linear scaling relations, group additivity for surface heats of formation, and Bronsted-Evans-Polanyi correlations for the estimation of activation energies.[1] Despite the work which has been done in this field in developing new techniques, little attention has been paid to characterizing the accuracy of the estimates afforded by these methods. In this work we use theoretical and computational methods to explicitly characterize the distributions of errors inherent to these models when used both separately and in concert with each other. We find that in each case the errors are distributed approximately normally, implying that the distribution of combined errors will also be approximately normally distributed but with larger variances. This is borne out by comparison of DFT and semi-empirical estimates of heats of formation, heats of reaction, and activation energies. The implications for the use of these techniques are also explored via the microkinetic modeling of ethanol steam reforming.

1.            Salciccioli, M., et al., A review of multiscale modeling of metal-catalyzed reactions: Mechanism development for complexity and emergent behavior. Chemical Engineering Science, 2011. 66(19): p. 4319-4355.