The group additivity method for Arrhenius parameters is applied to hydrogen addition to alkenes and alkynes and the reverse β-scission reactions, an important reaction family in thermal processes based on radical chemistry. A consistent set of group additive values for 33 groups is derived to calculate the activation energy and preexponential factor for a broad range of H-addition reactions. The group additive values are determined from CBS-QB3 ab initio calculated rate coefficients. A mean factor of deviation between the CBS-QB3 and the experimental rate coefficients for 7 reactions of only 2 in the range 300-1000 K is found. Tunneling coefficients for these reactions were found to be significant below 400 K and a correlation accounting for tunneling is presented. Application of the obtained group additive values to predict the kinetics for a set of 11 additions and β scissions yields rate coefficients within a factor 3.5 from the CBS-QB3 results except for 2 β scissions with severe steric effects. The mean factor of deviation with experimental rate coefficients is 2.0, showing that the group additive method with tunneling corrections can accurately predict the kinetics, at least as accurate as the most commonly used density functional methods. The constructed group additive model can hence be applied to predict the kinetics of hydrogen radical additions to a broad range of unsaturated compounds.
IntroductionThe addition of hydrogen radicals to alkenes and its reverse β scission, are important elementary steps in radical processes such as polymerization, pyrolysis, steam cracking, partial oxidation and combustion.[1] Therefore, the reaction family of hydrogen addition/β scission forms an indispensable part of any radical reaction network.A reliable reactor optimization requires an accurate kinetic model based on elementary reactions. For radical chemistry, on which many of the world largest scale chemical processes are based, the reactive nature of the radical intermediates results in huge reaction networks typically involving hundreds of species and thousands of elementary reactions. [2][3][4] Currently, most elementary reaction networks are automatically generated using advanced algorithms for the selection of the relevant reactions. [5][6][7][8][9][10][11] Sensitivity studies on these reaction networks point out that the main part of the uncertainty on the product yields stems from inaccurate knowledge of kinetic data. [12,13] Therefore, accurate kinetic data are essential to obtain reliable process simulations. Moreover, if rate-based network construction algorithms are applied, [14] accurate rate data are even more important as inaccuracies can result in the construction of an incomplete network that is not capable of grasping the underlying chemistry of the process.A quantitative description of radical processes thus requires that rate parameters be known for all of the different reactions comprising the reaction network. However, it is very difficult to measure kinetic parameters for individual radical r...