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Group additivity methods simplify the determination of thermodynamic properties of a wide range of chemically related species involved in detailed reaction schemes. In this paper, we expand Benson's group additivity method to organosilanes. Based on quantum‐chemical calculations, the thermodynamic data of 22 stable silicon‐organic species are calculated, presented in the form of NASA polynomials, and compared to the available experimental data. Based on this theoretical database, a complete set of 24 Si‐ and C‐atom‐centered, single‐bonded and nonradical group additivity values for enthalpy of formation, standard entropy, and heat capacity at temperatures from 200 to 4000 K is derived through unweighted multivariate linear regression.

An automatic method for the reduction of chemical kinetic mechanisms under specific physical or thermodynamic conditions is presented. The method relies on the genetic algorithm search logic to gradually reduce the number of reactions from the detailed mechanism while still preserving its ability to describe the overall chemistry at an acceptable error. Accuracy of the reduced mechanism is determined by comparing its solution to the solution obtained with the full mechanism under the same initial and/or physical conditions. However, not only the chemical accuracy and the size of the mechanism are considered but also the time for its solution which helps to avoid stiff and slow converging mechanisms, thus preferring the fast solutions. The reduction method is demonstrated for a detailed mechanism for methane combustion, GRI‐Mech 3.0, which was reduced from 325 reactions and 53 species to 58 reactions and 26 species, and for an iron oxide formation mechanism from iron pentacarbonyl doped flames by Wlokas et al. (Int J Chem Kinet 2013, 45(8), 487–498),  originally consisting of 144 reactions and 34 species, which was reduced to 37 reactions and 24 species. The performance of the reduced mechanisms is shown for homogeneous constant pressure reactors and for burner‐stabilized flames. The results show a good agreement between reduced and full mechanisms for both the reactor and flame cases. The presented method is flexible and can be easily adjusted to either yield more accurate (but bigger) or smaller (but less accurate) reduced mechanisms, depending on the user's preference.

This paper describes an automatic method for the optimization of reaction rate constants of reduced reaction mechanisms. The optimization technique is based on a genetic algorithm that aims at finding new reaction rate coefficients that minimize the error introduced by the preceding reduction process. The error is defined by an objective function that covers regions of interest where the reduced mechanism may deviate from the original mechanism. The mechanism's performance is assessed for homogeneous reactor or laminar-flame simulations against the results obtained from a given reference-the original mechanism, another detailed mechanism, or experimental data, if available. The overall objective function directs the search towards more accurate reduced mechanisms that are valid for a given set of operating conditions. An optional feature to the objective function is a penalty term that permits to minimize the change to the reaction coefficients, keeping them as close as possible to the original value. This means that the penalty function can be used to constrain the reaction rates modifications during the optimization if needed. It is demonstrated that the penalty function is successful and can be combined with predefined uncertainty bounds for each reaction of the mechanism. In addition, the penalty function can be modified to achieve a further reduction of the mechanism. The algorithm is demonstrated for the optimization of a previously reduced variant of the GRI-Mech 3.0, a tert-butanol combustion mechanism by Sarathy et al. (Combust. Flame, 2012, 159, 2028-2055) and a hydrogen mechanism by Konnov (Combust. Flame, 2008, 152,

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