An automated data-centric infrastructure, Process Informatics Model (PrIMe), was applied to validation and optimization of a syngas combustion model. The Bound-to-Bound Data Collaboration (B2BDC) module of PrIMe was employed to discover the limits of parameter modifications based on uncertainty quantification (UQ) and consistency analysis of the model−data system and experimental data, including shock-tube ignition delay times and laminar flame speeds. Existing syngas reaction models are reviewed, and the selected kinetic data are described in detail. Empirical rules were developed and applied to evaluate the uncertainty bounds of the literature experimental data. The initial H 2 /CO reaction model, assembled from 73 reactions and 17 species, was subjected to a B2BDC analysis. For this purpose, a dataset was constructed that included a total of 167 experimental targets and 55 active model parameters. Consistency analysis of the composed dataset revealed disagreement between models and data. Further analysis suggested that removing 45 experimental targets, 8 of which were self-inconsistent, would lead to a consistent dataset. This dataset was subjected to a correlation analysis, which highlights possible directions for parameter modification and model improvement. Additionally, several methods of parameter optimization were applied, some of them unique to the B2BDC framework. The optimized models demonstrated improved agreement with experiments compared to the initially assembled model, and their predictions for experiments not included in the initial dataset (i.e., a blind prediction) were investigated. The results demonstrate benefits of applying the B2BDC methodology for developing predictive kinetic models.

A skeletal chemical kinetic model for СН 4 /air combustion with 100 reactions and 24 chemical species was developed from the detailed mechanism, with 42 species and 298 reactions. The mechanism reduction was performed with the multi target reduction strategy realized in the in-house developed DLR RedMaster code. RedMaster is able to analyze the different chemical processes (ignition delay time and laminar flame speed) in the given time and height points. The obtained reduced model describes satisfactory experimental data for ignition delay and flame speed under conditions: p 5 = 1-50 bar, T 5 = 940K-210K, f = 0.5−2; p = 1-60 bar, T 0 = 300K, f = 0.6−1.4. Some problems related to reaction mechanism reduction are analyzed.

A reaction mechanism for cyclohexane (cyC 6 H 12) is developed to study its oxidation at both low and high temperatures, including PAH formation. Based on values of rate coefficients available in literature, uncertainty analyses has been performed for each reaction class included in the mechanism, and the optimum values are implemented in the cyC 6 H 12 sub-mechanism. Furthermore, reactions of bi-cyclic ethers and cylohexanone decompositions via further dehydrogenation steps and ring-opening leading to smaller olefins and ketone radicals were implemented. Thermochemical properties of the main species of the low-temperature sub-mechanism are determined by applying the group additivity method of Benson. The properties of some new Benson's groups, including ring corrections for cycling species were estimated. The ignition delay data from rapid compression machines (RCM) and shock tube experiments and also laminar flame speed data have been used for the model validation. The concentration profiles of the soot precursor species from burner-stabilized premixed flames experiments at different pressures and temperatures are well predicted by the current model.

-Numerical tool of Process Informatics Model (PrIMe) is mathematically rigorous and numerically efficient approach for analysis and optimization of chemical systems. It handles heterogeneous data and is scalable to a large number of parameters. The Boundto-Bound Data Collaboration module of the automated data-centric infrastructure of PrIMe was used for the systematic uncertainty and data consistency analyses of the H2/CO reaction model (73/17) and 94 experimental targets (ignition delay times). The empirical rule for evaluation of the shock tube experimental data is proposed. The initial results demonstrate clear benefits of the PrIMe methods for an evaluation of the kinetic data quality and data consistency and for developing predictive kinetic models.

A module of PrIMe automated data-centric infrastructure, Bound-to-Bound Data Collaboration (B2BDC), was used for the analysis of systematic uncertainty and data consistency of the H2/CO reaction model (73/17). In order to achieve this purpose, a dataset of 167 experimental targets (ignition delay time and laminar flame speed) and 55 active model parameters (pre-exponent factors in the Arrhenius form of the reaction rate coefficients) was constructed. Consistency analysis of experimental data from the composed dataset revealed disagreement between models and data. Two consistency measures were applied to identify the quality of experimental targets (Quantities of Interest, QoI): scalar consistency measure, which quantifies the tightening index of the constraints while still ensuring the existence of a set of the model parameter values whose associated modeling output predicts the experimental QoIs within the uncertainty bounds; and a newly-developed method of computing the vector consistency measure (VCM), which determines the minimal bound changes for QoIs initially identified as inconsistent, each bound by its own extent, while still ensuring the existence of a set of the model parameter values whose associated modeling output predicts the experimental QoIs within the uncertainty bounds. The consistency analysis suggested that elimination of 45 experimental targets, 8 of which were self- inconsistent, would lead to a consistent dataset. After that the feasible parameter set was constructed through decrease uncertainty parameters for several reaction rate coefficients. This dataset was subjected for the B2BDC framework model optimization and analysis on. Forth methods of parameter optimization were applied, including those unique in the B2BDC framework. The optimized models showed improved agreement with experimental values, as compared to the initially-assembled model. Moreover, predictions for experiments not included in the initial dataset were investigated. The results demonstrate benefits of applying the B2BDC methodology for development of predictive kinetic models.