ИСТИНА

An automated data-centric infrastructure, Process Informatics Model (PrIMe), was applied for validation and optimization of a syngas combustion model. The Bound-to-Bound Data Collaboration (B2B-DC) module of PrIMe was employed to discover the limits of parameter modifications based on the systematic uncertainty and consistency analysis of the model-data system, with experimental data including shock-tube ignition delay times and laminar flame speeds. The initial H2/CO reaction model, assembled from 73 reactions and 17 species, was subjected to a B2B-DC 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 B2B-DC framework. The optimized models demonstrated improved agreement with experiment, as compared to the initially-assembled model, and their predictions for experiments not included in the initial dataset (i.e. a blind prediction) were investigated.