Uncertainty quantification and data consistency analysis for the development of hydrogen and syngas oxidation model

Abstract

The exponential growth of contemporary chemical kinetics data coupled with the potential inherent high uncertainty poses significant challenges for developing chemical kinetic models. To obtain a high-quality dataset for the optimization of H2 and syngas oxidation model, this study developed a framework for data consistency analysis and uncertainty analysis of chemical kinetic data. The framework combines simulations based on solving conservation equations, data-driven metaheuristic algorithms, and statistical analysis. Over 2000 experimental data points, including ignition delay times measured in shock tubes, laminar flame speeds, and concentration profiles measured in perfectly stirred reactors and plug flow reactors, were collected and used to construct the experimental dataset. Seventeen parameters of a detailed H2 and syngas oxidation model were selected to build the reaction rate constant (RRC) dataset. The initially estimated experimental uncertainties and RRC uncertainty factors were updated by slack variables in the iterative algorithm. Some high-uncertainty data were identified and excluded from the optimization dataset. The final experimental dataset achieves data consistency, and the feasible parameter set of RRC shows good prediction capability. The experimental data with high uncertainties were highlighted, and the uncertainty sources were analyzed.