Development of an Uncertainty Quantification Predictive Chemical Reaction Model for Syngas Combustion

Slavinskaya, N. A.; Abbasi, M.; Starcke, J. H.; Whitside, R.; Mirzayeva, A.; Riedel, U.; Li, W.; Oreluk, J.; Hegde, A.; Packard, A.; Frenklach, M.; Gerasimov, G.; Shatalov, O.

doi:10.1021/ACS.ENERGYFUELS.6B02319

Title: Development of an Uncertainty Quantification Predictive Chemical Reaction Model for Syngas Combustion

Journal Article · Mon Jan 23 23:00:00 EST 2017 · Energy and Fuels

DOI: https://doi.org/10.1021/ACS.ENERGYFUELS.6B02319 · OSTI ID:1362059

Slavinskaya, N. A. ^[1]; Abbasi, M. ^[2]; Starcke, J. H. ^[2]; Whitside, R. ^[2]; Mirzayeva, A. ^[2]; Riedel, U. ^[2]; Li, W. ^[3]; Oreluk, J. ^[3]; Hegde, A. ^[3]; Packard, A. ^[3];

^[3]; Gerasimov, G. ^[4]; Shatalov, O. ^[5]

Inst. of Combustion Technology, Stuttgart (Germany). German Aerospace Center (DLR); UNIVERSITY OF UTAH
Inst. of Combustion Technology, Stuttgart (Germany). German Aerospace Center (DLR)
Univ. of California, Berkeley, CA (United States). Dept. of Mechanical Engineering
Moscow State Univ., Moscow (Russian Federation). Inst. of Mechanics
Moscow State Univ., Moscow (Russian Federation). Inst. of Mechanics

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. Here, the initial H₂/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.

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Research Organization:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0002375

OSTI ID:: 1362059

Report Number(s):: DOE-UTAH-DENA--0002375-FRENKLACH-0009

Journal Information:: Energy and Fuels, Journal Name: Energy and Fuels Journal Issue: 3 Vol. 31; ISSN 0887-0624

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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