Auto-thermal reforming (ATR) of natural gas: An automated derivation of optimised reduced chemical schemes

Abstract : A fully automated strategy is discussed to construct reduced chemistry suitable for the numerical simulation of stationary combustion systems of large dimension, such as auto-thermal reforming (ATR) of natural gas for syngas production. Because of computing limitations in terms of space and time resolution, three-dimensional simulations of an ATR unit cannot be addressed with detailed chemistry. A procedure is proposed to automatically derive optimised and reduced chemical schemes under specific ATR operating conditions. A stochastic model problem is first designed to probe the dynamical response of a detailed chemical scheme, over a large range of chemical compositions of the mixture. Reference composition space trajectories are built, featuring turbulent micro-mixing and reactions. Following these trajectories, the chemical response is analysed using directed relation graphs with error propagation combined with quasi-steady state hypothesis, to reduce the number of species and elementary reactions. Then, the time evolution of the model problem is coupled with a genetic algorithm, to optimise on the fly the chemical rates of the reduced kinetics, according to the reference composition space trajectories. The accuracy of the reduced scheme is monitored with a fitness function and the results are tested against the reference detailed chemistry.
Type de document :
Article dans une revue
Proceedings of the Combustion Institute, Elsevier, 2017, 36 (3), pp.3321-3330. 〈10.1016/j.proci.2016.07.110〉
Liste complète des métadonnées

https://hal-normandie-univ.archives-ouvertes.fr/hal-01657558
Contributeur : Luc Vervisch <>
Soumis le : mercredi 6 décembre 2017 - 20:48:42
Dernière modification le : mardi 5 juin 2018 - 10:14:21

Identifiants

Citation

Nicolas Jaouen, Luc Vervisch, Pascale Domingo. Auto-thermal reforming (ATR) of natural gas: An automated derivation of optimised reduced chemical schemes. Proceedings of the Combustion Institute, Elsevier, 2017, 36 (3), pp.3321-3330. 〈10.1016/j.proci.2016.07.110〉. 〈hal-01657558〉

Partager

Métriques

Consultations de la notice

72