A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks

TL;DR

This paper presents a data-driven sparse-learning method to identify key reactions in chemical networks, enabling efficient mechanism reduction for combustion processes without extensive simulations.

Contribution

The paper introduces a novel sparse-learning approach for identifying influential reactions in chemical mechanisms, facilitating mechanism reduction with minimal computational effort.

Findings

The method accurately identifies influential reactions consistent with existing chemical knowledge.

Reduced mechanisms derived from the method perform comparably to full mechanisms across various conditions.

The approach requires no additional data or simulations beyond species concentrations and reaction rates.

Abstract

Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic representation of reactions captures the interactions between reacting species and products. Determining the minimal chemical interactions underlying the dynamic behavior of systems is a major task. In this paper, we introduce a novel approach for the identification of the influential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique. The proposed approach identifies a set of influential reactions using species concentrations and reaction rates, with minimal computational cost without requiring additional data or simulations. The new approach is applied to analyze the combustion chemistry of H2 and C3H8 in a constant-volume homogeneous reactor. The influential reactions identified by the sparse-learning method are consistent with the current kinetics knowledge of chemical mechanisms. Additionally, we show that a reduced version of the parent mechanism can be generated as a combination of the influential reactions identified at different times and conditions and that for both H2 and C3H8 this reduced mechanism performs closely to the parent mechanism as a function of ignition delay over a wide range of conditions. Our results demonstrate the potential of the sparse-learning approach as an effective and efficient tool for mechanism analysis and mechanism reduction.