The behavior and chemical reactivity of a group of molecules are typically illustrated using intricate and extensive chemical reaction networks. These networks consist of a group of species and a series of reactions that detail their evolution. Although recent years have seen a surge in efforts to create numerical algorithms that generate dense chemical reaction networks with thousands of reactions and molecules, the simulation of these networks is a computationally demanding task, even for reaction mechanisms that describe the combustion of hydrocarbons. This paper introduces an innovative and unbiased approach to data-driven model reduction of extensive reaction networks called the SParse IdeNtification (SPIN) algorithm. SPIN combines tools from different domains to identify a set of crucial reactions using species concentrations and reaction rates, all while maintaining minimal computational costs and without requiring extra data or simulations. SPIN is successfully tested for large combustion networks of propane and n-heptane. The study demonstrates that, despite containing only one-fifth of the reactions found in the full mechanism, the SPIN reduced mechanism for n-heptane combustion serves as a highly accurate approximation of the original mechanism, with an average deviation of only 8.4% in ignition delay. Notably, this outstanding performance is achieved without bias towards any particular target property, such as ignition delay, as the reduction and model parameters are optimized to obtain the best possible results. We demonstrate that SPIN can operate as a standalone method or be hybridized with existing species-based reduction methods to further enhance its ability to identify the most significant reactions. This capability is particularly beneficial in comprehending the intricate mechanisms of combustion.
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