Abstract
Simulation and modeling technologies play an increasingly critical role in enhancing feedstock utilization, improving product quality, and enhancing enterprise competitiveness in the refining industry. Molecular-level kinetic models for refining processes typically involve highly detailed and complex data, significantly increasing computational costs. Additionally, the data granularity within these models fails to align with the practical requirements of industrial production. To address these, this study proposes the Reaction Network Coarse-Graining Integration (RNCGI) algorithm to achieve flexible adjustments to model complexity and data granularity. Species with similar molecular structures and kinetic behaviors within the reaction network are identified and integrated. Subsequently, a kinetic parameter mapping approach is developed to preserve the critical reaction behaviors. Finally, the reaction coarse-graining process significantly reduces the number of reactions within the network. The RNCGI algorithm was validated using the naphtha catalytic reforming process. The original complete network with 206 species nodes was integrated into two versions with different data granularities: a medium-scale network with 42 nodes and a small-scale network with 5 nodes. Both coarse-grained networks calculated the product composition accurately and provided clearer insights into core species transformation mechanisms compared to the original network. Additionally, the RNCGI algorithm ensures the completeness of reaction routes. By flexibly handling detailed data in molecular-level models, this work preserves the necessary complexity of models tailored to industrial needs, supporting the development of refinery-wide production planning and scheduling strategies.