Acid-catalyzed hydrocarbon transformations are essential for industrial processes, including oligomerization, cracking, alkylation, and aromatization. However, these chemistries are extremely complex, and computational (automatic) reaction network generation is required to capture these intricacies. The approach relies on the concept that underlying mechanisms for the transformations can be described by a limited number of reaction families applied to various species, with both gaseous and protonated intermediate species tracked. Detailed reaction networks can then be tailored to each industrially relevant process for better understanding or for application in kinetic modeling, which is demonstrated here. However, we show that these networks can grow very large (thousands of species) when they are bound by typical carbon number and rank criteria, and lumping strategies are required to decrease computational expense. For acid-catalyzed hydrocarbon transformations, we propose lumping isomers based on carbon number, branch number, and ion position to reach high carbon limits while maintaining the high resolution of species. Two case studies on propene oligomerization verified the lumping technique in matching a fully detailed model as well as experimental data.

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烯烃在酸性沸石上的催化转化：反应机理和集总技术的自动生成

酸催化烃转化对于工业过程至关重要，包括低聚、裂化、烷基化和芳构化。然而，这些化学反应极其复杂，需要计算（自动）反应网络生成来捕捉这些复杂性。该方法依赖于这样的概念，即转化的潜在机制可以通过应用于各种物种的有限数量的反应族来描述，同时跟踪气态和质子化中间物种。然后可以针对每个工业相关过程定制详细的反应网络，以便更好地理解或应用于动力学建模，这在此处进行了演示。然而，我们表明，当这些网络受到典型碳数和等级标准的约束时，它们可以变得非常大（数千种物种），并且需要集总策略来降低计算开销。对于酸催化的烃转化，我们建议基于碳数、支链数和离子位置的集总异构体以达到高碳限制，同时保持物种的高分辨率。两个关于丙烯低聚的案例研究验证了集总技术在匹配完全详细的模型和实验数据方面的作用。