Kinetic modeling with automatic reaction network generator, an application to naphtha steam cracking. (15th September 2020)

• Record Type: Journal Article
• Authors: Ren, Yu; Guo, Gaoshun; Liao, Zuwei; Yang, Yao; Sun, Jingyuan; Jiang, Binbo; Wang, Jingdai; Yang, Yongrong
• Is Part Of: Energy. Volume 207(2020)
• Publication Date: 2020-09-15
• Subjects: Kinetic modeling -- Automatic generator -- Naphtha -- Steam cracking; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118204
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurate process simulation models of naphtha steam cracking are significant for improving product yields, reducing energy consumption, and process control. However, the complete mechanistic model for steam cracking of naphtha feedstock is not available in the literature. It is a challenging task to build large-scale detailed kinetic models of such complex mixtures. The application of automatic reaction network generation is a possible way to solve this problem efficiently. An open-source automatic reaction network generator RMG is used to construct the detailed mechanistic model of naphtha steam cracking. A step-by-step construction methodology that starts with automatic reaction network generation for a single hydrocarbon and then merges reaction networks of all considered hydrocarbons according to specific merging rules is proposed. The final merged model contains 1947 species and 82130 reactions. The pyrolysis of n-decane in the literature and a set of naphtha steam cracking experiments are used to verify the accuracy of the generated reaction network for single hydrocarbon and naphtha mixture, respectively. The results show that the simulated yields of major products are in overall agreement with experiments, and the deviations of minor products are within an acceptable range. Highlights: Automatic reaction network generation is applied on naphtha steam cracking. Detailed free-radicals kinetic model is constructed by a step-by-step methodology. The steps include components selection, network generation and merge rules. The simulated yields of major products are in overall agreement with experiments. The deviations of minor products are within an acceptable range.