New kinetic model of coal tar hydrogenation process via carbon number component approach. (1st January 2015)

• Record Type: Journal Article
• Authors: Dai, Fei; Gong, Maoming; Li, Chunshan; Li, Zengxi; Zhang, Suojiang
• Is Part Of: Applied energy. Volume 137(2015:Jan. 01)
• Page Start: 265
• Page End: 272
• Publication Date: 2015-01-01
• Subjects: Coal tar -- Kinetic model -- Hydrogenation process -- Carbon number component; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2014.10.009
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A kinetic model based on carbon number component approach is developed. Product streams are represented by the structural matrix at a molecular level. A transformation method is developed for interrelating properties and composition. Both the kinetic model and the transformation method present good prediction effect. Abstract: Hydrogenation technology is an important chemical upgrading process for low quality oil such as coal tar. Kinetic modeling for hydrogenation process remains a challenging task because of the large amount of compounds and complex reactions involved. Therefore, a new systematic methodology is proposed in this study to characterize mixture streams for the kinetic modeling of coal tar hydrogenation. The methodology incorporates both lumped method based on boiling point to represent feedstock and a carbon number-based component approach in the form of a structural matrix to characterize products at a molecular level. A mathematical transformation model is built for interrelating the bulk properties and molecular composition of products. A detailed kinetic model for coal tar hydrogenation is constructed based on the reaction pathway networks between lumped feedstock and carbon number-based molecular product. Detailed molecular compositions of the products are obtained from experiments to provide a basis for estimating the parameters of the kinetic model. The proposed method is verified by experiment results, which are consistent with predicted values.