Dynamic modeling of an industrial diesel hydroprocessing plant by the method of continuous lumping. (2nd November 2015)

• Record Type: Journal Article
• Authors: Aydın, Erdal; Celebi, Ayse D.; Sildir, Hasan; Arkun, Yaman; Canan, Ummuhan; Is, Gamze; Erdogan, Murat
• Is Part Of: Computers & chemical engineering. Volume 82(2015)
• Page Start: 44
• Page End: 54
• Publication Date: 2015-11-02
• Subjects: Ultra low sulfur diesel -- Hydrodesulfurization -- Continuous lumping -- Dynamic reactor modeling -- Parameter estimation; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2015.06.005
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A dynamic model of a diesel hydro-processing plant (DHP) is developed. The reactor model is a pseudo-homogeneous, one dimensional, dynamic model. The continuous lumping method is used to model the hydro-desulfurization kinetics. Model parameters are estimated using industrial plant data. Both steady-state and dynamic model predictions match the plant data closely. Abstract: Diesel hydroprocessing is an important refinery process which consists of hydrodesulfurization to remove the undesired sulfur from the oil feedstock followed by hydrocracking and fractionation to obtain diesel with desired properties. Due to the new emission standards to improve the air quality, there is an increasing demand for the production of ultra low sulfur diesel fuel. This paper is addressing the development of a reliable dynamic process model which can be used for real-time optimization and control purposes to improve the process conditions of existing plants to meet the low-sulfur demand. The overall plant model consists of a hydrodesulfurization (HDS) model for the first two reactor beds followed by a hydrocracking (HC) model for the last cracking bed. The models are dynamic, non-isothermal, pseudo-homogeneous plug flow reactor models. Reaction kinetics are modeled using the method of continuous lumping which treats the reaction medium as a continuum of species whose reactivities depend on the true boiling point of the mixture. The key modeling parameters are estimated using industrial data. Steady-state and dynamic model predictions of the reactor bed temperatures, sulfur removal, and diesel production match closely the plant data.