| 초록 |
Atmospheric residue desulfurization (ARDS) process treats high sulfur atmospheric residue (HS-AR) as a feed to remove impurities such as sulfur, nitrogen, conradson carbon residue (CCR), and metals (Ni, V, etc.). The catalyst to remove impurities causes deactivation as time goes by, and the process of replacing the catalyst costs a lot. Therefore, it is necessary to construct a simulation model that predicts the conversion along with the degree of catalyst aging. The model will be used to determine operating conditions that keep the yield at an appropriate level. ARDS process can be divided into feed preheat, reaction, separation, and fractionation parts. The reaction part is represented using a data-driven AI model, and the remaining parts are modeled using Aspen HYSYS. Finally, by integrating these two models, a simulation model was built to predict the degree of catalyst aging. This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (No. 22ATOG-C162087-02), and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2021R1C1C1004217). |
