Chin.J.Chem.Eng. ›› 2018, Vol. 26 ›› Issue (5): 1102-1109.doi: 10.1016/j.cjche.2018.01.002

• Process Systems Engineering and Process Safety • Previous Articles     Next Articles

Modeling and optimization of industrial Fischer-Tropsch synthesis with the slurry bubble column reactor and iron-based catalyst

Chufu Li   

  1. National Institute of Clean-and-Low-Carbon Energy, Shenhua Group Co. Ltd., Beijing 102211, China
  • Received:2017-10-25 Revised:2018-01-01 Online:2018-05-28 Published:2018-06-29
  • Supported by:

    Supported by the National Key R&D Program of China (2017YFB0602500)

Abstract: To optimize industrial Fischer-Tropsch (FT) synthesis with the slurry bubble column reactor (SBCR) and ironbased catalyst, a comprehensive process model for FT synthesis that includes a detailed SBCR model, gas liquid separation model, simplified CO2 removal model and tail gas cycle model was developed. An effective iteration algorithm was proposed to solve this process model, and the model was validated by industrial demonstration experiments data (SBCR with 5.8 m diameter and 30 m height), with a maximum relative error < 10% for predicting the SBCR performances. Subsequently, the proposed model was adopted to optimize the industrial SBCR performances simultaneously considering process and reactor parameters variations. The results show that C5+ yield increases as catalyst loading increases within 10-70 ton and syngas H2/CO value decreases within 1.3-1.6, but it doesn't increase obviously when the catalyst loading exceeds 45 ton (about 15 wt% concentration). Higher catalyst loading will result in higher difficulty for wax/catalyst separation and higher catalyst cost. Therefore, the catalyst loading (45 ton) is recommended for the industrial demonstration SBCR operation at syngas H2/CO=1.3, and the C5+ yield is about 402 ton" per day, which has an about 16% increase than the industrial demonstration run result.

Key words: Fischer-Tropsch synthesis, Slurry bubble column reactor, Double bubble model, Process simulation and optimization