Prediction of sulphur content in the industrial hydrotreatment process

Fuel Processing Technology

Volumn 89, Issue 3, 2008, Pages 292-300

  Lukec, Ivana ^a   Sertić Bionda, Katica ^b   Lukec, Darko ^a  

^a Ziegler doo   (Croatia)

^b UNIVERSITY OF ZAGREB   (Croatia)

Author keywords

Hydrotreatment; Neural networks; Quality control; Soft sensor

Indexed keywords

GAS OILS; NEURAL NETWORKS; OPTIMIZATION; QUALITY CONTROL; THERMODYNAMICS; WATER TREATMENT;

HYDROTREATMENT; SOFT SENSORS;

SULFUR;

EID: 39049122107     PISSN: 03783820     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fuproc.2007.11.032     Document Type: Article

References (12)

1. Lin B., Recke B., Knudsen J.K.H., and Jørgensen S.B. A systematic approach for soft sensor development. Comput. Chem. Eng. 31 (2007) 419-425
2. Sertić-Bionda K., Gomzi Z., and Šarić T. Testing of hydrodesulfurization process in small trickle-bed reactor. Chem. Eng. J. 106 (2005) 105-110
3. Bhaskar M., Valavarasu G., Sairam B., Balaraman K.S., and Balu K. Three-phase reactor model to simulate the performance of pilot-plant and industrial trickle-bed reactors sustaining hydrotreating reactions. Ind. Eng. Chem. Res. 43 (2004) 6654-6669
4. Froment G.F. Modeling in the development of hydrotreatment processes. Catal. Today 98 (2004) 43-54
5. Rodriguez M.A., and Ancheyta J. Modeling of hydrodesulfurization (HDS), Hydrodenitrogenation (HDN), and the Hydrogenation of Aromatics (HDA) in Vacuum Gas Oil Hydrotreater. Energy Fuels 18 (2004) 789-794
6. Zhong W., and Yu J. Improve nonlinear soft sensing modeling by combining multiple models. Hydrocarbon process. (2000) 108-112 April
7. Korsten H., and Hoffmann U. Three-phase reactor model for hydrotreating in pilot trickle-bed reactors. AIChE J. 42 5 (1996) 1350-1360
8. Macías M.J., and Ancheyta J. Simulation of an isothermal hydrodesulfurization small reactor with different catalyst particle shapes. Catal. Today 98 (2004) 243-252
9. Bellos G.D., Kallinikos L.E., Gounaris C.E., and Papayannakos N.G. Modelling of the performance of industrial HDS reactors using a hybrid neural network approach. Chem. Eng. Process. 44 (2005) 505-515
10. Barsamian A., and Macias J. Inferential property predictors using neural networks. Hydrocarbon Process. (1998) 107-112 Oct.
11. Principe J.C., Euliano N.R., and Lefebvre W.C. Neural and Adaptive Sistems: Fundamentals through Simulation (2000), John Wiley & Sons, New York
12. Molga E.J., van Woezik B.A.A., and Westerterp K.R. Neural networks for modelling of chemical reaction systems with complex kinetics: oxidation of 2-octanol with nitric acid. Chem. Eng. Process. 39 (2000) 323-334