Optimization of crude oil blending with neural networks

Proceedings of the IEEE Conference on Decision and Control

Volumn 5, Issue , 2004, Pages 4903-4908

  Yu, Wen ^a   De Jesús Rubio, José ^a   Morales, América ^b  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

^b INSTITUTO MEXICANO DEL PETRÓLEO   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; BLENDING; BOUNDARY VALUE PROBLEMS; CRUDE PETROLEUM; ERRORS; INTEGRAL EQUATIONS; LINEAR PROGRAMMING; MATHEMATICAL MODELS; NEURAL NETWORKS; NONLINEAR SYSTEMS; OIL FIELDS; OPTIMIZATION; PETROLEUM REFINING; SENSITIVITY ANALYSIS;

FLOW RATES; JACOBIAN MATRIX; MASS BALANCE; REAL-TIME OPTIMIZERS (RTO);

NONLINEAR PROGRAMMING;

EID: 14544273335     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. J.Alvarez-Ramirez, A.Morales, R.Suarez, Robustness of a class of bias update controllers for blending systems, Industrial Engineering Chemistry Research, Vol.41, No.19, 4786-4793, 2002
2. M.S.Bazaraa, C.Shetty, Nonlinear Programming: Theory and Algorithm, New York: Wiley, 1979.
3. J.B.Dennis, Mathematical Programming and Electrical Networks, Chapman & Hall, London, 1959
4. M.Forti, A.Tesi, New conditions for global stabilit of neural networks with application to linear and quadratic programming problems, IEEE Trans. Circuits and Systems, Part I, Vol.42, No.7, 354-366, 1995.
5. J.F.Forbes, T.E.Marlin, Model accuracy for economic optimizing controllers: the bias update case, Industrial Engineering Chemistry Research, Vol.33, 1919-1929, 1994.
6. J.H.Gary, G.E.Handwerk, Petroleum Refining Technology and Economics, Marcer Dekker, New York, 1994.
7. Z.P.Jiang and Y.Wang, Input-to-State Stability for Discrete-Time Nonlinear Systems, Automatica, Vol.37, No.2, 857-869, 2001.
8. X.B.Liang, J.Wang, A recurrent neural network for nonlinear optimization with a continuously differentiable object function and bound constraints, IEEE Trans. Neural Networks, Vol.11, No.6, 1251-1262, 2000.
9. D.G.Luenberger, Introduction to Linear and Nonlinear Programming, Reading, MA: Addison-Wesley, 1973.
10. W.L.Luyben, Process Modeling, Simulation and Control for Chemical Engineers, 2nd edition, McGraw-Hill, Inc., 1990.
11. R.T.Rockafeller, Convex Analysis, Princetron, NJ:Princetron University Press, 1979.
12. Y.Serpemem, F.W.Wenzel, A.Huubel, Blending technology key to making new gasoline, Oil Gas J, Vol.62, 89-101, 1991
13. A.Singh, J.F.Forbes, P.J.Vermeer, S.S.Woo, Model-based real-time optimization of automotive gasoline blending operations, Journal of Process Control, Vol.10, 43-58, 2000.
14. W.Yu, A.Morales, Modeling of Gasoline Blending Systems via Neural Networks, Technical Report of Instituto Mexicano del Petróleo, March, 2003.
15. W.Yu and X. Li, Some Stability Properties of Dynamic Neural Networks, IEEE Trans. Circuits and Systems, Part I, Vol.48, No.1, 256-259, 2001.
16. W.Yu and X. Li, Some New Results on System Identification with Dynamic Neural Networks, IEEE Trans. Neural Networks, Vol.12, No.2, 412-417, 2001.
17. W.Yu, A.S. Poznyak and X.Li, Multilayer Dynamic Neural Networks for Nonlinear System On-line Identification, International Journal of Control, Vol.74, No.18, 1858-1864,2001.
18. Wen Yu, América Morales, Gasoline blending system modeling via static and dynamic neural networks, International Journal of Modelling and Simulation, accepted for publication.
19. A.H.Zahed, S.A.Mullah and M.D.Bashir, Predict octane number for gasoline blends, Hydrocarbon Processing, N0.5, 85-87, 1993.
20. Y.Zhang, D.Monder and J.F.Forbes, Real-time optimization under parametric uncertainty a probability constrained approach, Journal of Process Control, Vol.12, 373-389, 2002.