Operational optimization of crude oil distillation systems using artificial neural networks

Computers and Chemical Engineering

Volumn 59, Issue , 2013, Pages 178-185

  Ochoa Estopier, Lluvia M ^a   Jobson, Megan ^a   Smith, Robin ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Heat exchanger networks; Heat integration; Product yields

Indexed keywords

CRUDE OIL DISTILLATION SYSTEMS; HEAT EXCHANGER NETWORK; HEAT INTEGRATION; OPERATING CONDITION; OPERATIONAL OPTIMIZATION; OPTIMIZATION FRAMEWORK; PRODUCT YIELDS; TWO-STAGE PROCEDURES;

CRUDE OIL; DISTILLATION; HEAT EXCHANGERS; NEURAL NETWORKS; OPTIMIZATION; WASTE HEAT;

DISTILLATION COLUMNS;

EID: 84886382541     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2013.05.030     Document Type: Article

References (36)

1. Abilov A., Zeybek Z. Use of neural network for modeling of non-linear process integration technology in chemical engineering. Chemical Engineering and Processing 2000, 39(5):449-458.
2. Alhajree I., Zahedi G., Manan Z.A., Zadeh S.M. Modeling and optimization of an industrial hydrocracker plant. Journal of Petroleum Science and Engineering 2011, 78(September (3-4)):627-636.
3. Bagajewicz M., Ji S. Rigorous procedure for the design of conventional atmospheric crude fractionation units. Part I: Targeting. Industrial & Engineering Chemistry Research 2001, 40(January (2)):617-626.
4. Basak K., Abhilash K.S., Ganguly S., Saraf D.N. On-line optimization of a crude distillation unit with constraints on product properties. Industrial & Engineering Chemistry Research 2002, 41(Mar (6)):1557-1568.
5. Beale M.H., Hagan M.T., Demuth H.B. Neural network toolbox user's guide 2011, The MathWorks, Inc., Natick, MA.
6. Bellos G.D., Kallinikos L.E., Gounaris C.E., Papayannakos N.G. Modelling of the performance of industrial HDS reactors using a hybrid neural network approach. Chemical Engineering and Processing: Process Intensification 2005, 44(May (5)):505-515.
7. Chen L. Heat-integrated crude oil distillation design 2008, University of Manchester, Manchester, (PhD Thesis).
8. Gadalla M. Retrofit of heat integrated crude oil distillation systems 2003, University of Manchester Institute of Science and Technology, Manchester, (PhD Thesis).
9. Hartmann J.C.M. Determine the optimum crude intake level - a case history. Hydrocarbon Processing 2001, 80(6).
10. Heaton J. Introduction to Neural Networks with Java 2005, Heaton Research, Inc., Chesterfield. 2nd ed.
11. Himmelblau D. Applications of artificial neural networks in chemical engineering. Korean Journal of Chemical Engineering 2000, 17(4):373-392.
12. Inamdar S.V., Gupta S.K., Saraf D.N. Multi-objective optimization of an industrial crude distillation unit using the elitist non-dominated sorting genetic algorithm. Chemical Engineering Research and Design 2004, 82(A5):611-623.
13. Kirkpatrick S., Gelatt C. Optimization by simulated annealing. Science 1983, 220(4598):671-680.
14. Liau L.C.-K., Yang T.C.-K., Tsai M.-T. Expert system of a crude oil distillation unit for process optimization using neural networks. Expert Systems with Applications 2004, 26(February (2)):247-255.
15. Liebmann K., Dhole V.R., Jobson M. Integrated design of a conventional crude oil distillation tower using pinch analysis. Chemical Engineering Research and Design 1998, 76:335-347.
16. López D.C., Mahecha C. Optimization model of a system of crude oil distillation units with heat integration and metamodeling. Ciencia, Tecnología y Futuro 2009, 3:159-174.
17. Motlaghi S., Jalali F., Ahmadabadi M. An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework. Expert Systems with Applications 2008, 35(November (4)):1540-1545.
18. Narasimha P.L., Delashmit W.H., Manry M.T., Li J., Maldonado F. An integrated growing-pruning method for feedforward network training. Neurocomputing 2008, 71(August (13-15)):2831-2847.
19. Nascimento C.A.O., Giudici R., Guardani R. Neural network based approach for optimization of industrial chemical processes. Computers & Chemical Engineering 2000, 24(October (9-10)):2303-2314.
20. Nolfi S., Parisi D. Evolution of artificial neural networks. The handbook of brain theory and neural networks 2002, 418-421. The MIT Press, London. M. Arbib (Ed.).
21. Osman E.A., Fahd K., Arabia S. Using artificial neural networks to develop new PVT correlations for Saudi crude oils. Abu Dhabi international petroleum exhibition and conference 2002, 1-15.
22. Palmer K., Realff M. Metamodeling approach to optimization of steady-state flowsheet simulations. Chemical Engineering Research and Design 2002, 80(October (7)):760-772.
23. Rastogi V. Heat-integrated crude oil distillation system design 2006, University of Manchester, Manchester, (PhD Thesis).
24. Rodríguez C.A. Fouling mitigation strategies for heat exchanger networks 2005, University of Manchester, Manchester, (PhD Thesis).
25. Sarle W.S. Stopped training and other remedies for overfitting. Proceedings of the 27th symposium on the interface of computer science and statistics 1995, 352-360.
26. Seo J.W., Oh M., Lee T.H. Design optimization of a crude oil distillation process. Chemical Engineering & Technology 2000, 23(February (2)):157-164.
27. Shirvany Y., Zahedi G., Bashiri M. Estimation of sour natural gas water content. Journal of Petroleum Science and Engineering 2010, 73(August (1-2)):156-160.
28. Smith R. Chemical process design and integration 2005, John Wiley & Sons, Ltd., Chichester.
29. Smith R., Jobson M., Chen L. Recent development in the retrofit of heat exchanger networks. Applied Thermal Engineering 2010, 30(November (16)):2281-2289.
30. Stanley K., Miikkulainen R. Efficient evolution of neural network topologies. CEC'02: Proceedings of the 2002 congress of evolutionary computation 2002, 1757-1762.
31. Suphanit B. Design of complex distillation systems 1999, University of Manchester Institute of Science and Technology, Manchester, (PhD Thesis).
32. Uppaluri R., More R.K., Bulasara V.K., Banjara V.R. Optimization of crude distillation system using Aspen Plus: Effect of binary feed selection on grass-root design. Chemical Engineering Research and Design 2010, 88(February (2)):121-134.
33. Wang W., Van Gelder P.H.A.J.M., Vrijling J.K. Some issues about the generalization of neural networks for time series prediction. ICANN'05: Proceedings of the 15th international conference on artificial neural networks: Formal models and their applications 2005, 559-564. Springer-Verlag, Berlin.
34. Watkins R.N. Petroleum refinery distillation 1979, Gulf Publishing Company, Texas. 2nd ed.
35. Zahedi G., Lohi A., Karami Z. A neural network approach for identification and modeling of delayed coking plant. International Journal of Chemical Reactor Engineering 2009, 7.
36. Zhao W., Chen D., Shangxu H. Optimizing operating conditions based on ANN and modified gas. Computers & Chemical Engineering 2000, 24:61-65.