Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes16-17331
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
Creative Commons License

Volume 16 article 525 pages: 246 - 253

Vladimir Bukhtoyarov
Siberian Federal University, Krasnoyarsk, Russia

Vadim Tynchenko
Siberian Federal University, Krasnoyarsk, Russia

Eduard Petrovskiy
Siberian Federal University, Krasnoyarsk, Russia

Natalia Bukhtoyarova
Siberian Federal University, Krasnoyarsk, Russia

Vadim Zhukov
Siberian Federal University, Krasnoyarsk, Russia

The current state of production systems of the oil and gas sector makes high demands on the reliability of decision making at the operational level of process facilities control. In many situations, petroleum refining requires decision support based on predictive models. Such models should be accurate and computationally effective, which makes high demands on the selection of effective methods for constructing models of oil refinery facilities, in particular, rectification columns. The purpose of the research is to provide such requirements, the authors investigate several methods for constructing models of process technologies (facilities), estimating their accuracy based on the data of the actual rectification process technology. On average, multivariate adaptive regression splines were most effective results obtained on the set of model parameters. This method, using the samples of observations considered in the paper, allows building models with an average (referring to a set of simulated parameters) simulation error of 8.2%. The results indicate that it is possible to replace “conventional” models with fast regression models. Calculation and parameter prediction for technological facilities, including for generating control actions, using such models is possible in real time mode.

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This study has been undertaken as part of the research into the subject MK-1574.2017.8  “Designing the expert system of the analysis and control of reliability, risks and emergencies in support of the operation of petroleum refinery equipment” funded by the Grant Advisory Board for the President of the Russian Federation in a bid to provide governmental support to young Russian scientists.

1. Qingfeng, W., Wenbin, L., Xin, Z., Jianfeng, Y., Qingbin, Y. (2011). Development and application of equipment maintenance and safety integrity management system. Journal of Loss Prevention in the Process Industries, vol. 24, 321-332.

2. Bertolini, M., Bevilacqua, M., Ciarapica, F.E., Giacchetta, G. (2009). Development of risk-based inspection and maintenance procedures for an oil refinery. Journal of Loss Prevention in the Process Industries, vol. 22, 244-253.

3. Wolf, F.G. (2001). Operationalizing and testing normal accident theory in petrochemical plants and refineries. Production and Operations Management, vol. 10, 292-305.

4. Ancheyta, J. (2011). Modeling and simulation of catalytic reactors for petroleum refi ning. John Wiley & Sons.

5. Rivero, R., Garcia, M., Urquiza, J. (2004). Simulation, exergy analysis and application of diabatic distillation to a tertiary amyl methyl ether production unit of a crude oil refinery. Energy, vol. 29, 467-489.

6. Speight, J. G. (2014). The chemistry and technology of petroleum. CRC press.

7. Dimian, A. C., Bildea, C. S., & Kiss, A. A. (2014). Integrated design and simulation of chemical processes (Vol. 13). Elsevier.

8. Ansari, R.M., Tadé, M.O. (2012). Nonlinear model-based process control: applications in petroleum refining. Springer Science & Business Media.

9. Sildir, H., Arkun, Y., Canan, U., Celebi, S., Karani, U., & Er, I. (2015). Dynamic modeling and optimization of an industrial fluid catalytic cracker. Journal of Process Control, 31, 30-44.

10. Espada, J.J., Coto, B., van Grieken, R., Moreno, J.M. (2008). Simulation of pilot-plant extraction experiments to reduce the aromatic content from lubricating oils. Chemical Engineering and Processing: Process Intensification, 47: 1398-1403.

11. Nimmagadda, S.L., Rudra, A. (2017). Visualization as a Big Data Artefact for Knowledge Interpretation of Digital Petroleum Ecosystems. International SERIES on Information Systems and Management in Creative eMedia (CreMedia), vol. 2016, no. 2, 34-43.

12. He, S., Lucio-Vega, J., Zhang, L., Shi, Q., Horton, S.R., Al-Khattaf, S.S.F, Klein, M.T. (2017). Integrating Visualization Methods with Chemical Kinetics Model Solution and Editing Tools. Energy Fuels, vol. 31, 9881–9889.

13. Chatterjee, S., & Hadi, A. S. (2015). Regression analysis by example. John Wiley & Sons.

14. Draper, N. R., & Smith, H. (2014). Applied regression analysis (Vol. 326). John Wiley & Sons.

15. Dimian, A.C., Bildea, C.S., Kiss, A.A. (2014). Integrated design and simulation of chemical processes. Elsevier.

16. Ramzan, N., Naveed, S., Muneeb, R., Tahir, F.M. (2013). Simulation of natural gas processing plant for bumpless shift. NFC IEFR Journal of Engineering and Scientific Research, vol. 1.

17. Chaudhuri, U.R. (2010). Fundamentals of petroleum and petrochemical engineering. CRC Press.

18. Demuth, H.B., Beale, M.H., De Jess, O., Hagan, M.T. (2014). Neural network design. Martin Hagan.

19. Ochoa-Estopier, L.M., Jobson, M., Smith, R. (2013). Operational optimization of crude oil distillation systems using artificial neural networks. Computers & Chemical Engineering, vol. 59, 178-185.

20. Zhang, W., & Goh, A. T. (2016). Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers, 7(1), 45-52.

21. Milborrow, S., Hastie, T., Tibshirani, R., Miller, A., & Lumley, T. (2015). earth: Multivariate adaptive regression splines. R package version, 4(0).

22. Breiman, L. (2017). Classifi cation and regression trees. Routledge.

23. Langdon, W., Poli, R. (2013). Foundations of genetic programming. Springer Science & Business Media.

24. Hart, J. (2013). Nonparametric smoothing and lackof-fit tests. Springer Science & Business Media.

25. Li, X., Engelbrecht, A., Epitropakis, M.G. (2013). Benchmark functions for CEC’2013 special session and competition on niching methods for multimodal function optimization. RMIT University, Evolutionary Computation and Machine Learning Group, Australia, Tech. Rep.

26. Ge, S.S., Hang, C.C., Lee, T.H., Zhang, T. (2013). Stable adaptive neural network control. Springer Science & Business Media.

27. Kisi, O. (2015). Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 528, 312 320.

28. Whitley, D. (2014). An executable model of a simple genetic algorithm. Foundations of Genetic Algorithms, 2: 45-62.
29. Fu, M. C. (2016). Handbook of simulation optimization. Springer.