Abstract
Atmospheric distillation column plays a very important role in the crude oil processing. It is multivariate, strongly nonlinear process. Its operation and proper control impacts significantly the overall refinery performance. One may find in literature several papers describing column modeling with full spectrum of approaches staring from the first principle models up to regression black-boxes. Presented model composes of subsequent sections associated with technology. Each section is modeled as the nested NARIMA model. This methodology has been previously tested in several other chemical applications. It is extended by decomposition and coordination of subsequent sub-models in the considered case. Such a structure enables to obtain process nonlinear model with clear technological meaning of all considered elements. Further, it may be directly used in the process of control philosophy design. The procedure is illustrated with real data originating from the industrial installation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gani, R., Ruiz, C., Cameron, I.: A generalized model for distillation columns - I. Comput. Chem. Eng. 10(3), 181–198 (1986)
Cameron, I.T., Ruiz, C.A., Gani, R.: A generalized model for distillation columns - II. Comput. Chem. Eng. 10(3), 199–211 (1986)
Chaudhuri, U.R.: Fundamentals of Petroleum and Petrochemical Engineering. Chemical Industries. CRC Press, Boca Raton (2010)
Nelles, O.: Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Heidelberg (2001)
Hammerstein, A.: Nichtlineare Integralgleichungen nebst Anwendungen. Acta Math. 54(1), 117–176 (1930)
Nelles, O.: Process Dynamics and Control: Modeling for Control and Prediction. Wiley, Hoboken (2007)
Sindelar, R., Babuska, R.: Input selection for nonlinear regression models. IEEE Trans. Fuzzy Syst. 12(5), 688–696 (2004)
Isermann, R., Münchhof, M.: Identification of Dynamic Systems: An Introduction with Applications. Springer, Heidelberg (2011)
Bai, E.W.: A blind approach to Hammerstein model identification. IEEE Trans. Signal Process. 50(7), 1610–1619 (2002)
Vörös, J.: An iterative method for Hammerstein-Wiener systems parameter identification. J. Electr. Eng. 55(11), 328–331 (2004)
Wills, A., Schön, T.B., Ljung, L., Ninness, B.: Identification of Hammerstein-Wiener models. Automatica 49(1), 70–81 (2013)
Han, Y., de Callafon, R.: Closed-loop identification of Hammerstein systems using iterative instrumental variables. In: Proceedings of the 18th IFAC World Congress, Miedzyzdroje, Poland, vol. 18, pp. 13930–13935 (2011)
Haber, R., Keviczky, L.: Nonlinear System Identification - Input-Output Modeling Approach. Volume 2: Nonlinear System Structure Identification. Kluwer Academic Publishers, Dordrecht, Boston, London (1999)
Sun, N.Z., Sun, A.: Model Calibration and Parameter Estimation: For Environmental and Water Resource Systems. Springer, New York (2015)
Watkins, R.N.: Petroleum Refinery Distillation, 2nd edn. Gulf Publishing Company, Houston (1979)
Hsie, W.H.L., McAvoy, T.J.: Modeling, simulation and control of crude towers. Chem Eng Comm. 98, 1–29 (1990)
Green, D.W., Perry, R.H.: Perry’s Chemical Engineers’ Handbook, 8th edn. McGraw-Hill Professional, New York (2008)
Radulescu, G., Paraschiv, N., Marinoiu, V.: A model for the dynamic simulation of a crude oil unit. Control. Eng. Appl. Inform. 2(1), 43–50 (2000)
Domański, P.D., Golonka, S., Jankowski, R., Kalbarczyk, P., Moszowski, B.: Control rehabilitation impact on production efficiency of ammonia synthesis installation. Ind. Eng. Chem. Res. 55, 10366–10376 (2016). https://doi.org/10.1021/acs.iecr.6b02907
Domański, P.D., Ławryńczuk, M.: Assessment of predictive control performance using fractal measures. Nonlinear Dyn. 89, 773–790 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Falkowski, M., Domański, P.D. (2020). Nested NARIMA Model of the Atmospheric Distillation Column. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2019. AUTOMATION 2019. Advances in Intelligent Systems and Computing, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-030-13273-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-13273-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13272-9
Online ISBN: 978-3-030-13273-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)