Using Mathematical Modeling to Generate Training Data in Hydrotreating Processes

Maksim Babidorich, Alexander Demin, Olga Reutova, Elena Docenco

Abstract


The research presents a method for applying a static mathematical model in the process of generating a database for training an artificial neural network. The study was carried out on the example of predicting the physicochemical properties of a model of multicomponent mixture of diesel fuel and hydrogen-containing gas. Data generation is carried out by enumeration of mutable variables in the range of valid values. The following process parameters are chosen as variables: temperature, pressure and flow rate. It was found that changes in the chemical composition of the flows do not affect the result. Diesel fuel initial boiling point and the boiling point of 50%, 90% and 95% of the fraction changes on average by 1% ÷ 4%. Deviations of the calculated physical and chemical characteristics as a result of fluctuations in the content of hydrogen bearing gas components do not exceed 1.1%. As a result, a neural network was obtained, which determines the desired values with an error of 0.2%. This will allow the use of a neural network in dynamic systems for assessing process equipment fouling.

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References


S. Aguel, Z. Meddeb, and M.R. Jeday “Parametric study and modeling of cross-flow heat exchanger fouling in phosphoric acid concentra-tion plant using artificial neural network,” Journal of Process Control. vol. 84, 2019, pp. 133–145.

A.M. Demin and A.P. Naumenko “Diagnosis of heat exchange equipment based on operating parameters of a diesel fuel hydrotreater,” [in Russian] Omsk Scientific Bulleti. vol. 4, 2019, pp. 84–88.

M. Markowski and P. Trzcinski “On-line control of the heat exchanger network under fouling constraintsr,” Energyi. vol. 185, 2019, pp. 521–526.

D.K. Mohanty and P. Singru “Fouling analysis of a shell and tube heat exchanger using local linear wavelet neural network,” International Journal of Heat and Mass Transfer. vol. 77, 2014, pp. 946–955.

S. Tang, Y.-L. He, F.-L. Wang, Q. Zhao, and Y. Yu “Fouling analysis of a shell and tube heat exchanger using local linear wavelet neural network,” International Journal of Heat and Mass Transfer. vol. 77, 2014, pp. 946–955.

V.M. Kapustin and A.A. Gureev Oil refining technology. In 4 parts. Part two. Physical and chemical processes, [in Russian] Chemistry, Moscow, 2015.

F. Schollet Deep Learning with Python, [in Russian] Piter, St. Petersburg, 2018.


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