Mathematical modeling and optimization of semi-regenerative catalytic reforming of naphtha
Division for Chemical Engineering, National Research Tomsk Polytechnic University, 30, Lenin Avenue, 634050 Tomsk, Russia
2 Well Testing Center “GasInformPlast”, 8, Razvitiya Avenue, 634055 Tomsk, Russia
3 Joint Stock Company “Tomsk Oil and Gas Research and Design Institute”, 72, Mira Avenue, 634027 Tomsk, Russia
* Corresponding author: email@example.com
Accepted: 20 July 2021
Catalytic naphtha reforming is extensively applied in petroleum refineries and petrochemical industries to convert low-octane naphtha into high-octane gasoline. Besides, this process is an important source of hydrogen and aromatics obtained as side products. The bifunctional Pt-catalysts for reforming are deactivated by coke formation during an industrial operation. This results to a reduction in the yield and octane number. In this paper modeling and optimization of a semi-egenerative catalytic reforming of naphtha is carried out considering catalyst deactivation and a complex multicomponent composition of a hydrocarbon mixture. The mathematical model of semi-egenerative catalytic reforming considering coke formation process was proposed. The operating parameters (yield, octane number, activity) for different catalysts were predicted and optimized. It was found that a decrease in the pressure range from 1.5 to 1.2 MPa at the temperature 478–481 °C and feedstock space velocity equal to 1.4–1 h induces an increase in the yield for 1–2 wt.% due to an increase in the aromatization reactions rate and a decrease in the hydrocracking reactions rate depending on the feedstock composition and catalyst type. It is shown that the decrease in pressure is limited by the requirements for the catalyst stability due to the increase in the coke formation rate. The criterion of optimality is the yield, expressed in octanes per tons.
© E. Ivanchina et al., published by IFP Energies nouvelles, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.