Heavy naphtha fractions 85-155°C recycling in the catalytic reforming industrial unit

被引：4

作者：

Chernyakova, Ekaterina S. ^{[1
]}

Koksharov, Anton G. ^{[1
]}

Ivanchina, Emilia D. ^{[1
]}

Yakupova, Inna V. ^{[1
]}

机构：

[1] Natl Res Tomsk Polytech Univ, 30 Lenin Ave, Tomsk 634050, Russia

来源：

16TH INTERNATIONAL SCIENTIFIC CONFERENCE CHEMISTRY AND CHEMICAL ENGINEERING IN XXI CENTURY DEDICATED TO PROFESSOR L.P. KULYOV (CCE 2015) | 2015年 / 15卷

关键词：

catalytic reforming; mathematical modelling; product output; heavy naphtha; ALKYLATION; OPTIMIZATION; BENZENE;

D O I：

10.1016/j.proche.2015.10.060

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Catalytic naphtha reforming is a vital process for refineries due to the production of high-octane components, which is intensely demanded in our modern life. In these paper, the mathematical modelling method application for catalytic reforming installation of Komsomolsk oil-refinery is proposed. The mathematical model-based system "Catalyst Control" was used for catalytic reforming installation monitoring. The quality of the product from the unit was studied, with hydrocracking gasoline used as the feedstock. The impact of the feedstock on the product output was analyzed. It is shown that the feedstock replacement of catalytic reforming unit L-35-11/450K has positive impact on high-octane product output and increases the resource efficiency of the process. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：378 / 383

页数：6

共 14 条

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[3] Comparison between Artificial Neural Network and Rigorous Mathematical Model in Simulation of Industrial Heavy Naphtha Reforming Process
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[4] Heavy naphtha upgrading by catalytic reforming over novel bi-functional zeolite catalyst
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[8] Dynamic Modeling and Economic Model Predictive Control with Production Mode Switching for an Industrial Catalytic Naphtha Reforming Process
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[9] Catalytic conversion of heavy naphtha to reformate over the phosphorus-ZSM-5 catalyst at a lower reforming temperature
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[10] Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique
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