Development of Catalyst Grading System for Diesel Hydrodesulfurization Using Machine Learning Techniques

被引：1

作者：

Kurogi, Takayuki ^{[1
]}

Etou, Mayumi ^{[2
]}

Hamada, Rei ^{[1
]}

Sakai, Shingo ^{[1
]}

机构：

[1] JGC Catalysts & Chem Ltd, Petr Refining Catalysts Res Ctr, 13 2 Kitaminato Machi,Wakamatsu Ku, Kitakyushu, Fukuoka 8080027, Japan

[2] Oita Univ, Fac Sci & Technol, Dept Integrated Sci & Technol, 700 Dannoharu, Oita 8701192, Japan

来源：

JOURNAL OF THE JAPAN PETROLEUM INSTITUTE | 2022年 / 65卷 / 04期

关键词：

Hydrodesulfurization; Diesel; Catalyst grading; Machine learning; LIGHT CYCLE OIL; NIMO; COMO; ALUMINA;

D O I：

10.1627/jpi.65.150

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Improved catalyst performance for diesel hydrodesulfurization and meeting various requirements from each refinery was investigated using the machine learning technique to effectively and efficiently estimate the optimized grading ratio of selected hydrodesulfurization catalysts. A catalyst grading optimization program was developed based on "design of experiment" and "multi-objective optimization," in which virtual experiments and multi-objective optimization were carried out to estimate the grading ratio of the optimum catalyst to minimize the refined oil sulfur concentration under specific conditions. The calculated evaluation value of the optimized grading system obtained from our program showed quite good agreement with the pilot experimental results using the same system. In addition, the pilot evaluation showed superior HDS activity of the grading system obtained from our program compared to the single catalysts used for the grading system as well as other catalyst systems with other grading ratios.

引用

页码：150 / 155

页数：6

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