Challenges for Selective Catalytic Naphtha Reforming Products Using Response Surface Methodology (RSM)

IF 1.3 4区工程技术 Q3 CHEMISTRY, ORGANIC

Petroleum Chemistry Pub Date : 2024-05-23 DOI:10.1134/s0965544124020099

Rand Q. Al-Khafaji, Duha Khalid, Muthana K. Al-Zaidi

{"title":"Challenges for Selective Catalytic Naphtha Reforming Products Using Response Surface Methodology (RSM)","authors":"Rand Q. Al-Khafaji, Duha Khalid, Muthana K. Al-Zaidi","doi":"10.1134/s0965544124020099","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3>The prediction of catalytic naphtha reforming products is one of the main challenges issues in oil sector. Investigating continues catalytic reforming (CCR) C5+, C1, C2, C3, and C4 are achieved by using response surface methodology (RSM). The process can be described in terms of several controllable variables which are research octane number (RON), naphthenes and aromatics. In present work, a quadratic polynomial equation for Naphtha C5+, SCFB H2 has been obtained by utilizing RSM and the results were tested by design of experiment (DOE) and ANOVA analysis. The experimental results show good agreement with the predicted model with a yield of C5+ ranging from 77.27 to 109 when the RON is in the range of 68 to 95, naphthenes (vol %) is in the range of 15 to 25 and aromatics (vol %) is in the range of 10 to 30. H2 yield varying from 0 to 1.37 is significantly affected by increasing C5+ and reduced by decreasing RON. The yield of other products is calculated by multiple regression analysis depending on C5+ conversion range 77–100. The yields of other products of reformer (C1, C2, C3, C4) can be calculated from correlation that developed using multiple regression analysis. This case study indicates that the statistical model is useful of CCR.","PeriodicalId":725,"journal":{"name":"Petroleum Chemistry","volume":"23 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Petroleum Chemistry","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1134/s0965544124020099","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, ORGANIC","Score":null,"Total":0}

引用次数: 0

Abstract

The prediction of catalytic naphtha reforming products is one of the main challenges issues in oil sector. Investigating continues catalytic reforming (CCR) C₅₊, C₁, C₂, C₃, and C₄ are achieved by using response surface methodology (RSM). The process can be described in terms of several controllable variables which are research octane number (RON), naphthenes and aromatics. In present work, a quadratic polynomial equation for Naphtha C₅₊, SCFB H₂ has been obtained by utilizing RSM and the results were tested by design of experiment (DOE) and ANOVA analysis. The experimental results show good agreement with the predicted model with a yield of C₅₊ ranging from 77.27 to 109 when the RON is in the range of 68 to 95, naphthenes (vol %) is in the range of 15 to 25 and aromatics (vol %) is in the range of 10 to 30. H₂ yield varying from 0 to 1.37 is significantly affected by increasing C₅₊ and reduced by decreasing RON. The yield of other products is calculated by multiple regression analysis depending on C₅₊ conversion range 77–100. The yields of other products of reformer (C₁, C₂, C₃, C₄) can be calculated from correlation that developed using multiple regression analysis. This case study indicates that the statistical model is useful of CCR.

Abstract Image

查看原文本刊更多论文

利用响应面方法 (RSM) 研究选择性催化石脑油转化产品面临的挑战

摘要石脑油催化重整产品的预测是石油行业面临的主要挑战之一。采用响应面方法（RSM）对持续催化重整（CCR）C5+、C1、C2、C3 和 C4 进行了研究。该过程可以用几个可控变量来描述，即研究辛烷值（RON）、环烷烃和芳烃。在本研究中，利用 RSM 获得了石脑油 C5+、SCFB H2 的二次多项式方程，并通过实验设计（DOE）和方差分析对结果进行了测试。实验结果表明，当 RON 在 68 至 95 之间，环烷烃（体积分数）在 15 至 25 之间，芳烃（体积分数）在 10 至 30 之间时，C5+ 的产率在 77.27 至 109 之间，与预测模型非常吻合。H2 收率在 0 至 1.37 之间，C5+ 的增加对其有显著影响，而 RON 的减少则会降低 H2 收率。其他产品的产率是通过多元回归分析计算得出的，取决于 C5+ 转化率范围 77-100。转化炉其他产品（C1、C2、C3、C4）的收率可通过多元回归分析得出的相关性计算得出。该案例研究表明，统计模型对 CCR 是有用的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Petroleum Chemistry 工程技术-工程：化工

CiteScore

2.50

自引率

21.40%

发文量

102

审稿时长

6-12 weeks

期刊介绍： Petroleum Chemistry (Neftekhimiya), founded in 1961, offers original papers on and reviews of theoretical and experimental studies concerned with current problems of petroleum chemistry and processing such as chemical composition of crude oils and natural gas liquids; petroleum refining (cracking, hydrocracking, and catalytic reforming); catalysts for petrochemical processes (hydrogenation, isomerization, oxidation, hydroformylation, etc.); activation and catalytic transformation of hydrocarbons and other components of petroleum, natural gas, and other complex organic mixtures; new petrochemicals including lubricants and additives; environmental problems; and information on scientific meetings relevant to these areas. Petroleum Chemistry publishes articles on these topics from members of the scientific community of the former Soviet Union.