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Molecular Representation of the Petroleum Gasoline Fraction

机译:石油汽油馏分的分子表示

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摘要

The computer-aided reconstruction of gasoline composition is an active area of petroleum and petrochemical research as a result of the demand for molecular-level management of the petroleum feed streams. To that end, in this work, a molecular compositional model based on a predefined representative molecular set was built that allows for the conversion of conventional bulk property data to an approximate molecular composition. The selection of representative molecules was based on their presence in gasoline molecular compositional measurement and their potential contribution to the key physical properties. Around 170 hydrocarbons and heteroatom species were chosen as predefined identities of molecules that can exist in a gasoline sample. The physical property data of all of the representative molecules were collected, and suitable mixing rules for the gasoline range stream were applied for the accurate prediction of bulk properties. The approximate concentration of representative molecules was obtained through fitting the predicted bulk property to the measured data. The methodology was verified through intensive tests on various gasoline samples, including straight-run naphtha, catalytic cracking gasoline, coking gasoline, and reformates. The modeling was also accomplished in a sequential order using basic to advanced measurements to find the optimum number of measurements required for detailed composition evaluation on various feedstocks. The propagation of error in the experimental measurement and prediction method on composition has been evaluated.
机译:由于需要对石油原料流进行分子级管理,因此计算机辅助的汽油成分重建是石油和石化研究的活跃领域。为此,在这项工作中,建立了基于预定义的代表性分子集的分子组成模型,该模型可以将常规的整体性质数据转换为近似的分子组成。代表性分子的选择基于其在汽油分子组成测量中的存在及其对关键物理性能的潜在贡献。选择了约170种碳氢化合物和杂原子物种作为汽油样品中可能存在的分子的预定义标识。收集所有代表性分子的物理性质数据,并将适用于汽油范围物流的合适混合规则应用于准确预测体积性质。通过将预测的整体性质与测量数据进行拟合,可获得代表性分子的近似浓度。通过对各种汽油样品(包括直馏石脑油,催化裂化汽油,焦化汽油和重整产品)的密集测试验证了该方法学。还使用基本到高级的测量顺序地完成建模,以找到对各种原料进行详细组成评估所需的最佳测量数量。评估了误差在实验测量和预测方法中对成分的传播。

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  • 来源
    《Energy & fuels》 |2018年第2期|1525-1533|共9页
  • 作者单位

    China Univ Petr, State Key Lab Heavy Oil Proc, Beijing 102249, Peoples R China;

    Univ Delaware, Dept Chem & Biomol Engn, Newark, DE 19716 USA;

    China Univ Petr, State Key Lab Heavy Oil Proc, Beijing 102249, Peoples R China;

    China Univ Petr, State Key Lab Heavy Oil Proc, Beijing 102249, Peoples R China;

    China Univ Petr, State Key Lab Heavy Oil Proc, Beijing 102249, Peoples R China;

    Univ Delaware, Dept Chem & Biomol Engn, Newark, DE 19716 USA;

    China Univ Petr, State Key Lab Heavy Oil Proc, Beijing 102249, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 00:39:05

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