...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Fuel >The use of an artificial neural network to estimate natural gas/water interfacial tension
【24h】

The use of an artificial neural network to estimate natural gas/water interfacial tension

机译：使用人工神经网络估算天然气/水界面张力

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The gas/water interfacial tension (IFT) is an important property that influences many aspects within the petroleum industry, e.g., the vertical distribution of the hydrocarbons and multiphase flow calculations. Laboratory measurement of IFT usually requires an expensive experimental apparatus and a sophisticated interpretation procedure. This paper presents the use of the artificial neural network (ANN) to estimate the IFT in gas/water systems. A total of 956 sets of experimental data consisting of pure methane and synthetic natural gas were acquired from previous literature reports to develop the model. Seven factors were selected as independent variables to estimate IFT using multivariate parametric regression (MPR): temperature, pressure, mole fractions of the gas compositions (CO2, nitrogen, methane, and ethane), and salt (NaCl) concentration in water. A three-layered (7-19-1) ANN trained with the Levenberg-Marquardt back propagation algorithm was used. The mean absolute error, mean percentage error, root mean squared error, and determination coefficient for all of the datasets were calculated to be 0.81 mN/m, 1.97%, 1.25 mN/m and 0.992, respectively, demonstrating the high estimation accuracy and strong generalization capability of the model. The performance of the ANN was further compared with a newly proposed MPR model and three explicit empirical correlations found in previous literature reports. The comparison result suggests that the estimation accuracy can be improved significantly by using ANN compared with these four other correlations. (c) 2015 Elsevier Ltd. All rights reserved.
机译：气/水界面张力（IFT）是一种重要属性，会影响石油工业的许多方面，例如碳氢化合物的垂直分布和多相流计算。 IFT的实验室测量通常需要昂贵的实验设备和复杂的解释程序。本文介绍了使用人工神经网络（ANN）估算燃气/水系统中的IFT。从以前的文献报告中总共获得了956套由纯甲烷和合成天然气组成的实验数据以开发该模型。使用多元参数回归（MPR）选择七个因子作为自变量来估计IFT：温度，压力，气体成分的摩尔分数（CO2，氮气，甲烷和乙烷）以及水中的盐（NaCl）浓度。使用由Levenberg-Marquardt反向传播算法训练的三层（7-19-1）ANN。所有数据集的平均绝对误差，平均百分比误差，均方根误差和确定系数分别计算为0.81 mN / m，1.97％，1.25 mN / m和0.992，证明了较高的估计准确性和强大的模型的泛化能力。人工神经网络的性能进一步与新提出的MPR模型和先前文献报道中发现的三个明确的经验相关性进行了比较。比较结果表明，与其他四个相关性相比，使用ANN可以显着提高估计精度。 （c）2015 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Fuel》 |2015年第1期|28-36|共9页
  • 作者

    Zhang Jiyuan; Feng Qihong; Zhang Xianmin; Zhang Xueli; Yuan Nuo; Wen Shengming; Wang Shoulei; Zhang Angang;

  • 作者单位

    China Univ Petr East China, Sch Petr Engn, Qingdao, Peoples R China;

    China Univ Petr East China, Sch Petr Engn, Qingdao, Peoples R China;

    China Univ Petr East China, Sch Petr Engn, Qingdao, Peoples R China;

    China Univ Petr East China, Sch Petr Engn, Qingdao, Peoples R China;

    China Univ Petr East China, Sch Petr Engn, Qingdao, Peoples R China;

    PetroChina Coalbed Methane Co Ltd, Beijing, Peoples R China;

    China Natl Offshore Oil Corp, Res Inst, Beijing, Peoples R China;

    PetroChina Res Inst Petr Explorat & Dev, Beijing, Peoples R China;

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

    Interfacial tension; Artificial neural network; Natural gas; Multivariate parametric regression;

    机译：界面张力人工神经网络天然气多元参数回归;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号