View in EDS HTML Full Text PDF Full Text

Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability.

Saved in:
Bibliographic Details
Title: Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability.
Authors: Gao, Cen1 (AUTHOR), Lu, Pu-Huai1 (AUTHOR), Ye, Wei-Min1,2 (AUTHOR) ye_tju@tongji.edu.cn, Liu, Zhang-Rong1 (AUTHOR), Wang, Qiong1 (AUTHOR), Chen, Yong-Gui1 (AUTHOR)
Source: Environmental Science & Pollution Research. Mar2023, Vol. 30 Issue 13, p35872-35890. 19p.
Database: Environment Complete
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: eih
DbLabel: Environment Complete
An: 162677527
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Gao%2C+Cen%22">Gao, Cen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Pu-Huai%22">Lu, Pu-Huai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ye%2C+Wei-Min%22">Ye, Wei-Min</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ye_tju@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhang-Rong%22">Liu, Zhang-Rong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Qiong%22">Wang, Qiong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yong-Gui%22">Chen, Yong-Gui</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Environmental+Science+%26+Pollution+Research%22">Environmental Science & Pollution Research</searchLink>. Mar2023, Vol. 30 Issue 13, p35872-35890. 19p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eih&AN=162677527
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11356-022-24558-5
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 35872
    Titles:
      – TitleFull: Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Gao, Cen
      – PersonEntity:
          Name:
            NameFull: Lu, Pu-Huai
      – PersonEntity:
          Name:
            NameFull: Ye, Wei-Min
      – PersonEntity:
          Name:
            NameFull: Liu, Zhang-Rong
      – PersonEntity:
          Name:
            NameFull: Wang, Qiong
      – PersonEntity:
          Name:
            NameFull: Chen, Yong-Gui
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 03
              Text: Mar2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 09441344
          Numbering:
            – Type: volume
              Value: 30
            – Type: issue
              Value: 13
          Titles:
            – TitleFull: Environmental Science & Pollution Research
              Type: main
ResultId 1