View in EDS

Prediction of deep learning algorithms for the microemulsion generation conditions in complex environments.

Saved in:
Bibliographic Details
Title: Prediction of deep learning algorithms for the microemulsion generation conditions in complex environments.
Authors: Li, Hao1 (AUTHOR), Ding, Tianshun1 (AUTHOR), Tao, Shengyang1 (AUTHOR) taosy@dlut.edu.cn
Source: Journal of the Taiwan Institute of Chemical Engineers. Feb2026, Vol. 179, pN.PAG-N.PAG. 1p.
Subjects: Microemulsions, Microfluidics, Prediction models, Machine learning, Deep learning, Fluid dynamics, Convolutional neural networks
Abstract: • RAM-CNN achieves 100 % fluid and 95.8 % droplet morphology recognition accuracy. • GAN-LGBMnet enhances small-sample learning with MAPE <7 % for droplet prediction. • User-friendly GUI enables ≤2-click workflows for non-expert microfluidic design. • MO-DNN predicts single/double emulsion parameters with <10 % error on unseen data. Constructing high-quality datasets for AI-driven microemulsion prediction remains challenging due to limitations in chip design, imaging hardware, and fluid dynamics. Conventional semi-empirical models suffer from poor accuracy and generalizability in complex nonlinear systems, while traditional microfluidic chips often yield non-spherical droplets. Optical constraints in capillary-based systems further hinder data acquisition. A deep learning framework integrates three components: (1) A multi-branch CNN with residual modules and self-attention (RAM-CNN) for robust droplet/fluid morphology recognition; (2) GAN-LGBMnet, combining adversarial networks and LightGBM, to augment small datasets and analyze key features; (3) A multi-output neural network (MO-DNN) predicting single/double-emulsion parameters. The system is deployed via an intuitive GUI (MICA) for non-specialist use. RAM-CNN achieves 100 % fluid-regime and 95.8 % droplet-morphology recognition accuracy, maintaining >91 % performance under ±28 % brightness variations. Enhanced by GAN-LGBMnet, MO-DNN predicts droplet diameters and generation rates with MAPE <7 % for both emulsion types. The MICA platform demonstrates <10 % error on unseen data, enabling precise emulsion design. This work bridges theoretical models with practical microfluidic optimization through automated, user-friendly AI tools. [Display omitted] [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Taiwan Institute of Chemical Engineers is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 190825523
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Prediction of deep learning algorithms for the microemulsion generation conditions in complex environments.
– Name: Author
  Label: Authors
  Group: Au
  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Li%2C+Hao%22&quot;&gt;Li, Hao&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Ding%2C+Tianshun%22&quot;&gt;Ding, Tianshun&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Tao%2C+Shengyang%22&quot;&gt;Tao, Shengyang&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; taosy@dlut.edu.cn&lt;/i&gt;
– Name: TitleSource
  Label: Source
  Group: Src
  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Journal+of+the+Taiwan+Institute+of+Chemical+Engineers%22&quot;&gt;Journal of the Taiwan Institute of Chemical Engineers&lt;/searchLink&gt;. Feb2026, Vol. 179, pN.PAG-N.PAG. 1p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Microemulsions%22&quot;&gt;Microemulsions&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Microfluidics%22&quot;&gt;Microfluidics&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Prediction+models%22&quot;&gt;Prediction models&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Machine+learning%22&quot;&gt;Machine learning&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Deep+learning%22&quot;&gt;Deep learning&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Fluid+dynamics%22&quot;&gt;Fluid dynamics&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Convolutional+neural+networks%22&quot;&gt;Convolutional neural networks&lt;/searchLink&gt;
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • RAM-CNN achieves 100 % fluid and 95.8 % droplet morphology recognition accuracy. • GAN-LGBMnet enhances small-sample learning with MAPE &lt;7 % for droplet prediction. • User-friendly GUI enables ≤2-click workflows for non-expert microfluidic design. • MO-DNN predicts single/double emulsion parameters with &lt;10 % error on unseen data. Constructing high-quality datasets for AI-driven microemulsion prediction remains challenging due to limitations in chip design, imaging hardware, and fluid dynamics. Conventional semi-empirical models suffer from poor accuracy and generalizability in complex nonlinear systems, while traditional microfluidic chips often yield non-spherical droplets. Optical constraints in capillary-based systems further hinder data acquisition. A deep learning framework integrates three components: (1) A multi-branch CNN with residual modules and self-attention (RAM-CNN) for robust droplet/fluid morphology recognition; (2) GAN-LGBMnet, combining adversarial networks and LightGBM, to augment small datasets and analyze key features; (3) A multi-output neural network (MO-DNN) predicting single/double-emulsion parameters. The system is deployed via an intuitive GUI (MICA) for non-specialist use. RAM-CNN achieves 100 % fluid-regime and 95.8 % droplet-morphology recognition accuracy, maintaining &gt;91 % performance under &#177;28 % brightness variations. Enhanced by GAN-LGBMnet, MO-DNN predicts droplet diameters and generation rates with MAPE &lt;7 % for both emulsion types. The MICA platform demonstrates &lt;10 % error on unseen data, enabling precise emulsion design. This work bridges theoretical models with practical microfluidic optimization through automated, user-friendly AI tools. [Display omitted] [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: &lt;i&gt;Copyright of Journal of the Taiwan Institute of Chemical Engineers is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190825523
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.jtice.2025.106414
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Microemulsions
        Type: general
      – SubjectFull: Microfluidics
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Fluid dynamics
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
    Titles:
      – TitleFull: Prediction of deep learning algorithms for the microemulsion generation conditions in complex environments.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Hao
      – PersonEntity:
          Name:
            NameFull: Ding, Tianshun
      – PersonEntity:
          Name:
            NameFull: Tao, Shengyang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 18761070
          Numbering:
            – Type: volume
              Value: 179
          Titles:
            – TitleFull: Journal of the Taiwan Institute of Chemical Engineers
              Type: main
ResultId 1