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Research on cross-screening mechanism of moist coal particles and intelligent prediction method of screening performance.

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Title: Research on cross-screening mechanism of moist coal particles and intelligent prediction method of screening performance.
Authors: Guo, Chenhao1 (AUTHOR), Zhao, Lala1,2 (AUTHOR) lala.zhao@cumt.edu.cn, Xu, Feng1 (AUTHOR), Duan, Chenlong2,3 (AUTHOR), Jiang, Haishen2,3 (AUTHOR), Yang, Yadong2,4 (AUTHOR), Liu, Zeping2,4 (AUTHOR)
Source: International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 5, p1193-1213. 21p.
Subject Terms: *Discrete element method, *Machine learning, *Process optimization, *Support vector machines, *Coal dust, *Particle swarm optimization
Abstract: In this work, the cross-screening mechanism of moist coal particles on the cross-screen was investigated based on DEM (discrete element method) and a validated linear cohesion model. The impact of various operating parameters on the screening performance of cross-screen were explored. Three machine learning models were established to forecast the screening performance of cross-screen. The results show that significant improvements in screening efficiency η can be achieved by reducing the feeding rate q and cohesion energy density k, or increasing the rotational speed of roller shafts n and the inclination angles of the screen surface θ. The fluctuation of the average particle velocity v is negligible at different q. The PSO -SVM (particle swarm optimization and support vector machine) prediction model emerged the best predictive performance for screening performance with superior data fitting. This work provides crucial theoretical understandings for optimization and intelligent design of cross-screen. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Research on cross-screening mechanism of moist coal particles and intelligent prediction method of screening performance.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Chenhao%22">Guo, Chenhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Lala%22">Zhao, Lala</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lala.zhao@cumt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Feng%22">Xu, Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Duan%2C+Chenlong%22">Duan, Chenlong</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Haishen%22">Jiang, Haishen</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yadong%22">Yang, Yadong</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Zeping%22">Liu, Zeping</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Coal+Preparation+%26+Utilization%22">International Journal of Coal Preparation & Utilization</searchLink>. 2026, Vol. 46 Issue 5, p1193-1213. 21p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Discrete+element+method%22">Discrete element method</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br />*<searchLink fieldCode="DE" term="%22Coal+dust%22">Coal dust</searchLink><br />*<searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this work, the cross-screening mechanism of moist coal particles on the cross-screen was investigated based on DEM (discrete element method) and a validated linear cohesion model. The impact of various operating parameters on the screening performance of cross-screen were explored. Three machine learning models were established to forecast the screening performance of cross-screen. The results show that significant improvements in screening efficiency η can be achieved by reducing the feeding rate q and cohesion energy density k, or increasing the rotational speed of roller shafts n and the inclination angles of the screen surface θ. The fluctuation of the average particle velocity v is negligible at different q. The PSO -SVM (particle swarm optimization and support vector machine) prediction model emerged the best predictive performance for screening performance with superior data fitting. This work provides crucial theoretical understandings for optimization and intelligent design of cross-screen. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/19392699.2025.2492720
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 1193
    Subjects:
      – SubjectFull: Discrete element method
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Process optimization
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Coal dust
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
    Titles:
      – TitleFull: Research on cross-screening mechanism of moist coal particles and intelligent prediction method of screening performance.
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            NameFull: Guo, Chenhao
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            NameFull: Zhao, Lala
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            NameFull: Xu, Feng
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            NameFull: Duan, Chenlong
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            NameFull: Jiang, Haishen
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            NameFull: Yang, Yadong
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            NameFull: Liu, Zeping
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            – D: 01
              M: 05
              Text: 2026
              Type: published
              Y: 2026
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              Value: 46
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            – TitleFull: International Journal of Coal Preparation & Utilization
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