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Numerical model of water removal and air penetration during vacuum dewatering

Drying technology, 2021, Vol.39 (10), p.1349-1358 [Peer Reviewed Journal]

2020 The Author(s). Published with license by Taylor & Francis Group, LLC 2020 ;ISSN: 0737-3937 ;ISSN: 1532-2300 ;EISSN: 1532-2300 ;DOI: 10.1080/07373937.2020.1745825

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  • Title:
    Numerical model of water removal and air penetration during vacuum dewatering
  • Author: Sjöstrand, Björn ; Nilsson, Lars ; Ullsten, Henrik ; Barbier, Christophe
  • Subjects: air penetration ; Chemical Engineering ; Kemiteknik ; numerical model ; papermaking ; Vacuum dewatering ; water removal
  • Is Part Of: Drying technology, 2021, Vol.39 (10), p.1349-1358
  • Description: Dewatering and air flow in high vacuum suction boxes was examined. The work was mainly numerical and was based on, and compared with, previously published experimental results of vacuum dewatering from laboratory equipment and from a pilot paper machine. A previously published numerical model for wet pressing is used as the basis for this work. The aims of this study were to find new fitting parameters that allows the previous model to be used for vacuum dewatering instead of pressing, and to examine two extensions to the original model. The results indicate that the new vacuum dewatering model for moisture can predict the dewatering behavior for several different experimental data series both from laboratory equipment and a pilot paper machine using the same set of fitting parameters. Two different numerical models for air flow through the paper sheet, during vacuum dewatering, were developed based on postulating that the decrease in moisture permeability is accompanied by a simultaneous increase in air permeability. The models for air flow can also be fitted to experimental data and predict the magnitudes of air flow during vacuum dewatering. The data sets for air flow exhibit a certain degree of operator dependence though, so that one set of fitting parameters is not enough for obtaining good agreement with all data sets.
  • Publisher: Taylor & Francis
  • Language: English
  • Identifier: ISSN: 0737-3937
    ISSN: 1532-2300
    EISSN: 1532-2300
    DOI: 10.1080/07373937.2020.1745825
  • Source: SWEPUB Freely available online
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