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Drying characteristics and numerical simulation of tissue paper

  • Yan Yan EMAIL logo , WeiChao Wang , Zhao Pan , Zhen Yang , JiaXi Li , ZiYu Yao and YunHeng Qi
Published/Copyright: April 8, 2025
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Abstract

This study aims to investigate the drying process of tissue paper in depth and to analyze it comprehensively by combining experimental and simulation methods. In the experimental part, a variety of drying conditions were designed, including different air temperatures, air velocities, and bottom heating temperatures, to accurately measure the change in moisture ratio of tissue paper. Meanwhile, a mathematical model for predicting the tissue drying moisture ratio was constructed based on the experimental data, and the tissue drying process was simulated. The simulation presented the process of moisture transfer within the tissue paper visually and quantitatively. The experimental and simulation results validate and complement each other, revealing the synergistic influence mechanism of drying parameters on the drying effect of tissue paper. The results showed that the effects of air temperature, bottom heating temperature, and air velocity on the drying rate of tissue paper were in the following order: bottom heating temperature > air temperature > air velocity, and among eight drying kinetic models, the Aghbashlo model was the most suitable for describing the drying characteristics of tissue paper in thin layers, and the accuracy of the prediction model was verified by simulation.


Corresponding author: Yan Yan, School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710048, China, E-mail:

Funding source: the Natural Science Basic Research Program of Shaanxi

Award Identifier / Grant number: ProgramNo. 2023-JC-QN-0154

Funding source: the program of China Scholarship Council (CSC)

Award Identifier / Grant number: (No.202208610115)

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: All other authors state no conflict of interest.

  6. Research funding: Support of the Natural Science Basic Research Program of Shaanxi (ProgramNo. 2023-JC-QN-0154) and the program of China Scholarship Council (CSC) (No.202208610115) is gratefully acknowledged.

  7. Data availability: Not applicable.

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Received: 2025-02-25
Accepted: 2025-03-21
Published Online: 2025-04-08
Published in Print: 2025-06-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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