Paper wet strength at the press felt seam
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Omid Mohammadi
,
Boris Stoeber
and
Sheldon Green
Abstract
Paper machine press felts often contain seams that facilitate their manufacturing and replacement. However, in industrial practice, breaks in the paper sheet commonly initiate at these seam locations. This study examines how press felt seams affect the local wet strength of high grammage papers. A custom experimental setup was developed to dewater paper using either tensioned seamed felts or two uniform felts separated by a small gap (“gap seam”). The pressed paper was then subjected to tensile testing. The pressure distribution near real press felt seams were mapped with pressure-sensitive film. From this, an “effective gap” was defined as the region with reduced pressure. This effective gap increases linearly with felt tension. The wet strength of paper produced using a seamed felt with a given effective gap matched that of paper pressed with a gap seam of the same size. The tests were conducted under industrially relevant conditions for a cardstock-grade paper, including press felt tension, pressure, furnish, and moisture content. Results showed that paper wet strength near the seam decreases exponentially with increasing seam size. The characteristic length scale of this decay closely matches the average fiber length in the furnish.
Funding source: Natural Sciences and Engineering Research Council of Canada (NSERC)
Funding source: AstenJohnson Inc.
Acknowledgments
The authors express their gratitude to AstenJohnson Inc. and NSERC, who provided financial support for this research. We specifically acknowledge the help of Allan Manninen and Daniel Hedou of AstenJohnson Inc., who provided valuable advice about industry norms, and who provided press felt samples.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. OM performed all experiments and drafted the initial version of the manuscript. Dr. SG and Dr. BS provided research supervision and refined the manuscript for clarity and conciseness.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This project received support from AstenJohnson Inc. and the Natural Sciences and Engineering Research Council of Canada (NSERC). Additional funding was provided through the Canada Research Chair program.
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Data availability: Not applicable.
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