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Challenges in computational materials modelling and simulation: A case-study to predict tissue paper properties.

Flávia P Morais1, Joana M R Curto1,2

  • 1Fiber Materials and Environmental Technologies (FibEnTech-UBI), Universidade da Beira Interior, R. Marquês de D'Ávila e Bolama, 6201-001, Covilhã, Portugal.

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Summary

This study models how fiber characteristics influence tissue paper properties like softness and strength. Computational modeling links fiber pulp parameters to final tissue performance, enabling innovative product design.

Keywords:
3D fiber-based simulatorArtificial neural networkMultiple linear regressionTissue functional propertiesTissue paper materials

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Area of Science:

  • Pulp and Paper Science
  • Materials Science
  • Computational Modeling

Background:

  • Growing global demand for tissue paper necessitates optimizing raw material management and enhancing product performance.
  • Key tissue properties—softness, strength, and absorption—are influenced by fiber morphology, processing, and structure.
  • Establishing predictive models for these properties presents a complex, multifactorial challenge.

Purpose of the Study:

  • To investigate the trade-offs between input variables (fiber morphology, suspension, structure) and final tissue properties.
  • To explore relationships between raw material characteristics and tissue paper performance.
  • To leverage computational modeling for designing advanced tissue products.

Main Methods:

  • Utilized data-driven modeling approaches: multiple linear regression (MLR) and artificial neural networks (ANN).
  • Developed MLR and ANN models using data from experimental designs and laboratory-prepared structures.
  • Employed a 3D fiber-based simulator to analyze fiber influence on structural properties.

Main Results:

  • Realistic predictions were achieved, successfully linking fiber and tissue structure characteristics.
  • Demonstrated the ability to model the impact of fiber pulp parameters on end-use tissue properties.
  • Identified key variables influencing tissue softness, strength, and absorption.

Conclusions:

  • Computational modeling offers a viable approach to predict tissue paper properties based on fiber characteristics.
  • This methodology facilitates the design of innovative tissue products with tailored performance.
  • The study provides a framework for optimizing furnish management and enhancing tissue quality.