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

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.