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Seepage Time Soft Sensor Model of Nonwoven Fabric Based on the Extreme Learning Machine Integrating Monte Carlo
Jing Zhang1, Yiqiang Fan2, Lulu Zhang1
1School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Sensors (Basel, Switzerland)
|April 3, 2021
Summary
A new Monte Carlo-Extreme Learning Machine (MCELM) model accurately predicts liquid flow in nonwoven fabrics. This advanced model enhances the rapid, precise manufacturing of materials like masks for disease prevention.
Area of Science:
- Materials Science
- Chemical Engineering
- Computational Modeling
Background:
- Nonwoven fiber materials are crucial for masks preventing diseases like COVID-19.
- Modeling liquid penetration in complex nonwoven structures is challenging.
- Understanding liquid flow characteristics is vital for material performance.
Purpose of the Study:
- To develop a novel soft sensor model for predicting seepage time in nonwoven fabrics.
- To integrate Monte Carlo (MC) and Extreme Learning Machine (ELM) for enhanced prediction.
- To improve the understanding of liquid seepage in relation to structural properties.
Main Methods:
- Utilized the Monte Carlo (MC) method for data sample expansion.
- Employed the Extreme Learning Machine (ELM) to build a prediction model.
- Assessed dyeing time, insertion degree, and height based on material properties.
Main Results:
- The MCELM model demonstrated superior accuracy and prediction speed compared to BP and RBF networks.
- The model effectively links liquid seepage characteristics with the structural properties of porous media.
- Accurate prediction of dyeing time and liquid penetration was achieved.
Conclusions:
- The MCELM model offers a powerful tool for precise and rapid manufacturing of nonwoven materials.
- This approach provides valuable insights for predicting the behavior of nonwoven fabrics.
- The study advances the application of computational models in materials science and engineering.
