Performance control study of interleaved meltblown non-woven materials based on statistical analysis and predictive...

Area of Science:

• Materials Science
• Chemical Engineering
• Nonwoven Technology

Background:

• Meltblown nonwoven materials are crucial for filtration applications.
• Optimizing their preparation process, particularly intercalation, is a key research area in chemical production.
• Understanding the relationship between material structure and performance is essential.

Purpose of the Study:

• To establish a product performance prediction model for intercalated meltblown materials.
• To investigate the impact of intercalation on structural variables and product performance.
• To determine the maximum filtration efficiency using a BP neural network model.

Main Methods:

• Developed a prediction model based on data from intercalated and unintercalated meltblown materials.
• Analyzed structural variables (thickness, porosity, compressive resilience) and their relationship with performance metrics (filtration resistance, efficiency, air permeability).
• Employed multiple regression analysis and a BP neural network for performance prediction and optimization.

Main Results:

• Established a predictive model for intercalated meltblown material performance under varying process parameters.
• Quantified the influence of intercalation on material structure and filtration characteristics.
• Successfully utilized a BP neural network to identify conditions for maximum filtration efficiency.

Conclusions:

• The study provides a theoretical framework for controlling meltblown nonwoven material performance.
• The developed BP neural network model effectively predicts and optimizes filtration efficiency.
• This research supports advancements in the chemical production of high-performance filtration materials.