We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于 BP 神经网络的聚偏氟乙烯/聚丙烯 梯度复合滤料工艺优化.
- Authors
康乐; 王立志; 高晓平
- Abstract
Mask was an important epidemic prevention barrier to prevent virus from entering human body through respiratory system and mucous membrane. The disposable mask had some problems, such as rapid decline of filtration efficiency with electrostatic attenuation, large respiratory resistance, short service life and so on. Electrospun nanofiber membrane was compounded with melt blown cloth to reduce the dependence of particle filtration on static electricity and realize long-term filtration. Polyvinylidene fluoride (PVDF) nanofiber membrane was prepared by electrospinning with N, N-dimethylformamide (DMF) as solvent. Then it was coated with polypropylene (PP) melt blown base cloth to prepare PVDF/PP nano/micron structure composite fiber membrane. The effect of electrospinning process parameters on the aerosol filtration performance of composite fiber membrane was experimentally studied. The ternary quadratic polynomial model was established to optimize the spinning process and predict the fiber membrane resistance. At the same time, the back propagation (BP) neural network model was constructed to predict the fiber membrane resistance. The results show that the effects of voltage, receiving distance, injection speed, spinning solution concentration and fiber membrane surface density on the filtration efficiency and filtration resistance are consistent. When the concentration of spinning solution is 15wt% and the area density is 3 g/m2, the optimized spinning process parameters are voltage of 30 kV, receiving distance of 16.8 cm and injection speed of 1.6 mL/h. The filtration resistance predicted by polynomial model is 76.79 Pa, the relative error is 9.23%, and the error coefficient of variation (CV) value is 59%. The filtration resistance predicted by BP neural network is 81.25 Pa, the relative error is 1.99%, and the error CV value is 48%. The experiments show that the ternary quadratic model and BP neural network have high prediction accuracy.
- Subjects
ARTIFICIAL neural networks; STATIC electricity; BACK propagation; COMPOSITE structures; POLYVINYLIDENE fluoride; RESPIRATORY organs; FILTERS &; filtration
- Publication
Acta Materiae Compositae Sinica, 2022, Vol 39, Issue 8, p3783
- ISSN
1000-3851
- Publication type
Article
- DOI
10.13801/j.cnki.fhclxb.20210913.005