Neural network model and linear multiple regression method analysis pressure drop in air filtration properties of the melt blowing nonwoven fabrics

2010 ◽  
Author(s):  
Zhao Bo
Keyword(s):  
Neural Network ◽  
Pressure Drop ◽  
Neural Network Model ◽  
Regression Method ◽  
Air Filtration ◽  
Melt Blowing ◽  
Nonwoven Fabrics ◽  
Multiple Regression Method ◽  
Filtration Properties ◽  
Method Analysis

Simulating the Filtration Properties of Nonwoven Fabrics: Comparison of Artificial Neural Network, Statistical and Grey Models

2010 ◽  
Vol 20-23 ◽  
pp. 1021-1027 ◽  
Author(s):  
Ting Chen ◽  
Shang Zhen Zhao ◽  
Li Li Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Processing Parameters ◽  
Computer Assisted ◽  
Ann Model ◽  
Nonwoven Fabrics ◽  
Grey Models ◽  
Artificial Neural ◽  
Few Data ◽  
Filtration Properties

The artificial neural network, statistical and grey models are established for predicting the filtration properties of melt blown nonwoven fabrics from the processing parameters. The results show that the ANN model yields very accurate predictions and a reasonably good ANN model can be achieved with relatively few data points. The statistical model gives satisfactory prediction results for most cases, and the grey model needs to be improved for precise predictions. The results show great perspective of this research in the field of computer assisted design of melt blowing nonwoven technology.

Branched nanofibers with tiny diameters for air filtration via one-step electrospinning

2020 ◽  
pp. 152808372092377
Author(s):  
Bilal Zaarour ◽  
Hussen Tina ◽  
Lei Zhu ◽  
XiangYu Jin
Keyword(s):  
Sodium Chloride ◽  
Pressure Drop ◽  
Polyvinylidene Fluoride ◽  
Filtration Efficiency ◽  
Air Filtration ◽  
Air Filter ◽  
One Step ◽  
Tetrabutylammonium Chloride ◽  
Filtration Properties ◽  
Branched Structure

Engineering the surface morphology of fibers has been attracting significant consideration in various areas and applications. In this study, polyvinylidene fluoride (PVDF) branched nanofibers with a diameter of less than 50 nm are electrospun directly at a low relative humidity by adding tetrabutylammonium chloride. The effects of the branched structure on the specific surface area and pore size distribution are investigated, and the filtration properties of the air filter based on branched nanofiber webs with different basis weights are studied. The results exhibit that the air filter based on PVDF branched nanofibers with the basis weight of 1 g/m2 has an outstanding filtration efficiency (99.999%) to 0.26 µm sodium chloride particles under the pressure drop of 126.17 Pa. We believe that this study can be used as a useful reference for the preparation of branched nanofibers through one-step electrospinning.

Prediction of Fiber Diameter of Melt Blown Nonwovens Produced by Sharp Die Using Neural Network Theory

2011 ◽  
Vol 101-102 ◽  
pp. 543-546
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Fiber Diameter ◽  
Polymer Melt ◽  
Processing Parameters ◽  
Ann Model ◽  
Melt Blowing ◽  
Melt Temperature ◽  
Nonwoven Fabrics ◽  
Air Jet ◽  
Neural Network Theory

Melt blowing is known for directly converting polymer resin into nonwoven fabrics of microfibers. The fiber diameter of melt blown nonwoven fabrics is strongly influenced by the air jet flow field developed from the sharp die. The dual slot sharp die is often used to yield polymer fibers in this process. The objective of this paper is to investigate the fiber diameter of melt blowing nonwovens produced by sharp die using an artificial neural network model. The fiber diameter of melt blowing nonwoven is mostly influenced by processing parameters (polymer flow rate, polymer melt temperature, initial air velocity, die-to-collector distance and initial air temperature). By analyzing the results obtained with the aid of the ANN model, the effects of melt blowing process parameters on the fiber diameter can be predicted. The results demonstrate that ANN model is a effective and a excellent method for predictors.

HETP and Pressure Drop Prediction for Structured Packing Distillation Columns Using a Neural Network Model

1999 ◽  
Vol 38 (4) ◽  
pp. 1736-1739 ◽  
Author(s):  
Amanda K. Whaley ◽  
Christopher A. Bode ◽  
Joydeep Ghosh ◽  
R. Bruce Eldridge
Keyword(s):  
Neural Network ◽  
Pressure Drop ◽  
Network Model ◽  
Neural Network Model ◽  
Structured Packing ◽  
Distillation Columns
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