scholarly journals Influence of material contamination on polypropylene melt filtration using assembled and fused screens

SPE Polymers ◽  
2021 ◽  
Author(s):  
Kerstin Koller ◽  
Christian Paulik ◽  
Christoph Burgstaller
Keyword(s):  
2019 ◽  
Vol 19 (4) ◽  
pp. 312-323
Author(s):  
Agnieszka Brochocka ◽  
Aleksandra Zagawa ◽  
Rafał Panek ◽  
Jarosław Madej ◽  
Wojciech Franus

Abstract In this work, a method for introducing zeolites and mesoporous siliceous materials into the melt-blown process for the production of polypropylene nonwovens was developed and the functional materials obtained were tested. Both types of additives were introduced in the melt-blown technology using a device placed in the duct of the die assembly. Nine types of polypropylene melt-blown nonwovens were made with different types of zeolites (clinoptilolite, Na-X, Na-A, Na-P1, sodalite, Na-P1 with hexadecyl trimethylammonium bromide (HDTMA), ZeoEco 20, and BioZeo R.01) or mesoporous silica material (Mobil Composition of Matter No. 41, abbreviated as MCM-41). The nonwovens were studied in terms of protective and functional parameters: sodium chloride and paraffin oil mist aerosol penetration, airflow resistance, and sorption capacity for toluene, ammonia, acetone, and cyclohexane, in accordance with the requirements of the European standards concerning respiratory protective equipment. The tests showed that zeolites and MCM-41 can be successfully incorporated within the structure of elementary polymer fibers using an environmentally friendly “dry” melt-blown technology with nonwovens modified so as to impart multiple functionalities in one integrated technological process. The developed method of introducing the studied materials to polypropylene melt-blown nonwovens led to the production of new multipurpose materials with good protective and functional properties. The best polypropylene nonwovens were produced with the addition of 250 g/m2 of MCM-41 or Na-P1 zeolite modified with HDTMA.


1979 ◽  
Vol 10 (3) ◽  
pp. 236-239 ◽  
Author(s):  
A. V. Genis ◽  
D. V. Fil'bert ◽  
A. A. Sindeev

2011 ◽  
Vol 187 ◽  
pp. 411-415
Author(s):  
Lu Yue Xia ◽  
Hai Tian Pan ◽  
Meng Fei Zhou ◽  
Yi Jun Cai ◽  
Xiao Fang Sun

Melt index is the most important parameter in determining the polypropylene grade. Since the lack of proper on-line instruments, its measurement interval and delay are both very long. This makes the quality control quite difficult. A modeling approach based on stacked neural networks is proposed to estimation the polypropylene melt index. Single neural network model generalization capability can be significantly improved by using stacked neural networks model. Proper determination of the stacking weights is essential for good stacked neural networks model performance, so determination of appropriate weights for combining individual networks using the criteria about minimization of sum of absolute prediction error is proposed. Application to real industrial data demonstrates that the polypropylene melt index can be successfully estimated using stacked neural networks. The results obtained demonstrate significant improvements in model accuracy, as a result of using stacked neural networks model, compared to using single neural network model.


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