In this study, plastic wastes named (PET and PVC) were used to prepare polymer matrix composite (PMC) which can be used in different applications. Composite materials were prepared by mixing unsaturated polyester resin (UP) with plastic wastes, two types of plastic waste were used in this work included polyethylene-terephthalate (PET) and Polyvinyl chloride (PVC) with various weight fractions (0, 5,10,15, 20 and 25%) added as a filler in flakes form. In this work, some of the tests that were carried out included (tensile, bending, and compressive strength) as mechanical tests, in addition to (thermal conductivity and water absorption) as physical tests. The values of tensile, compressive strength and Young's modulus of UP increased after the reinforcement with PVC or PET. Thermal insulation of the composites is significantly higher than that of pure UP. Although the values of water absorption for composite are higher than pure UP due to the existence of the interface between matrix and filler material. Also, the low cost of plastic wastes recycling encourage preserving the environment.
EFFECT OF PLASTIC WASTES AS LOW COST MATERIAL ON SOME PROPERTIES OF UNSATURATED POLYESTER.: EFFECT OF PLASTIC WASTES AS LOW COST MATERIAL ON SOME PROPERTIES OF UNSATURATED POLYESTER.
Plastic Waste
polymer matrix composite (PMCs)
Polyethylene-terephthalate (PET)
Polyvinyl chloride (PVC).
Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Several Nonlinear Estimators for Regression Function
: دوال الانحدار
المقدر الخطي الموضعي
المقدر شبه المعلمي المدمج
مقدر الشبكة العصبية الاصطناعية
الشبكات العصبية ذات الانتشار العكسي للخطأ
regression function
Local Linear estimator
Combined semi-parametric estimator
Artificial neural network estimator (ANN)
Back propagation Neural Nets
The aim of this paper is to estimate a nonlinear regression function of the Export of the crude oil Saudi (in Million Barrels) as a function of the number of discovered fields.
Through studying the behavior of the data we show that its behavior was not followed a linear pattern or can put it in a known form so far there was no possibility to see a general trend resulting from such exports.
We use different nonlinear estimators to estimate a regression function, Local linear estimator, Semi-parametric as well as an artificial neural network estimator (ANN).
The results proved that the (ANN) estimator is the best nonlinear estimator am
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