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International Journal of Polymer Science
Volume 2016, Article ID 5340252, 15 pages
Research Article

Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

1Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
2Materials Science and Technology Program, Qatar University, P.O. Box 2713, Doha, Qatar
3Department of Computer Science & Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
4Qatar Petrochemical Company (QAPCO), Doha, Qatar
5School of Mechanical & Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AH, UK
6School of Engineering, University of Ulster, Newtownabbey BT37 0QB, UK

Received 21 January 2016; Revised 19 April 2016; Accepted 21 April 2016

Academic Editor: De-Yi Wang

Copyright © 2016 P. Noorunnisa Khanam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.