Prediction of thermal conductivity of polyvinylpyrrolidone (PVP) electrospun nanocomposite fibers using artificial neural network and prey-predator algorithm

• Published in: PloS one 2017-09, Vol.12 (9), p.e0183920-e0183920
• Main Authors: Khan, Waseem S, Hamadneh, Nawaf N, Khan, Waqar A
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Biology and Life Sciences; Carbon; Chemical engineering; Computer and Information Sciences; Conductivity; Correlation analysis; Correlation coefficient; Correlation coefficients; Data processing; Dielectric properties; Ecology and Environmental Sciences; Electricity; Electrospinning; Engineering and Technology; Engineering schools; Error functions; Ferric Compounds - chemistry; Fibers; Filler materials; Heat conductivity; Heat transfer; Industrial engineering; Logic programming; Materials science; Mathematical models; Multi wall carbon nanotubes; Nanocomposites; Nanocomposites - chemistry; Nanoparticles; Nanotechnology; Nanotubes; Nanotubes, Carbon - chemistry; Neural networks; Neural Networks (Computer); Nickel; Nickel - chemistry; Optimization techniques; Physical Sciences; Polymers; Polymethyl methacrylate; Polyvinylpyrrolidone; Povidone - chemistry; Predictions; Prey; Research and Analysis Methods; Thermal Conductivity; Tourism; Zinc; Zinc - chemistry; Zinc ferrites
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0183920

In this study, multilayer perception neural network (MLPNN) was employed to predict thermal conductivity of PVP electrospun nanocomposite fibers with multiwalled carbon nanotubes (MWCNTs) and Nickel Zinc ferrites [(Ni0.6Zn0.4) Fe2O4]. This is the second attempt on the application of MLPNN with prey predator algorithm for the prediction of thermal conductivity of PVP electrospun nanocomposite fibers. The prey predator algorithm was used to train the neural networks to find the best models. The best models have the minimal of sum squared error between the experimental testing data and the corresponding models results. The minimal error was found to be 0.0028 for MWCNTs model and 0.00199 for Ni-Zn ferrites model. The predicted artificial neural networks (ANNs) responses were analyzed statistically using z-test, correlation coefficient, and the error functions for both inclusions. The predicted ANN responses for PVP electrospun nanocomposite fibers were compared with the experimental data and were found in good agreement.