Prediction of water retention capacity of hydrolysed electrospun polyacrylonitrile fibers using statistical model and artificial neural network

Abstract

Box Behnken design of experiment was used to study the effect of process variables such as alkali concentration, temperature and time on water retention capacity of the alkaline hydrolysed electrospun fibres. The hydrolysis of electrospun polyacrylonitrile fibres was carried out using sodium hydroxide with different processing conditions like concentration of alkali, temperature and time. With the increase in the concentration of alkali, time and temperature, the water retention capacity of membrane was found to increase in the membranes. Water retention capacities of the membranes were modeled and predicted using empirical as well as artificial neural network (ANN model). The fiber diameter and mean flow pore diameter of electrospun polyacrylonitrile fibers and hydrolyzed fibers shown in SEM images were 310 AE 50, 275 AE 75 nm, 0.9258 and 1.12 microns, respectively. The present study indicated that the nanofibrous membranes have potential for the water absorbing applications.

Optimizing parameters on alignment of PCL/PGA nanofibrous scaffold: An artificial neural networks approach

“…10.040 0141-8130/© 2014 Elsevier B.V. All rights reserved. predict the diameter [27][28][29] and the water preservation ability [30] of electrospun nanofibers.…”

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

“…The thermal conductivity of semi-crystalline polymer increases with crystallinity. PVP is an amorphous polymer and in amorphous polymer the mean free path is very small due to the phonon scattering caused by a number of defects in the amorphous structure, resulting in low values of a thermal conductivity [ 23 , 27 – 30 ].…”