Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Characterization of new cellulose sansevieria ehrenbergii fibers for polymer composites

Abstract: Natural cellulose fibers were newly identified from the sources of sansevieria ehrenbergii plant. These fibers were extracted using the mechanical decortication process. The hierarchical cell structure of the plant and fibers was analyzed using scanning electron microscope, optical microscope, Fourier transforms infrared, and X-ray diffraction. The density and diameter of the fibers were found to be approximately 0.887 g/cm3 and 10–250 μm, respectively. The various chemical compositions were analyzed and compared with other natural fibers. The thermal stability of the fiber was examined through thermogravimetric analysis/differential thermogravimetric analysis (DTG). The maximum peak temperature was obtained at 333.02 °C in DTG curve. The raw fibers exhibited a tensile strength of 50–585 MPa, an elongation at break of 2.8–21.7%, a Young’s modulus of 2.5–7.5 GPa, and a corrected compliances Young’s modulus of 2.5–7.8 GPa.

Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L.

Abstract: In this study, a comparative approach was made between artificial neural network (ANN) and response surface methodology (RSM) to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP) ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE), mean absolute error (MAE), standard error of prediction (SEP), model predictive error (MPE), chi square statistic (χ2), and coefficient of determination (R2) based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.