MATEC Web Conf.
Volume 347, 202112th South African Conference on Computational and Applied Mechanics (SACAM2020)
|Number of page(s)||12|
|Published online||23 November 2021|
Neural Network for Modeling the Mechanical Properties of Gelatin-Cellulose Nanocrystals Hydrogel Membrane for Heavy Metal ions Removal from Wastewater
Department of Chemical and Metallurgical Engineering, Vaal University of Technology, Private Bag X021, South Africa
* Corresponding author: firstname.lastname@example.org
The mechanical properties of Gelatin-cellulose nanocrystals hydrogel membrane were investigated for the removal of heavy metal ions from wastewater. The membrane was characterized using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) analysis. Neural Network (NN) model was developed to predict the mechanical properties such as Young’s modulus, tensile strength, and elongation. The NN predicted results are very close to the experimental results with R2 = 0.99315. The predicted values were found to be in excellent agreement with the experimental data and the current model has a good learning precision and generalization. The results revealed that the developed model is very accurate.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.