Ephraim Igberase
Department of Chemical Engineering, Durban University of Technology, Steve Biko, Durban, South Africa.
Innocentia G. Mkhize
Department of Chemical Engineering, Durban University of Technology, Steve Biko, Durban, South Africa.
In this post, we present a brief overview of our recently published book chapter titled “Methylene Blue Adsorption Utilizing Enhanced Chitosan Beads: A Response Surface Methodology and Artificial Neural Network Study.”
This research applied the ANN technique with the help of the ANN Toolbox V4.0 in MATLAB 2019. Changing the network parameters altered the coding process conditions for Levenberg-Marquardt (LM). The LM could only make 10 data passes during data simulations. Also, no early stopping mechanism was used during the LM training. displays a well-established three-layer system with four neurons in the input layer, indicating contact time, solution pH, adsorbent dose, and concentration, which is used to model and forecast MB elimination. There are ten modes in the hidden layer and one neuron in the output layer. Shows how the network interacts with the training, testing, and validation data; correlation coefficients were found to be 0.95613, 1, 1, and 0.97017 for training, testing, validation, and overall data, respectively. The predicted results from the model correlate with the experimental data. The ANN model has great prediction ability, as shown by the overall correlation coefficient, and is suited for accurately predicting data. The results of the present ANN study align well with the findings.