| Summary: | In this study, a three-layered feed-forward backpropagation (FFBPN) method in an artificial neural network (ANN) was employed to predict the adsorption performance for the removal of chromium (VI) from an aqueous solution. The two parameters used to develop the network using data from previous studies were temperature and contact time. The collected data was used to train the neural network to predict the desired output value of chromium removal. The accuracy of the simulated output value for the chromium removal was optimized by varying the number of neurons in the hidden layer. As a result, ANN successfully predicted the output values with accuracy of 99.97%. In addition, the developed model has followed the Langmuir isotherm with better fitting values. © 2024 Author(s).
|
|---|