Modeling of Remove Cefixime by Fenton Method with Artificial Neural Network

The aim of this study was to model the removal efficiency of cefixime by the Fenton method using a neural network. This model predicts experimental results well. In this model, the amount of hydrogen peroxide, iron catalyst, cefixime removal time, initial concentration of cefixime, and pH are the input parameters. The output variable is the removal percentage of cefixime. Total error squares (SSE), mean the square root of error (RMSE), adjusted coefficient of determination (), and coefficient of determination   in determining the number of optimal neurons in the middle of the performance index. According to the obtained results, the neural network model was able to predict the absorption efficiency with the sigmoid tangent transfer function in the hidden layer and the linear stimulus transfer function in the output layer. Also, the results of modeling the neural network with org-art showed that the grid with a 1-13-5 arrangement (5 neurons in the input layer, 13 neurons in the hidden layer, and 1 neuron in the output layer) had the best result in predicting the output. The correlation coefficients of all the levels of training, validation, and test 0.3 were 0.99436, 0.9993, and 0.96901, respectively. To predict the trend of changes, neural network tools have been used in MATLAB software.