mahnaz yasemi

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.

In this paper, solubility of oxygen in organic solvents has been estimated using Artificial Neural Networks (ANN). Solubility data were studied for wide ranges of temperature (298.2-348.29 K) and pressure (0.0535 to 9.2338 MPa). Solvents are included of methanol, n-propanol, octane, toluene, dibutyl ether and 2-methyltetrahydrofuran. Network model consists of four inputs in input layer for acentric factor, molecular weight, TR and PR of the system and one neuron in output layer corresponding to solubility of oxygen. The best structure for feed-forward back propagation neural network is logarithmic sigmoid transfer function for hidden layer, 13 neurons in this layer and linear transfer function for output layer. Results show that optimum neural network architecture is able to predict the solubility of oxygen in organic solvents with an acceptable level of accuracy, R2 of 0.999997, ARD % of 0.8103 and AAD% of 0.0042. Sensitivity analysis shows that TR has the greatest effect on the solubility of oxygen.