Use of Polymer Membranes for Modeling Desulfurization in the Process of Pervaporation through Artificial Neural Network

The present study considered the amount of thiophene alkane separation within the process of pervaporation by use of of membrane polyethylene glycol and polydimethyl siloxane-polyacrylonitrile with the help of Artificial Neural Network modeling. In this research, the effects of such parameters as Volumetric flow rate and temperature, as well as feedstuff properties (separation factor and flux) on the Desulfurization process efficiency were evaluated, and the Multi Layers Perceptron (MLP) neural network feed forward along with Propagation learning algorithm and Levenberg-Marquardt function with inputs and outputs were implemented. Tansig activation algorithm was used for the hidden layer, and Purelin algorithm was utilized for the output layer. Furthermore, 5 neurons were defined for the hidden layer. After processing the data, 70 percent were allocated for learning, 15% were allocated for validity, and the remaining 15% were allocated for the experience. The achieved results with the aforementioned method had a suitable accuracy. The graphs of the error percentage for the actual values of the separation factor and flux outputs were compared to the achieved values from modeling through related membranes for evaluating the efficiency of pervaporation process in separation of ethanol, Acetone, and butanol from water. Finally, the graphs were drawn.