Modeling Dehydration Of Organic Compounds By Means Of Polymer Membranes With Help Of Artificial Neural Network And COMSOL Multiphysics

The present study analyzes the amount of water-alcohol separation by pervaporation and use of polymer membranes with help of Artificial Neural Network and COMSOL Multiphysics. The influence of such parameters as volumetric flow rate temperature separation factor and permeate flux over the efficiency of dehydration process was analyzed through Artificial Neural Network. The reserarcher in this study used a Feed Forward multilayer Perceptron neural network with a back propogation algorithm and Levenberg-Marquardt function with two inputs and two outputs. The Tansig transfer function was used for the hudden layer and Purelin was used for the output layer five nerons were defined for the hidden layer. After data precessing 70 percent of the data was allocated for learning 15 percent was allocated for validation and 25 percent was allocated for testing. The output values of Artificial Neural Network modelling were compard with the real values of pervaporation for separation of water from Ethanol Acetone and butanol. The results revealed that the proposed model had a good performance. Moreover the output of COMSOL software for pervaporation of five different alcohols were compared with the real values and the error percentage of the actual amount of flux was calculated with the modeling value by means of related membranes. The results of COMSOL modeling showed that the error percentages of 3.049 3.7 3.51 2.88 and 3.82 were respectively achieved for dehydration process of Acetone Butanol Ethanol Isopropanol and Methanol.