Robust artificial neural network (ANN) was developed based on experimental data to predict dehydration of isopropanol by means of novel PVA–APTEOS/TEOS nanocomposite membranes in pervaporation (PV) process. The input properties were water concentration, feed temperature and nanoparticles content, while pervaporation separation index (PSI) was output. The Bayesian Regularization (BR) training method with full sampling was employed to train the network. Then, optimal ANN architecture was determined as 3:3:3:1 with log-sigmoid transfer function for hidden and output layers. The model finding revealed that nanoparticles content has significant effect on membrane performance (about 70%). The results demonstrated that the ANN model prediction and experimental data are quite match and the model can be employed with confidence for prediction of each nanocomposite membrane performance. Simulated annealing (SA) was also employed to determine controllable conditions to find the biggest PSI.
Simulation and determination of optimum conditions of pervaporative dehydration of isopropanol process using synthesized PVA–APTEOS/TEOS nanocomposite membranes by means of expert systems
Ghafarinazari, Ali
2011-01-01
Abstract
Robust artificial neural network (ANN) was developed based on experimental data to predict dehydration of isopropanol by means of novel PVA–APTEOS/TEOS nanocomposite membranes in pervaporation (PV) process. The input properties were water concentration, feed temperature and nanoparticles content, while pervaporation separation index (PSI) was output. The Bayesian Regularization (BR) training method with full sampling was employed to train the network. Then, optimal ANN architecture was determined as 3:3:3:1 with log-sigmoid transfer function for hidden and output layers. The model finding revealed that nanoparticles content has significant effect on membrane performance (about 70%). The results demonstrated that the ANN model prediction and experimental data are quite match and the model can be employed with confidence for prediction of each nanocomposite membrane performance. Simulated annealing (SA) was also employed to determine controllable conditions to find the biggest PSI.
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