Effect of Operating Conditions on PV Performance of PVA Membranes: Experimental and Neural Network Modeling

Abstract

Poly (vinyl alcohol) membranes were prepared by in-situ crosslinking of poly(vinyl alcohol) with glutaraldehyde as crosslinking agent and hydrochloric acid as catalyst and used for dehydration of IPA mixtures. Effects of feed composition, operating temperature, vacuum pressure, and Reynolds number on the permeation performance of the membranes were evaluated. Eighty-nine experimental data was applied to investigate ANN modeling. A multi layered feedforward neural network was applied to model the PV membranes. Two major training algorithms and optimum number of neurons and layers were investigated. As a result, Bayesian regularization successfully predicted experimental data. Different network structures were optimized, using multi object genetic optimization algorithm. The results concluded that the network with structure composing two hidden layers performs better than the other with one hidden layer, and also there is an excellent compatibility between the experimental data and the predicted values of optimum network structure (4:3:2:2). Furthermore, the optimum network was applied to predict extrapolation data and the results showed that this network can extrapolate data as well as interpolating.

Keywords:

• ANN modeling
• feedforward neural network
• genetic optimization algorithm
• pervaporation
• PVA membrane

ACKNOWLEDGEMENTS

This study was partially supported by Iran National Science Foundation (INSF).

Notes

BR: Bayesian Regularization.

LM: Levenberg-Marquardt.

RMSE: Root mean squared error.

R² ₁: Squared correlation coefficients of output 1 (selectivity).

R² ₂: Squared correlation coefficients of output 2 (flux).