C3H8 separation from CH4 and H2 using a synthesized PDMS membrane: Experimental and neural network modeling

A polydimethylsiloxane (PDMS) membrane was synthesized and permeation behavior of ternary gas mixtures including C3H8, CH4 and H2 through it was studied as a function of operating parameters. Mixed gas permeability values were also compared with pure gas data as well as literature to validate experimental results. The aim was to predict separation factor (SF) of C3H8 as a function of feed temperature, pressure, flow rate and C3H8 concentration with the aid of artificial neural network (ANN) technique. Multilayer perceptron (MLP), which is the most common type of feedforward neural network (FFNN), was used for prediction. The Levenberg–Marquardt training method was initially employed to train the net. Then, optimum numbers of hidden layers and nodes in each layer were determined. The selected structure (4:4:5:1) was finally used to predict SF of C3H8 for different inputs in the domain of training data. The modeling results showed that there is an excellent agreement between the experimental data and the predicted values, with mean absolute errors of less than 1%. Both modeling and experimental results confirmed that increasing feed temperature, feed pressure and C3H8 concentration in feed debilitates separation performance; however, SF increases with increasing feed flow rate. As a result, ANN can be recommended for the modeling of mixed gas transport through dense membranes such as PDMS.