Separation of toluene/n-heptane mixtures experimental, modeling and optimization

In this paper a composite membrane is used to separate toluene from n-heptane mixture. The aim is to optimize the separation process conditions through modeling. Therefore this model should be able to predict membrane performance demonstrated by total permeation flux and toluene selectivity as a function of operating condition. In order to create a black box model of the process, a multi layer feed forward artificial neural network is used. An algorithm based on evaluating all possible structures is employed to create an optimum ANN model. Number of hidden layers, transfer function, training method and hidden neurons are determined with the aid of this algorithm.Performance confirms that there is good agreement between the experimental data and the model predicted values, with correlation coefficients of more than 0.99 and mean square errors of less than 1%. Both model and experimental data show that increasing temperature and toluene concentration increase total flux and decrease toluene selectivity but increasing permeate pressure decreases both.Having created and trained an optimized ANN model a multi-objective genetic algorithm is employed to find optimum operating conditions with respect to permeation flux and toluene selectivity as two targets of this separation. Considering the obtained Pareto set and corresponding decision variables, it is found that permeate pressure in this set is almost constant and only variations in temperature and feed concentration eventuate to the creation of the Pareto front.

► We model permeation behavior of n-heptane and toluene using a neural network model.
► The model result showed that ANN model can be used efficiently for this modeling.
► Multi-objective genetic algorithm is employed to find the Pareto optimal set.
► Temperature and feed concentration are contributors in creation of Pareto set.