Estimation of Density as a Function of Temperature and Pressure for Imidazolium-Based Ionic Liquids Using a Multilayer Net with Particle Swarm Optimization

The liquid density of imidazolium-based ionic liquids has been estimated using a combined method that includes an artificial neural network and a simple group contribution method. A total of 1736 data points of density at several temperatures and pressures, corresponding to 131 ionic liquids, have been used to train the neural network developed with particle swarm optimization. To discriminate among the different substances, the molar mass and the structure of the molecule were given as input variables. Then, new values of density as a function of temperature and pressure for 33 other ionic liquids (426 data points) have been predicted and the results compared to experimental data from the literature. The results show that the chosen artificial neural network with particle swarm optimization and the group contribution method represent an excellent alternative for the estimation of the liquid density of imidazolium-based ionic liquids with acceptable accuracy (AARD=0.44; R2 = 0.9934), for a wide range of temperatures and pressures (258 K to 393K and 99kPa to 206,940kPa).

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