The critical properties, the normal boiling temperature, and the acentric factor of 200 ionic liquids have been determined using an extended group contribution method, which is based on the well-known concepts of Lydersen and Joback and Reid, that was developed by the authors. The method does not require any additional data besides knowledge of the structure of the molecule and its molecular mass. Because experimental critical properties of ionic liquids are not available, the accuracy of the method is checked by calculating the liquid density of the ionic liquids considered in the study for which experimental data are available in the literature. The results show that the values determined for the critical properties, the normal boiling temperature, and the acentric factor are sufficiently accurate for engineering calculations, generalized correlations, and equation of state methods, among other applications.

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A hybrid technique that combines an artificial neural network with a particle swarm optimization (ANN+PSO) was used to forecast the disturbance storm time (Dst) index from 1 to 6 h ahead. Our ANN was optimized by PSO to update ANN weights and to predict the short‐term Dst index using past values as input parameters. The database used contains 233,760 hourly data from 1 January 1990 to 31 August 2016, considering storms and quiet period, grouped into three data sets: learning set (with 116,880 hourly data points), validation set (with 58,440 data points), and testing set (with 58,440 data points). Several ANN topologies were studied, and the best architecture was determined by systematically adding neurons and evaluating the root‐mean‐square error (RMSE) and the correlation coefficient (R) during the training process. These results show that the hybrid algorithm is a powerful technique for forecasting the Dst index a short time in advance like t + 1 to t + 3, with RMSE from 3.5 nT to 7.5 nT, and R from 0.98 to 0.90. However, t + 4 to t + 6 predictions become slightly more uncertain, with RMSE from 8.8 nT to 10.9 nT, and R from 0.86 to 0.79. Additionally, an exhaustive analysis according to geomagnetic storm magnitude was conducted. In general, the results show that our hybrid algorithm can be correctly trained to forecast the Dst index with appropriate precision and that Dst past behavior significantly affects adequate training and predicting capabilities of the implemented ANN.

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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; R 2 = 0.9934), for a wide range of temperatures and pressures (258 K to 393 K and 99 kPa to 206,940 kPa).

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