Ionic liquids (ILs) have attracted considerable attention in both the academic and industrial communities for absorbing and separating gases. However, a data‐rich and well‐structured systematic database has not yet been established, and screening for highly efficient ILs meeting various requirements remains a challenging task. In this study, an extensive database of estimated Henry's law constants of twelve gases in more than ten thousand ILs at 313.15 K is established using the COSMO‐RS method. Based on the database, a new systematic and efficient screening method for IL selection for the absorption and separation of gases subject to important target properties is proposed. Application of the database and the screening method is highlighted through case studies involving two important gases separation problems (CO2 from CH4 and C2H2 from C2H4). The results demonstrate the effectiveness of using the screening method together with the database to explore and screen novel ILs meeting specific requirements for the absorption and separation of gases. © 2017 American Institute of Chemical Engineers AIChE J, 63: 1353–1367, 2017

Predicting the viscosity of ionic liquids (ILs) is crucial for their applications in chemical and related industries. In this study, a large data set of experimental viscosity data of ILs with a wide range of viscosity (7.83–142 000 cP), pressure (1–3000 bar), and temperature (258.15–395.32 K) are employed to build predictive models. The structures of cations and anions for 89 ILs are optimized, and the S σ‑profiles descriptors are calculated using the quantum chemistry method.Two new models are developed by using extreme learning machine (ELM) intelligence algorithm with the temperature, pressure, and a number of S σ‑profiles descriptors as input parameters. The coefficient of determination (R 2) and average absolute relative deviation (AARD %) of the total sets of the two predictive models are 0.982, 2.21% and 0.951, 4.10%, respectively. The results show that the two ELM models are reliable for predicting the viscosity of ILs.

In order to estimate the heat capacity of ionic liquids (ILs), statistical models have been proposed using the quantum-chemical based charge distribution area (S σ‑profile) as the molecular descriptors for two different mathematical algorithms: multiple linear regression (MLR) and extreme learning machine (ELM). A total of 2416 experimental data points, belonging to 46 ILs over a wide temperature range (223.1–663 K) at atmospheric pressure, have been utilized to carry out validation. The average absolute relative deviation (AARD %) of the whole data set of the MLR and ELM is 2.72% and 0.60%, respectively. Although both algorithms are able to estimate the heat capacity of ILs well, the nonlinear model (ELM) shows more accuracy, due to its capacity of determining a complex nonlinear relationship. Moreover, the derived models can shed some light onto structural features that are related to the heat capacity and be a suitable option to decrease trial-and-error experiments.

Predicting hydrogen sulfide (H2S) solubility in ionic liquids (ILs) is vital for industrial gas desulphurization.