Data-Driven Ionic Liquid Design for CO2 Capture: Molecular Structure Optimization and DFT Verification

To identify optimal ionic liquids (ILs) for CO2 capture, an efficient computer-aided IL design (CAILD) approach is desired. The traditional CAILD methods usually combine an equation of state with the UNIFAC-IL model to calculate gas solubility, which is computationally expensive and sometimes cannot give quantitatively satisfying results. In this contribution, a new CAILD approach is presented for the optimal design of ILs for CO2 capture, where mathematically simple and reliable data-driven models are applied to predict CO2 solubility. The IL design problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with the objective of maximizing the CO2 solubility of ILs under prespecified conditions. Global optimal solutions are successfully obtained due to model simplicity. Moreover, to prevent misleading results led by poor extrapolability of data-driven models, multiple data-driven models are trained from the same experimental solubility database. These models are then adopted in different batches of the MINLP formulation. For each batch, the optimization problem is solved to generate top IL candidates. Only the ILs that repeatedly appear in different batches are considered as reliable solutions falling into the validity domain of the data-driven models. Such a new strategy can effectively enhance design reliability. The CO2 capture performance of the designed ILs is finally confirmed using density functional theory calculations. The applicability of the proposed method is illustrated in a case study of post-combustion carbon capture.