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Carbon capture and storage (CCS) has gained great interest in recent years as a potential technology to mitigate industrial carbon dioxide (CO 2 ) emissions. Ionic liquids (ILs) were identified as potential CO 2 capturing solvents, due to their negligible vapour pressure, high thermal stability, and wide range of thermophysical properties. However, determining a task-specific IL merely through experimental studies is tedious and costly, as there are about a million possible combinations of cations and anions that may make up the ILs. This work presents a systematic approach to design an optimal IL for the purpose of carbon capture. The significant contribution of the presented approach in this work is the introduction of disjunctive programming to identify optimal operating conditions of the process involved while solving the IL synthesis problem. As studies show, the performance of ILs changes with the operating conditions, which in turn affects overall performance of the carbon capture process. Hence, the presented approach will determine the optimal IL by considering the effect of system operating conditions, and simultaneously determining optimal conditions of the carbon capture process. Operating conditions of the process are modelled as continuous variables; disjunctive programming can discretise these variables and reduce search space for results. Since most of the ILs to be designed are novel solvents, their thermophysical properties are estimated using the group contribution (GC) method. Appropriate structural constraints are defined to ensure the structure of the synthesised IL is feasible. An illustrative case study is solved to demonstrate the proposed approach.

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