Integrated Multilevel Screening of Ionic Liquids and Parallel Computing toward the Optimal Extractive Distillation Process for 4N-Grade Isopropanol Separation

Abstract The separation of 4N-grade isopropanol (IPA) from the mixture obtained from direct propylene hydration is of considerable importance, particularly for its applications in the semiconductor industry. In this work, we propose an integrated method combining multilevel screening of ionic liquids (ILs) with parallel computing toward the optimal design of a 4N-grade IPA separation process. Thermodynamic screening is performed to select several ILs with good separation abilities, followed by further screening for feasible ILs based on their thermal stability. Subsequently, a Python-based parallel optimization algorithm is proposed for the accelerated optimization of flash temperature and total annual cost (TAC). The stability and efficiency of parallel computing are systematically analyzed. A comparative study is carried out against the conventional ethylene glycol (EG)-based extractive distillation process. The results indicate that although [EMIM][Ac] and [EMIM][EtSO4] can also break the azeotropes of IPA/DIPE and IPA/water, they undergo thermal decomposition during the recovery process. Therefore, the only effective candidate ILs remaining are [EMIM][DEP], [EMIM][DMP], and [EMIM][SCN], with the [EMIM][DEP]-based extractive distillation process demonstrating the best performance, as its TAC is reduced by 31.19% relative to the EG-based process. The proposed parallel optimization algorithm significantly reduces computing time while ensuring the stability of optimization results, achieving a relative deviation in TAC of less than 0.8% and an 85.9% reduction in computing time.