A heuristic predictive model for screening green entrainers comparing life cycle assessment indexes and economics

We proposed a heuristic predictive model which avoided the formation of new azeotropic or near boiling point systems between the entrainer and heavy components for separating azeotropic systems, which result in new difficulties in solvent recovery and utilization, to minimize the impacts of TAC and LCA to screen the optimal entrainer for the simulation and optimization of the chemical separation process design. The novel heuristic predictive model is based on TAC and LCA indexes and qualitatively screens the entrainer with superior economic and green sustainable performance in the extractive distillation process according to the trend relationship between the relative volatility of heavy components to the entrainer and LCA indexes, the tray number of the solvent recovery column (NT) of TEDI and LTEDI, TAC, hot steam consumption (Q(R)), green sustainability (AP, GWP) and toxicity (FETP, HTP, TETP), which is predictable and feasible and solves the difficulty that the change of relative volatility of light-heavy components after adding the entrainer is not very different or the contrast is not obvious and comprehensively considers the optimal economic, environment-friendly and green sustainable entrainer. While pursuing the minimum economics of processed optimization, the LCA indexes of the heuristic predictive model also reveal the environmental friendliness and green sustainability effects of the screening solvent because it does not require a large amount of simulation and optimization work to verify that it can select an appropriate solvent which is economical, environmentally friendly and sustainable. In addition, it can be extended to separate ternary, quaternary and other multi-component azeotropes with great efficiency and high accuracy. Moreover, it provides a novel predictive theoretical rule of more dimensional objectives and the reference significance of systematic evaluation for the screening and application of deep eutectic solvents, ionic liquids, and their mixed solvents as well as the evaluation of the economics, environmental friendliness and sustainability of the simulation and optimization technology of the chemical process design.