Heuristic process prediction model for screening optimal green entrainers based on TAC and LCA impacts utilizing PSE concepts

Abstract

The key to extractive distillation separation lies in screening a suitable entrainer. Based on chemical process system engineering (PSE) concepts, a novel heuristic process prediction model is proposed with a minimized process economic cost index (PECI) for the efficient screening of an optimal green entrainer with the advantages of cost-effectiveness, environmental friendliness, and sustainability, followed by outlining the principles of characterizations and mathematical modeling in chemical ED processes. The relationships of α_HC/SOL and NTR with Q_R, TAC, PECI, and LTEDI indicated that the heuristic process prediction model was scientifically valid as well as efficient. Moreover, the proposed model progresses from binomial intersecting influencing factors to trinomial juxtaposing influencing factors; summarizes and discloses the system's theoretical rules and influencing elements of the changes in parameters and their relative volatility, avoiding the new azeotropes in the solvent recovery column; reduces the workloads of process simulation and optimization of distillation processes from (M × N) times to (1 + N) times; can be extended efficiently and accurately to provide the targeted prediction and systematic theoretical principles for separating different feed azeotropes, predicting and screening organic solvents, ionic liquids, deep eutectic solvents, and mixed solvents with the optimal TAC and environmental friendliness (AP and GWP) and sustainability (FETP, HTP, and TETP) as LCA impacts; has broad predictability and applicability combined with different process optimization algorithms; and provides infinite composite patterns and opportunities for integrating and developing innovative optimization module algorithms for future green chemical separation processes.