Extraction distillation and azeotropic distillation are two important methods for separating isopropyl alcohol (IPA) and water azeotrope. However, azeotropic distillation is generally more energy-intensive than extractive distillation separation process. Therefore, solvent design and process intensification for the extractive distillation process are the keys to addressing the problems of azeotropic separation and reducing energy consumption. In this contribution, a deep learning-based solvent high throughput screening framework was proposed to design the green solvent for separating the IPA/water mixtures. All properties, such as thermodynamic properties and EH&S properties, used for screening were predicted by the deep learning-based predictive models. From more than 108 individual molecules, five green solvent candidates were screened for the separation of IPA/water azeotrope. The energy consumption analysis of 5 solvents shows that ethylene glycol as solvent has the lowest separating energy consumption. Finally, the heat integration and heat pump distillation of the extractive distillation separation process was carried out, and the energy-saving potential reached 45.86%.

中文翻译：

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深度学习驱动的绿色溶剂设计和工艺强化，面向异丙醇-水共沸体系


萃取蒸馏和共沸蒸馏是分离异丙醇 （IPA） 和水共沸物的两种重要方法。然而，共沸蒸馏通常比萃取蒸馏分离过程更耗能。因此，萃取精馏过程的溶剂设计和工艺强化是解决共沸分离问题和降低能耗的关键。在这项研究中，提出了一个基于深度学习的溶剂高通量筛选框架，用于设计用于分离 IPA/水混合物的绿色溶剂。用于筛选的所有特性，如热力学特性和 EH&S 特性，都由基于深度学习的预测模型预测。从超过 108 个单独的分子中筛选出 5 种候选绿色溶剂，用于分离 IPA/水共沸物。5 种溶剂的能耗分析表明，乙二醇作为溶剂的分离能耗最低。最后，对萃取精馏分离工艺进行了热集成和热泵蒸馏，节能潜力达到45.86%。