Energy-Efficient and Low-Carbon Recovery of Organic Compounds via Electrified Deep Eutectic Solvent-Based Extractive Distillation

Abstract Methyl ethyl ketone (MEK) and ethanol (EtOH) form an azeotrope at atmospheric pressure, making their effective separation and recovery by conventional distillation difficult. In this study, two imidazole-based deep eutectic solvents (DESs) were selected as entrainers for extractive distillation of the MEK-EtOH azeotrope. Their separation performance was evaluated by ternary vapor-liquid equilibrium (VLE) experiments, and the data were correlated using the non-random two-liquid (NRTL) model. Quantum chemical calculations revealed that strong hydrogen-bonding interactions between the DESs and azeotropic components are essential for efficient separation. Extractive distillation processes using dimethyl sulfoxide (DMSO) and the best-performing DES as entrainers were designed and optimized via multi-objective optimization coupled with multi-criteria decision-making. Life cycle assessment was performed using Eco-indicator 99 (EI-99). Compared with the DMSO-based process, the DES-based process achieved lower total annual cost (TAC), CO2 emissions (ECO2), and EI-99 score, but exhibited a relatively lower thermodynamic efficiency. Compared with the non-electrified DES-based process, the flash vapor compression heat pump (FVCHP)-electrified DES-based process reduced TAC, ECO2, and EI-99 by 5.81%, 43.64%, and 50.71%, respectively. This study offers a low-carbon and economical strategy for azeotrope separation.

Authors

Institutions

Publication Details

Journal
ACS Sustainable Chemistry & Engineering
Published
2026-11-10
DOI
https://doi.org/10.1021/acssuschemeng.6c08298
Primary Topic
Process Optimization and Integration
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Energy-Efficient and Low-Carbon Recovery of Organic Compounds via Electrified Deep Eutectic Solvent-Based Extractive Distillation

Lanyi Sun, Yan Cheng, Jun Li, Xinyuan Cheng et al.
ACS Sustainable Chemistry & Engineering
Process Optimization and Integration
article

Energy-Efficient and Low-Carbon Recovery of Organic Compounds via Electrified Deep Eutectic Solvent-Based Extractive Distillation

Lanyi Sun, Yan Cheng, Jun Li, Xinyuan Cheng, Zhanhua Ma, Jing Yang, Haigang Liu, Sen Li, Renting Li, Xiao Wang
article en

Abstract

Abstract Methyl ethyl ketone (MEK) and ethanol (EtOH) form an azeotrope at atmospheric pressure, making their effective separation and recovery by conventional distillation difficult. In this study, two imidazole-based deep eutectic solvents (DESs) were selected as entrainers for extractive distillation of the MEK-EtOH azeotrope. Their separation performance was evaluated by ternary vapor-liquid equilibrium (VLE) experiments, and the data were correlated using the non-random two-liquid (NRTL) model. Quantum chemical calculations revealed that strong hydrogen-bonding interactions between the DESs and azeotropic components are essential for efficient separation. Extractive distillation processes using dimethyl sulfoxide (DMSO) and the best-performing DES as entrainers were designed and optimized via multi-objective optimization coupled with multi-criteria decision-making. Life cycle assessment was performed using Eco-indicator 99 (EI-99). Compared with the DMSO-based process, the DES-based process achieved lower total annual cost (TAC), CO2 emissions (ECO2), and EI-99 score, but exhibited a relatively lower thermodynamic efficiency. Compared with the non-electrified DES-based process, the flash vapor compression heat pump (FVCHP)-electrified DES-based process reduced TAC, ECO2, and EI-99 by 5.81%, 43.64%, and 50.71%, respectively. This study offers a low-carbon and economical strategy for azeotrope separation.

ACS Sustainable Chemistry & Engineering
China University of Petroleum, East China (CN)
Process Optimization and Integration
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon