Optimal design and energy intensity comparison of industrialized routes for electronic-grade dimethyl carbonate synthesis: Toward sustainable strategies
Dimethyl carbonate (DMC), as electrolyte solvent with strong ionic conductivity and excellent stability, is widely used in lithium batteries. In addition, DMC could be used as raw material for polycarbonate synthesis. There are a series of industrial production routes for DMC, such as the transester...
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| Vydáno v: | Separation and purification technology Ročník 382; s. 135983 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
26.02.2026
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| Témata: | |
| ISSN: | 1383-5866 |
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| Abstract | Dimethyl carbonate (DMC), as electrolyte solvent with strong ionic conductivity and excellent stability, is widely used in lithium batteries. In addition, DMC could be used as raw material for polycarbonate synthesis. There are a series of industrial production routes for DMC, such as the transesterification of ethylene carbonate (EC) with methanol (MeOH), the vapor-phase MeOH oxidative carbonylation method, and the indirect urea alcoholysis method, among others. However, systematic comparative investigations on these industrialized routes are still lacking, making it impossible to comprehensively assess the advantages and industrialization prospects of different DMC production processes. The optimization of DMC production processes falls under the category of rigorous Mixed-Integer Nonlinear Programming (MINLP) problems. In the transesterification route, the significant energy consumption stems from the separation of the DMC-MeOH azeotrope. To address this, strategies like heat pumps, extractive distillation, and pervaporation have been employed for process intensification, aiming to break the DMC-MeOH azeotrope. To ensure that the various DMC production routes are compared under optimal conditions, this present work integrates Genetic Algorithm (GA) with rigorous simulation to optimize the process parameters of each industrialized route. Concurrently, detailed comparisons are conducted in terms of economic indicators, environmental metrics, and exergy efficiency. The optimization results validate the robustness and effectiveness of the proposed optimization strategy, and the route for the direct synthesis of DMC from carbon dioxide (CO2) and MeOH demonstrates competitive advantages.
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•Process intensification for azeotrope separation using heat pump, extractive distillation and pervaporation realized.•Stochastic optimization algorithm coupled with process simulation for parameters optimization.•Series industrialized routes for Dimethyl Carbonate synthesis compared in terms of economic, environmental and exergy indexes.•Energy-efficient route for Dimethyl Carbonate synthesis obtained toward sustainable synthesis strategy. |
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| AbstractList | Dimethyl carbonate (DMC), as electrolyte solvent with strong ionic conductivity and excellent stability, is widely used in lithium batteries. In addition, DMC could be used as raw material for polycarbonate synthesis. There are a series of industrial production routes for DMC, such as the transesterification of ethylene carbonate (EC) with methanol (MeOH), the vapor-phase MeOH oxidative carbonylation method, and the indirect urea alcoholysis method, among others. However, systematic comparative investigations on these industrialized routes are still lacking, making it impossible to comprehensively assess the advantages and industrialization prospects of different DMC production processes. The optimization of DMC production processes falls under the category of rigorous Mixed-Integer Nonlinear Programming (MINLP) problems. In the transesterification route, the significant energy consumption stems from the separation of the DMC-MeOH azeotrope. To address this, strategies like heat pumps, extractive distillation, and pervaporation have been employed for process intensification, aiming to break the DMC-MeOH azeotrope. To ensure that the various DMC production routes are compared under optimal conditions, this present work integrates Genetic Algorithm (GA) with rigorous simulation to optimize the process parameters of each industrialized route. Concurrently, detailed comparisons are conducted in terms of economic indicators, environmental metrics, and exergy efficiency. The optimization results validate the robustness and effectiveness of the proposed optimization strategy, and the route for the direct synthesis of DMC from carbon dioxide (CO2) and MeOH demonstrates competitive advantages.
[Display omitted]
•Process intensification for azeotrope separation using heat pump, extractive distillation and pervaporation realized.•Stochastic optimization algorithm coupled with process simulation for parameters optimization.•Series industrialized routes for Dimethyl Carbonate synthesis compared in terms of economic, environmental and exergy indexes.•Energy-efficient route for Dimethyl Carbonate synthesis obtained toward sustainable synthesis strategy. |
| ArticleNumber | 135983 |
| Author | Yuan, Xigang Ge, Xiaolong Liu, Botan Gao, Ge |
| Author_xml | – sequence: 1 givenname: Ge surname: Gao fullname: Gao, Ge organization: College of Chemical Engineering and Materials Science, Tianjin Key Laboratory of Brine Chemical Engineering and Resource Eco-utilization, Tianjin University of Science and Technology, Tianjin 300457, China – sequence: 2 givenname: Botan surname: Liu fullname: Liu, Botan organization: College of Chemical Engineering and Materials Science, Tianjin Key Laboratory of Brine Chemical Engineering and Resource Eco-utilization, Tianjin University of Science and Technology, Tianjin 300457, China – sequence: 3 givenname: Xiaolong surname: Ge fullname: Ge, Xiaolong email: gexiaolong@tust.edu.cn organization: College of Chemical Engineering and Materials Science, Tianjin Key Laboratory of Brine Chemical Engineering and Resource Eco-utilization, Tianjin University of Science and Technology, Tianjin 300457, China – sequence: 4 givenname: Xigang surname: Yuan fullname: Yuan, Xigang organization: State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300350, China |
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