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Process evaluation for the separation of acetonitrile-water using extractive distillation with deep eutectic solvents as entrainers.

Authors :
Han, Dongmin
Chen, Yanhong
Dong, Mengru
Source :
Computers & Chemical Engineering. Aug2022, Vol. 164, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

· σ-profiles based COSMO-SAC theory is adopted to screen DESs entrainers for acetonitrile dehydration. · The selected DESs are further studied by the thermodynamic topological analysis. · For extractive distillation, EG and GC3:1 are evaluated economically. · The control structure of GC3:1 extractive distillation process is studied. Deep Eutectic Solvents (DESs) show properties that make them effective as environmental-friendly entrainers for extractive distillation. However, the technical and economic evaluation via process simulation and the method of screening a suitable DES for a given azeotropic system are less studied. In this work, the extractive distillation process for acetonitrile dehydration with glycolic acid/choline chloride 3:1 (GC3:1) as entrainer is studied. Firstly, the σ-profiles based on the COSMO-SAC model are employed to select an appropriate DES entrainer. Then, the feasibilities of GC3:1 and ethylene glycol (EG) as entrainer are further studied by the analysis of residue curve maps. Finally, the design and optimization of the extractive distillation process for acetonitrile dehydration with GC3:1 and EG are conducted. The results demonstrate that the conventional extractive distillation process with GC3:1 entrainer gives 13.40% and 9.64% reduction in TAC over the conventional and heat-integrated extractive distillation processes with EG as entrainer, respectively. The TAC of the GC3:1 process is further reduced by 3.14% after heat integration. In addition, the control scheme of the extractive distillation process with GC3:1 entrainer is studied by Aspen Dynamics, and the proposed control structure is effective in managing operational stability. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00981354
Volume :
164
Database :
Academic Search Index
Journal :
Computers & Chemical Engineering
Publication Type :
Academic Journal
Accession number :
158157847
Full Text :
https://doi.org/10.1016/j.compchemeng.2022.107865