Comparative optimal design and effective control of different pressure extractive distillation for separating acetone - Methanol

萃取蒸馏 共沸物 工艺工程 蒸馏 化学 分馏 沸点 多效蒸馏 工艺设计 过程集成 工程类 色谱法 有机化学
作者
Jiyan Liu,Mengru Dong,Junyao Ren,Yang Wu,Jie Kong,Guanghao Wan,Lanyi Sun
出处
期刊:Separation and Purification Technology [Elsevier BV]
卷期号:301: 121936-121936 被引量:16
标识
DOI:10.1016/j.seppur.2022.121936
摘要

Acetone and methanol are critical industrial feedstocks as well as organic solvents. Previous studies on the separation of the minimum-boiling azeotrope acetone-methanol by pressure-swing extractive distillation have shown superiority in terms of energy and cost savings. However, the pressure-varying configuration inevitably requires high-grade heat steam. To surpass the limitation, a novel different pressure extractive distillation strategy is proposed in this paper. The conceptual design of the different pressure extractive distillation configuration is carried out from thermodynamic insights, and the feasible ranges of entrainer/feed flow rate ratio and reflux ratio are determined. The design parameters are optimized by an improved non-dominated sorting genetic algorithm. Compared with the conventional extractive distillation process, the total annual cost (TAC) and CO2 emissions of the proposed process are reduced by 22.17% and 48.14%, respectively. An alternative different pressure extractive distillation process with feed preheating is then obtained by changing feed thermal condition, which has a 24.08% reduction in TAC and a 48.33% reduction in CO2 emissions in comparison with the conventional extractive distillation process. Compared with the existing heat-integrated pressure-swing extractive distillation process, the new process has a 2.90% and 22.36% reduction in TAC and CO2 emissions, respectively. Subsequently, two control schemes are presented by calculating controllability indicators in the steady-state design stage. The results illustrate that the improved control scheme with multiple feedforward control loops performs the best robustness.
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