Efficient Single-Column Extractive Distillation Process Achieved through Vapor–Liquid Separation of Feed

Hu, Nan; Zhang, Yuxin; Hu, Xiwei; Gu, Junjie; Xue, Jiaxing; Li, Qunsheng

doi:10.1021/acs.iecr.3c02951

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…The Component Object Model technology facilitated data exchange between Matlab and Aspen Plus. The Particle Swarm Optimization (PSO) algorithm was employed to optimize the parameters of the distillation process in Matlab [16]. The PSO algorithm obtained different particles (representing distillation parameters).…”

Section: Optimization Processmentioning

confidence: 99%

The Impact of Feed Composition on Entrainer Selection in the Extractive Distillation Process

Li,

Wu,

Zhao

et al. 2024

Processes

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The selection of entrainers primarily focuses on their ability to alter the relative volatility. However, the impact of feed composition on entrainer selection has often been overlooked. In this study, we conducted two case analyses of the minimum azeotropic mixtures in the ethyl acetate–ethanol and acetone–methanol systems to explore how the feed composition influences the entrainer selection when aiming for maximum economic efficiency. Additionally, the impact of the entrainer type (positive or reverse) on the economic benefits was also investigated. The cases revealed that economic benefits will be notably enhanced when the selected entrainer preferentially targets and removes the low-content component as the light key component. For the two cases studied, when the feed composition was 0.2–0.8, compared to preferentially separating the high-content component, preferentially separating the lower-content component resulted in a reduction in energy consumption by more than 24.14% and 22.72%, respectively. The results show that the ideal entrainer should be capable of converting the higher-content component in the feed into the heavy key component.

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Section: Optimization Processmentioning

confidence: 99%