Multilevel screening and mechanism analysis of ionic liquids for separating pyridine from coal pyrolysis model oil

IF 9 1区工程技术 Q1 ENGINEERING, CHEMICAL Separation and Purification Technology Pub Date : 2025-07-30 Epub Date: 2025-01-28 DOI:10.1016/j.seppur.2025.131856

Qian Liu , Wei Meng , Yuxin Qiu , Lifang Chen , Zhen Song , Zhiwen Qi

{"title":"Multilevel screening and mechanism analysis of ionic liquids for separating pyridine from coal pyrolysis model oil","authors":"Qian Liu , Wei Meng , Yuxin Qiu , Lifang Chen , Zhen Song , Zhiwen Qi","doi":"10.1016/j.seppur.2025.131856","DOIUrl":null,"url":null,"abstract":"<div><div>The separation of high-purity pyridine from coal pyrolysis oil is a typical challenge faced in the coal chemical industry for the high-value utilization of resources. In this study, a systematic framework combining multilevel screening and mechanism exploration is presented to investigate ionic liquids (ILs) for efficiently separating pyridine from toluene as a representative of coal pyrolysis oil. First, the COSMO-RS model is used to predict the key thermodynamic properties of ILs, thereby pre-screening ILs with high separation potential. Then, a deep learning method is employed to estimate the key physicochemical properties of ILs for further IL screening. Subsequently, the performance of the remaining ILs in a continuous extraction and extractive distillation is evaluated in Aspen Plus to obtain better-performing ILs in terms of energy and solvent consumption. The optimal three ILs, namely [C<sub>3</sub>OHPy][C(CN)<sub>3</sub>], [C<sub>4</sub>mim][HSO<sub>4</sub>], and [C<sub>3</sub>OHC<sub>1</sub>Pyr][C(CN)<sub>3</sub>] are determined through the multilevel screening strategy. Finally, quantum chemical calculation and molecular dynamics simulation are elaborated to reveal the separation mechanism at the microscopic scale and verify the reliability of the screening results.</div></div>","PeriodicalId":427,"journal":{"name":"Separation and Purification Technology","volume":"362 ","pages":"Article 131856"},"PeriodicalIF":9.0000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Separation and Purification Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1383586625004538","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}

引用次数: 0

Abstract

The separation of high-purity pyridine from coal pyrolysis oil is a typical challenge faced in the coal chemical industry for the high-value utilization of resources. In this study, a systematic framework combining multilevel screening and mechanism exploration is presented to investigate ionic liquids (ILs) for efficiently separating pyridine from toluene as a representative of coal pyrolysis oil. First, the COSMO-RS model is used to predict the key thermodynamic properties of ILs, thereby pre-screening ILs with high separation potential. Then, a deep learning method is employed to estimate the key physicochemical properties of ILs for further IL screening. Subsequently, the performance of the remaining ILs in a continuous extraction and extractive distillation is evaluated in Aspen Plus to obtain better-performing ILs in terms of energy and solvent consumption. The optimal three ILs, namely [C₃OHPy][C(CN)₃], [C₄mim][HSO₄], and [C₃OHC₁Pyr][C(CN)₃] are determined through the multilevel screening strategy. Finally, quantum chemical calculation and molecular dynamics simulation are elaborated to reveal the separation mechanism at the microscopic scale and verify the reliability of the screening results.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

用于从煤热解模型油中分离吡啶的离子液体的多级筛选和机理分析

从煤热解油中分离高纯吡啶是煤化工行业实现资源高价值利用所面临的典型挑战。本研究采用多层次筛选和机理探索相结合的系统框架，研究了以煤热解油为代表的离子液体对吡啶和甲苯的高效分离作用。首先，利用cosmos - rs模型预测il的关键热力学性质，从而预筛选具有高分离电位的il；然后，采用深度学习方法估计IL的关键物理化学性质，以便进一步筛选IL。随后，在Aspen Plus中对连续萃取和萃取精馏中剩余il的性能进行评估，以获得在能量和溶剂消耗方面性能更好的il。通过多级筛选策略确定了[C3OHPy][C(CN)3]、[C4mim][HSO4]和[C3OHC1Pyr][C(CN)3]这三个最优il。最后，通过量子化学计算和分子动力学模拟，揭示了微观尺度上的分离机理，验证了筛选结果的可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Separation and Purification Technology 工程技术-工程：化工

CiteScore

14.00

自引率

12.80%

发文量

2347

审稿时长

43 days

期刊介绍： Separation and Purification Technology is a premier journal committed to sharing innovative methods for separation and purification in chemical and environmental engineering, encompassing both homogeneous solutions and heterogeneous mixtures. Our scope includes the separation and/or purification of liquids, vapors, and gases, as well as carbon capture and separation techniques. However, it's important to note that methods solely intended for analytical purposes are not within the scope of the journal. Additionally, disciplines such as soil science, polymer science, and metallurgy fall outside the purview of Separation and Purification Technology. Join us in advancing the field of separation and purification methods for sustainable solutions in chemical and environmental engineering.