Abstract Furfural solvent extraction is a conventional process that removes toxic polycyclic aromatics (PCAs) from eco-friendly processing oil. To increase the product yield and extraction selectivity, the present study introduces water as the co-solvent and investigates its effect using both experimental and theoretical approaches. The effects of the water content in furfural on the product yield, PCA content, aromatic carbon fraction and hydrocarbon type distribution are systematically investigated and optimized. At the appropriate furfural/water ratio in two-stage extraction, the product yield increases by 9 wt% while maintaining the product quality at an acceptable level. An a priori COnductor-like Screening Model for Segment Activity Coefficient (COSMO-SAC) model is used to predict the product composition after extraction. The prediction is consistent with the experimental data. This model can identify the optimal water content using only one experimental result. The result shows that controlling the water content in furfural is critically important during the furfural-extract oil (FEO) furfural extraction process. Both the experimental data and the simulation method in this paper are expected to help control and optimize the extraction process, as well as provide a better understanding of the process.

中文翻译：

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水作为共溶剂在环保加工油提取中的作用：实验数据和理论方法的优化

摘要 糠醛溶剂萃取是一种从环保加工油中去除有毒多环芳烃 (PCA) 的常规工艺。为了提高产品收率和提取选择性，本研究引入水作为共溶剂，并使用实验和理论方法研究其效果。系统研究并优化了糠醛中水分对产物收率、PCA含量、芳香碳分数和烃类分布的影响。在适当的糠醛/水比下，两步提取中，产品收率提高了 9 wt%，同时将产品质量保持在可接受的水平。段活性系数的先验类导体筛选模型 (COSMO-SAC) 模型用于预测提取后的产品组成。预测与实验数据一致。该模型仅使用一个实验结果即可确定最佳含水量。结果表明，在糠醛提取油 (FEO) 糠醛提取过程中，控制糠醛中的水含量至关重要。本文中的实验数据和模拟方法都有望帮助控制和优化提取过程，并提供对过程的更好理解。