Optimization of Poly(ethylene terephthalate) Fiber Degradation by Response Surface Methodology Using an Amino Acid Ionic Liquid Catalyst

被引:11
|
作者
Liu, Lifei [1 ,2 ]
Yao, Haoyu [1 ,2 ]
Zhou, Qing [1 ,2 ,3 ]
Yan, Dongxia [1 ,2 ]
Xu, Junli [1 ,2 ,3 ]
Lu, Xingmei [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Beijing Key Lab Ion Liquids Clean Proc, State Key Lab Multiphase Complex Syst, CAS Key Lab Green Proc & Engn,Inst Proc Engn, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Chem Engn, Beijing 100049, Peoples R China
[3] Chinese Acad Sci, Innovat Acad Green Mfg, Inst Proc Engn, Beijing 100190, Peoples R China
来源
ACS ENGINEERING AU | 2022年 / 2卷 / 04期
基金
中国国家自然科学基金;
关键词
poly(ethylene terephthalate) (PET); glycolysis; ionic liquid (IL); optimization; catalysis; response surface methodology (RSM); POLYETHYLENE TEREPHTHALATE; GLYCOLYSIS; CONVERSION; PET;
D O I
10.1021/acsengineeringau.1c00039
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
With large quantities of poly(ethylene terephthalate) (PET) fibers being used and discarded each year, the chemical recycling of PET fibers is of far-reaching importance. The metal-based catalysts currently used in chemical recycling often lead to metal residues and product coloration, which limits the industrialization of PET recycling. In this work, environmentally friendly amino acid ionic liquids were synthesized, which can replace the conventional zinc acetate catalyst for the glycolysis of PET fibers, enabling 100% PET fiber conversion and 84.5% bis(2hydroxyethyl) terephthalate (BHET) yields. Meanwhile, the effects of different factors on the process were explored by response surface methodology (RSM), and the optimal conditions (125 min, 193.5 degrees C, 8 wt % catalyst dosage, and 15.7 g of ethylene glycol (EG)) were predicted, which were further verified experimentally. The error between the model prediction and the experimental value was only 3.49%, which proved the reliability of the model. This method breaks the limitation of a single-factor study and allows exploring the relationship between multiple factors simultaneously to find the optimal solution.
引用
收藏
页码:350 / 359
页数:10
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