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

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Optimization of Poly(ethylene terephthalate) Fiber Degradation by Response Surface Methodology Using an Amino Acid Ionic Liquid Catalyst
ACS Engineering Au ( IF 4.3 ) Pub Date : 2022-04-15 , DOI: 10.1021/acsengineeringau.1c00039
Lifei Liu _{1,

2} , Haoyu Yao _{1,

2} , Qing Zhou _{1,

2,

3} , Dongxia Yan _{1,

2} , Junli Xu _{1,

2,

3} , Xingmei Lu _{1,

2,

3}

Affiliation

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(2-hydroxyethyl) 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 °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.

中文翻译：

使用氨基酸离子液体催化剂通过响应面法优化聚（对苯二甲酸乙二醇酯）纤维降解

随着每年大量使用和丢弃的聚对苯二甲酸乙二醇酯 (PET) 纤维，PET 纤维的化学回收具有深远的意义。目前用于化学回收的金属基催化剂往往会导致金属残留和产品着色，这限制了PET回收的产业化。在这项工作中，合成了环保型氨基酸离子液体，可以替代传统的醋酸锌催化剂用于 PET 纤维的糖酵解，实现 100% 的 PET 纤维转化率和 84.5% 的对苯二甲酸双 (2-羟乙基) 酯 (BHET) 产率。同时，通过响应面法（RSM）探讨了不同因素对工艺的影响，并预测了最佳条件（125 min、193.5 ℃、8 wt %催化剂用量和15.7 g乙二醇（EG）） , 并通过实验进一步验证。模型预测与实验值的误差仅为3.49%，证明了模型的可靠性。该方法打破了单因素研究的局限，可以同时探索多个因素之间的关系，从而找到最优解。

更新日期：2022-04-15

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