Pham, Duong Dinh Vo, Dat-Nguyen Phong, Mai Thanh Nguyen, Huu Hieu Nguyen-Thoi, Trung Pham, Thuy-Phuong T. Phuong, Duy Ha Le Pham, Le Kim Hoang Won, Da Hye Nguyen, Dang Le Tri Nguyen, Tung M. 2024-06-20T02:30:08Z 2024-06-20T02:30:08Z 2024-06-20 2024-06 1385-8947 https://pubs.kist.re.kr/handle/201004/150090 The relentless growth of plastic has emerged as a significant environmental and human health concern. Catalytic glycolysis of polyethylene terephthalate (PET) has proven to be an effective solution. This study investigated a series of M-BDC (M = Ni, Co, Cu, and Zn) metal -organic frameworks as catalysts for PET glycolysis. Zn-BDC exhibited the best experimental performance. Subsequently, the effects of operating conditions were optimized for the first time through a comprehensive investigation using a model based on deep neural networks (DNNs). The guidance of the DNN model resulted in a BHET selectivity as high as 0.95, outperforming most current heterogeneous catalysts for PET depolymerization. Furthermore, the apparent activation energy was also estimated by kinetic study. In addition, our designed system exhibits high durability after five consecutive runs, reflecting its promising escalation at the industrial level. Notably, the fabrication of the M-BDC framework can utilize the organic linker terephthalic acid (TPA) derived from the BHET monomer. By applying this strategy, we have achieved a significant advancement towards closed-loop PET recycling, contributing to a more comprehensive definition of sustainable development. English Elsevier BV A new circulation in glycolysis of polyethylene terephthalate using MOF-based catalysts for environmental sustainability of plastic Article 10.1016/j.cej.2024.151667 1 Chemical Engineering Journal, v.490 Chemical Engineering Journal 490 N scie scopus 001241053300001 2-s2.0-85193904110 Engineering, Environmental Engineering, Chemical Engineering Article COORDINATION METAL-ORGANIC FRAMEWORK POLY(ETHYLENE-TEREPHTHALATE) NANOPARTICLES WASTE PET glycolysis Metal -organic frameworks Deep Neural Networks Machine learning Artificial intelligence Plastic circulation