An evolutionary study on technologies for polyethylene terephthalate waste recycling using natural language processing

Abstract

Polyethylene terephthalate (PET) is valued for its durability, tensile strength, low moisture absorption, and cost-effectiveness. However, its non-biodegradability poses an environmental threat, and plastic recycling is the sole remedy. This study proposes an NLP framework for concisely extracting and summarizing key information on recycling technologies and alternatives from relevant scientific literature. This NLP framework comprises three approaches: time-series knowledge graphs, dynamic transformer-based topic modeling, and estimating popularity indices for technologies. The framework aims to streamline the extraction of qualitative and quantitative insights for sustainable and economical PET waste recycling pathways. Key findings of the study show that there is a 406% rise in pyrolysis technology use, a 278% increase in chemical conversion, and a 1353% surge in waste PET utilization for electronic device-making. It is worth noting that some of the identified recycling pathways corroborate well with the actual implementation in the industries.