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Ontology-Based Modelling and Analysis of Sustainable Polymer Systems: PVC Comparative Polymer and Implementation Perspectives

Journal of Materials Science 2025 2 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Alexander Chidara, Kai Cheng, David Gallear

Summary

This study developed an ontology-based decision support framework using semantic reasoning to evaluate and optimize sustainable recycling pathways for polymers including PET, PLA, and PVC within a circular economy context. A UK industrial case study validated the framework and revealed that additive complexity in PVC and performance gaps between virgin and recycled materials remain major barriers to large-scale recycling.

This study develops an ontology-based decision support framework to enhance sustainable polymer recycling within the circular economy. The framework, constructed in Protégé (OWL 2), systematically captures polymer categories with emphasis on polyethylene terephthalate (PET), polylactic acid (PLA), and rigid polyvinyl chloride (PVC) as well as recycling processes, waste classifications, and sustainability indicators such as carbon footprint. Semantic reasoning was implemented using the Semantic Web Rule Language (SWRL) and SPARQL Protocol and RDF Query Language (SPARQL) to infer optimal material flows and sustainable pathways. Validation through a UK industrial case study confirmed both the framework's applicability and highlighted barriers to large-scale recycling, including performance gaps between virgin and recycled polymers. The comparative analysis showed carbon footprints of 2.8 kg CO2/kg for virgin PET, 1.5 kg CO2/kg for PLA, and 2.1 kg CO2/kg for PVC, underscoring material-specific sustainability challenges. Validation through a UK industrial case study further highlighted additive complexity in PVC as a major barrier to large scale recycling. Bibliometric and thematic analyses conducted in this study revealed persistent gaps in sustainability metrics, lifecycle assessment, and semantic support for circular polymer systems. By integrating these insights, the proposed framework provides a scalable, data-driven tool for evaluating and optimising polymer lifecycles, supporting industry transitions toward resilient, circular, and net-zero material systems.

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