Ontology- and Data-Driven Circularity Assessment of Waste-Derived Rigid PVC-u: A Micro–Macro Decision Framework for Sustainable Recycling and Smart Manufacturing

Abstract

The circularity of waste-derived rigid unplasticised poly (vinyl chloride) (PVC-u) is constrained by heterogeneous waste-stream sources, degraded material quality, legacy additives, and inconsistent decision criteria spanning technical, environmental, and economic aspects. In this work, we developed an ontology- and data-driven circularity assessment framework for waste-derived rigid PVC-u recycling systems, built on a Protégé ontology coupled with big data analytics. The framework structures knowledge on materials, recycling processes, process parameters, quality aspects, sustainability measures, carbon aspects, and cost aspects, and links it to process, operational, and business data. Semantic reasoning, based on rule-based and query-based logics, derives circularity, environmental, and economic measures and improves assessment consistency across different recycling cases. The assessment is carried out from two interconnected perspectives: a micro-level perspective focused on efficiency, quality, waste, energy, cost, and emissions at the plant level, and a macro-level perspective focused on circularity, resource retention, industrial value recovery, and net-zero implications at the system level. The combination of ontological modelling and analytics-driven assessment facilitates transparent, rule-based and traceable circularity assessment of recycling, processing and value recovery pathways for waste-derived rigid PVC-u. Applying the framework to three illustrative, literature-informed synthetic scenarios illustrates its ability to translate plant-level data into consistent, traceable micro- and macro-level circularity assessments aligned with circular economy and net-zero goals. Monte Carlo propagation of the scenario parameter ranges shows that every recycling route remains a net emission saver, whereas cost recovery and carbon-price viability differ strongly between routes, and that the 2030 circularity targets are met only if about 60% (circular material use) to 81% (end-of-life recycling) of the projected improvement is realised. These results are illustrative: we have not yet used industrial plant data, and empirical validation with recyclers is a priority for future work. This paper contributes an ontology- and data-driven circularity assessment framework for the sustainable recycling and smart manufacturing of waste-derived rigid PVC-u and outlines an approach to combine semantic intelligence with industrial analytics for circularity assessment.