Optimizing Circular Waste Matchmaking Through Social Resource Networks Using Predictive Analytics and Resource Compatibility Modeling

Author

Basil Derel


Abstract

 Growing material consumption, resource scarcity, and escalating waste generation are intensifying the need for circular systems capable of retaining the economic and functional value of residual resources across industrial and community networks. However, conventional waste exchanges frequently depend on fragmented listings, predefined material categories, and manual partner searches, limiting their ability to identify viable relationships between heterogeneous waste streams and resource demands. This study proposes an optimized circular waste matchmaking framework based on Social Resource Networks (SRNs), predictive analytics, and resource compatibility modeling. The framework represents waste generators, resource users, intermediaries, and material streams as interconnected network entities while integrating information concerning material composition, quantity, quality, location, availability, processing requirements, historical exchanges, and organizational demand. Predictive models anticipate resource availability and demand patterns, whereas compatibility modeling evaluates technical suitability, spatial proximity, temporal alignment, economic feasibility, environmental value, and exchange constraints. Match scores enable candidate exchanges to be prioritized according to multidimensional compatibility. Continuous feedback from accepted, rejected, and completed transactions subsequently refines predictions and matching decisions. The framework enables proactive opportunity discovery, reduces information asymmetry, improves secondary-resource utilization, and strengthens adaptive cross-sector circular resource exchange ecosystems.



Keywords

Social Resource Networks; Circular Waste Matchmaking; Predictive Analytics; Resource Compatibility Modeling; Industrial Symbiosis; Circular Economy



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References


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