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Formalization of Fabric Recycling Methods Depending on Raw Material Composition for Intelligent Decision Support Systems
(2026) Zagorodnya, A.; Tymofiiev, I.; Molchanova, M.; Mazurets, O.; Мазурець, Олександр Вікторович
The paper proposes a formalization of fabric recycling methods depending on raw material composition for intelligent decision support systems in textile recycling. The study focuses on combining computer vision, machine learning, and rule-based decision mechanisms to support automated textile waste sorting and processing. The proposed approach includes classification of fabrics into raw material categories, assessment of prediction reliability, and generation of recommendations for further handling, such as reuse, mechanical recycling, chemical recycling, downcycling, or manual inspection. The methodology emphasizes not only fabric recognition but also the intelligent selection of the most appropriate recycling scenario. Experimental results demonstrate that computer vision models can achieve high accuracy in distinguishing natural and synthetic fabrics, creating a practical basis for automated textile recycling systems. The proposed approach can be applied in textile sorting lines, recycling centers, and digital decision support services for sustainable waste management.