Generative AI-enabled adaptive material reintegration framework for proactive circular economy waste reclamation in food industrial ecosystems

The growing global demand for food and the inefficiencies across production, processing, and consumption stages have led to alarming levels of food waste, posing serious economic, environmental, and social challenges. Existing studies on food waste reclamation largely employ static optimization or traditional predictive models such as standard VAEs, CVAEs, and evolutionary algorithms, which fail to capture the temporal variability and multi-objective nature of food systems. To address these limitations, this study introduces an innovative AI-driven proactive framework that integrates a Temporal-Gradient Conditional Variational Autoencoder (TG-CVAE), a Proactive Reintegration Latent (PRL) Layer, and Temporal-Gradient Random Drift Particle Swarm Optimization (TG-RDPSO) for material reintegration based on temporal waste-stream modeling. This hybrid model is designed to inspire curiosity among researchers, as it not only predicts waste dynamics but also generates adaptive, sustainability-oriented reintegration strategies across the food value chain. The model was implemented using Python (TensorFlow and PyTorch), with experiments conducted on the Global Food Wastage Dataset (2018–2024) from Kaggle. Results reveal a 10%−14% increase in food resource recovery efficiency, 7%−9% cost reduction, and a notable decrease in carbon emissions, outperforming traditional PSO and GA models. The proposed framework also achieved an overall prediction accuracy improvement of 12% compared to baseline models. This proactive, feedback-enabled system continuously adapts to new data, providing food industry stakeholders with interpretable, decision-support oriented. The findings confirm that the proposed AI-based model is a scalable, intelligent, and human-aligned solution for achieving circularity, efficiency, and sustainability in the food industry ecosystem.