Optimizing Circular Economy Models Using Machine Learning for Sustainable Production

Abstract

The transition to a circular economy requires innovative technological solutions to minimize waste, maximize resource efficiency, and promote recycling. This paper investigates the application of machine learning in optimizing circular economy strategies, including predictive maintenance, automated recycling, and material lifecycle analysis. We explore reinforcement learning for dynamic waste management, computer vision for sorting recyclable materials, and generative models for sustainable product design. The findings demonstrate how AI-driven circular economy models can reduce environmental impact, improve resource efficiency, and support sustainable industrial transformation.