Rising electronics waste requires intelligent systems to handle complex mixes of materials and toxins. Emerging techniques like computer vision and machine learning algorithms powered by sensor networks allow real-time waste characterization across factory floors for automated handling. Cloud analytics reveal patterns and simulate operational changes to minimize recurring e-waste through informed segregation and closed-loop flows. Overall, the AI solution establishes a digital thread connecting equipment data and waste streams to drive sustainability via predictive analytics, tailored process upgrades and establishing circular material reentry. It exemplifies sophisticated approaches beyond manual compliance to enable scalable, robust e-waste management across electronics sectors.

Кольпикова В.О. (науч. рук. Сергиенко О.И.) Leveraging ai for optimized waste management at electronics industry // Сборник тезисов докладов конгресса молодых ученых. Электронное издание. – СПб: Университет ИТМО, [2024]. URL: https://kmu.itmo.ru/digests/article/13066