Traceability and Performance Optimization: Application of Generative AI, Digital Twin, and DRL in the Recycling Process of WEEE

The lack of transparency, unified standards, and effective regulation, along with the complexity of the supply chain, make it challenging to achieve reliable traceability throughout the entire process of recycling and reusing waste electronic appliances. This poses a challenge for effectively implementing carbon reduction measures. In response to the above issues, we propose a full process data management solution for WEEE recycling based on blockchain technology. In addition, a method combining digital twin and generative AI technology has been proposed to address the performance bottleneck issue of blockchain. Predicting future data flow through generative AI models and utilizing reinforcement learning algorithms to predictively optimize blockchain parameter configurations effectively improve blockchain performance and scalability. The experimental results demonstrate that the proposed method effectively enhances system adaptability and throughput. It achieves an integration of reliable traceability, accurate prediction, and performance optimization throughout the entire process of WEEE recycling and reuse data management.