Integrating Hydrometallurgical Electronic Waste Recycling with Cloud-Native Resilient Systems: A Multi-Dimensional Framework for Sustainable Resource Recovery and Digital Reliability

Abstract

The rapid proliferation of electronic devices has led to an unprecedented increase in electronic waste (e-waste), posing significant environmental, economic, and technological challenges. Hydrometallurgical processes have emerged as a promising solution for recovering valuable metals from e-waste, particularly waste printed circuit boards (WPCBs), due to their efficiency and selectivity. However, these processes are associated with environmental risks, operational complexities, and scalability challenges. Concurrently, advancements in cloud computing, artificial intelligence, and site reliability engineering (SRE) have introduced new paradigms for managing complex industrial systems with enhanced resilience and automation. This study proposes an integrative framework that combines hydrometallurgical e-waste recycling with cloud-native infrastructure, aiming to optimize resource recovery while ensuring system reliability, scalability, and environmental sustainability. The research synthesizes insights from life cycle assessment (LCA), metallurgical recovery techniques, polymer separation technologies, and cloud-based system architectures. A hybrid methodological approach is employed, incorporating theoretical modeling, literature synthesis, and cross-domain conceptual integration. The findings reveal that while hydrometallurgical processes offer high recovery efficiency, their environmental impacts and operational uncertainties necessitate advanced monitoring and control mechanisms. The integration of AI-driven self-healing systems, predictive analytics, and cloud automation significantly enhances process reliability and reduces downtime. Furthermore, the study highlights the importance of cybersecurity and supply chain resilience in the digitalization of recycling systems. The proposed framework contributes to the development of sustainable, intelligent recycling infrastructures aligned with Industry 5.0 principles. Future research directions include real-time adaptive control systems, decentralized recycling networks, and the incorporation of circular economy metrics into digital optimization platforms.