인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록·키워드
The rapid expansion of electric vehicles (EVs) and energy storage systems has resulted in a dramatic increase in end-of-life (EoL) lithium-ion batteries (LIBs), creating both critical resource recovery opportunities and environmental challenges. Conventional mechanical, pyro-, and hydrometallurgical recycling processes are often limited by high energy consumption, safety risks, and inefficient material recovery. This study presents an integrated framework for AI-powered LIB recycling, combining multi-sensor data acquisition, deep learning–based process optimization, and digital-twin simulation for sustainable operation. Experimental validation demonstrates that AI-assisted systems achieve up to 98% hazard prediction accuracy, 45% faster process throughput, and 27% lower energy use compared with rule-based control. Life-cycle assessment (LCA) results reveal a 21.3% reduction in carbon emissions (from 3.62 to 2.85 kg CO₂/kg recovered material) and a 19% decrease in total energy demand, primarily due to adaptive process control and reagent optimization. Economic analyses further show a 31% improvement in return on investment (ROI) and a shortened payback period. Explainable AI (SHAP/LIME) analyses identified temperature deviation (ΔT) and gas emission rate (G) as the dominant predictors of recycling risk, providing transparent and interpretable decision support. Despite challenges in data standardization, model transferability, and cybersecurity, emerging approaches—such as federated learning and physics-informed neural networks—are paving the way for next-generation self-evolving smart recycling ecosystems. This work establishes a foundation for scalable, low-carbon, and AI-driven circular battery economies aligned with global sustainability goals.
본문·목차
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