WENDY. LAB develops agentic AI systems that make civil and environmental infrastructure carbon‑intelligent: AI agents that perceive complex waste management systems, reason with rigorous life‑cycle and techno‑economic models, act in real facilities, and continuously learn from feedback.
A state-of-the-art LCA for a municipal landfill was developed to represent how a modern landfill is constructed, operated, closed, and monitored after closure, in consideration of landfill size, engineering design, waste composition, and gas collection and control regulations and practices.
This study addressed the dynamic nature of time-varying landfill emissions and how this temporal effect affects the estimates of global warming impacts was investigated. The dynamic and static global warming potential (GWP) estimates were compared using 100-yr and 20-yr time horizons. Different choices for static GWP values from IPCC's 4th and 5th analysis reports were considered to evaluate if they are a significant uncertainty source compared to the switch between static and dynamic GWP accounting methods.
A framework for streamlining data- and time-intensive LCA was developed to identify what are the most critical impacts, processes, flows, and inputs that affect the decision accuracy and consistency.
This recently funded project aims to comprehensively assess the economic and environmental performance of new technologies for chemical recycling of plastics using life-cycle assessment (LCA).