CAREER: An Uncertainty-Resilient Framework for Net-Zero Manufacturing Systems

Engineers use life-cycle assessments to analyze the total impacts and costs of manufacturing facilities over their lifetimes. These assessments are challenging because manufacturing facilities experience changes in the resources they consume, operating conditions, and costs. Facilities also are susceptible to unexpected disruptions such as power outages, floods, or public health crises. This CAREER project will develop a reliable and adaptable way to evaluate the environmental sustainability of manufacturing systems. It will combine real-time operational data with advanced analytical tools that can respond to changing conditions. The outcome of this project will inform manufacturers, so that they can design environmentally sustainable systems. The project will help train engineering students in the assessment of environmental impacts. The project will offer a “Rising Innovators” engineering camp for elementary and middle school students in the Tampa Bay region. The project will promote environmental literacy and create pathways into engineering. It will also help equip the next generation of sustainability and manufacturing engineers with tools to establish resilient manufacturing systems. The goal of this project is to improve the reliability of environmental sustainability assessments and support adaptive decision-making in net-zero manufacturing systems. The project will integrate real-time process data and probabilistic modeling via Bayesian inference. The research objectives are to 1) establish an uncertainty-resilient framework for life cycle environmental and economic assessments, 2) understand the limitations of achieving net-zero energy in discrete manufacturing systems, and 3) train the next generation of sustainability engineers. The uncertainty-resilient framework will be tested on a pultrusion-based recycling system that produces filament for additive manufacturing from post-consumer plastics. The framework will include production monitoring, life cycle assessment, and probabilistic modeling. This research will generate new insights into the effect of system-level variability on environmental trade-offs and production resilience. It will also dynamically reduce uncertainty and support decision-making under evolving conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2543472 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Nancy Diaz-Elsayed | Institution: University of South Florida, TAMPA, FL | Award Amount: $499,975 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543472 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543472.html