Keywords

constrained optimization, machine learning, nuclear waste, uncertainty propagation, vitrification

Abstract

The vitrification of high-level waste (HLW) by heating a mixture of glass-forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first-order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.

Original Publication Citation

Gunnell L, Lu X, Vienna JD, Kim D-S, Riley BJ, Hedengren J. Uncertainty propagation and sensitivity analysis for constrained optimization of nuclear waste vitrification. J Am Ceram Soc. 2025; 108:e20446. https://doi.org/10.1111/jace.20446

Document Type

Peer-Reviewed Article

Publication Date

2025-03-05

Publisher

Journal of the American Ceramic Society

Language

English

College

Ira A. Fulton College of Engineering

Department

Chemical Engineering

University Standing at Time of Publication

Full Professor

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