Optimization of the passive recovery of uranium from seawater
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The aim of this thesis is to optimize the design and deployment conditions utilized by a technology for passively collecting uranium from seawater that is currently under development by Oak Ridge and Pacific Northwest National Labs along with University partners. This system involves the production, deployment, and recycle of an amidoxime ligand grafted onto a high density polyethylene based adsorbent. While many adsorbent performance characteristics and cost inputs impact the final uranium production cost, the system and design parameters explored here include: degree of ligand grafting, number of adsorbent uses prior to ultimate disposal, length of immersion in the sea, and ocean temperature. Given the complicated empirically-driven nature of the cost calculation, the cost calculation tool is treated as a black box model, thus the minimization requires a derivative free optimization method. A literature review is conducted to explore applicable algorithms and the Nelder-Mead Simplex Method is ultimately selected. A base case is created using historical values to serve as an initial condition for optimization. From this case, the uranium production cost is minimized, resulting in an 11% decrease. From there, sensitivity cases are considered. An alternative elution process for recovering uranium from the adsorbent is studied. If this innovation can be realized, significant cost savings are shown to be attained if this process fulfills its promise of mitigating adsorbent degradation. Next, the effects of marine bacterial growth on cost are explored. It is determined that optimizing the deployment conditions and improving the uranium binding kinetics can mitigate this increase. Sensitivity analyses are conducted in order to provide insight as to how the optimal deployment conditions are determined. The results presented in this thesis can inform the direction of future research. Furthermore, as the technology continues to evolve, the methodology developed for this optimization will remain relevant and the optimization too can continue to be used to guide design and R&D decisions.