
Over a two-month period, contributed to the dpploy/engy-4390 repository by developing and documenting simulation notebooks for uranium milling processes, including the White Mesa plant and ammonium diuranate drying and calcination. Leveraged Python, Jupyter Notebooks, and the Cortix framework to build reproducible, parameterized simulations supporting scenario analysis and data traceability. Enhanced technical documentation with improved structure, objectives, and visual assets, facilitating knowledge transfer and process understanding. Addressed a critical bug by correcting reaction mechanism details, ensuring simulation accuracy. The work demonstrated depth in chemical engineering modeling, scientific computing, and technical writing, resulting in robust, auditable workflows for process simulation.
December 2024 monthly summary for dpploy/engy-4390 focusing on modeling tooling and documentation for the drying and calcination of ammonium diuranate, plus targeted documentation enhancements and a critical bug fix. Delivered on-track improvements to process understanding, reproducibility, and knowledge transfer while strengthening the reliability of the simulation workflow.
December 2024 monthly summary for dpploy/engy-4390 focusing on modeling tooling and documentation for the drying and calcination of ammonium diuranate, plus targeted documentation enhancements and a critical bug fix. Delivered on-track improvements to process understanding, reproducibility, and knowledge transfer while strengthening the reliability of the simulation workflow.
Month: 2024-11 — Delivered a reproducible, Cortix-integrated White Mesa plant simulation notebook enabling end-to-end setup and execution. No major bugs reported this month. This work accelerates experimentation, supports scenario analysis, and improves data traceability for decision-making.
Month: 2024-11 — Delivered a reproducible, Cortix-integrated White Mesa plant simulation notebook enabling end-to-end setup and execution. No major bugs reported this month. This work accelerates experimentation, supports scenario analysis, and improves data traceability for decision-making.

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