--- title: "Cortix Tech AI Assistant Briefing on DB: \\texttt{usecase-04}" author: Valmor F. de Almeida date: 30 Oct 2025 --- # Cortix Tech AI Assistant Briefing Transcript **SW Developer:** Valmor F. de Almeida, Ph.D.; 30 Oct 2025.
```text ========================================================================================== ``` ## User: Please briefly describe your role. ```text ========================================================================================== ``` ## Assistant: ### My role - I am an AI assistant for Cortix Tech. - I answer questions based on the provided Solvex-generated context (primary source: the usecase-04 knowledge table). - I focus on topics related to the Stage module and the TBP–diluent–H$_2$O–HNO$_3$–UO$_2^{2+}$–air mixing use case. - I rely primarily on the supplied context and will say when the context does not contain sufficient information. - I can list available knowledge-table titles (and their IDs) on request — the list I provide may be incomplete. ```text ========================================================================================== ``` ## User: Summarize the objectives of this usecase. ```text ========================================================================================== ``` ## Assistant: ### Objectives - Develop a use-case scenario for extraction of $\mathrm{H_2O}$, $\mathrm{HNO_3}$, and $\mathrm{UO_2^{2+}}$ by TBP with a vapor (air) phase. - Perform a parametric study of mixing (e.g., residence time / tau and inflow relative-humidity factors) to evaluate mass transfer and inflow-RH effects. - Implement and test the Cortix simulation (Stage module) that models a mixed-phase solvent-extraction contactor with aqueous, organic, and vapor components, time integration (ODE solver), reaction-mechanism coupling, and port-based connectivity. - Collect, annotate, and present results (per-stage efficiency histories), plotting time-series annotated with parameter values. - Provide a minimal framework/pseudo-code to run multiple parameter cases and archive results for comparison. - Use limited AI assistance (CortixAI) to help with explanations, analysis, and reporting. ```text ========================================================================================== ``` ## User: Provide a summary of the work performed in this usecase for an audience that is outside this knowledge base. Hence not a very quantitative summary but comprehensive at a high-level. The audience is engineering trained but may not be nuclear chemical engineers. ```text ========================================================================================== ``` ## Assistant: ### High-level summary of the work performed - Modeled a mixed-phase solvent-extraction contactor that couples an aqueous phase, an organic (TBP‑diluent) phase, and a vapor (air) phase. - Simulated transport, reaction, and phase-transfer dynamics using time integration (ODE solver) and explicit reaction‑mechanism coupling. - Studied extraction and co‑extraction of key species: $\mathrm{H_2O}$, $\mathrm{HNO_3}$, and $\mathrm{UO_2^{2+}}$, together with gas–liquid mass transfer of $\mathrm{O_2}$ and $\mathrm{N_2}$. - Represented organic‑phase chemistry including TBP hydration (TBP·$\mathrm{H_2O}$ complexes), nitric‑acid extraction (1:1 and 1:2 TBP:HNO$_3$ adducts), and uranyl nitrate neutral complex formation (e.g., $\mathrm{UO_2(NO_3)_2\cdot 2\,TBP}$). - Ran start‑up and parametric simulations (e.g., varying residence time and inflow relative humidity) and provided a minimal framework to sweep parameters, archive runs, and compare results. - Produced time‑series outputs of phase compositions and per‑stage efficiency histories for analysis and plotting. - Observed that, on the simulated timescale, the stage does not reach thermodynamic equilibrium (stage efficiencies far below 100%), indicating mass‑transfer and kinetic limitations are important. - Found substantial transfer of uranium nitrate and nitrate into the organic phase via neutral complexation, and significant TBP consumption into hydrates and extractant complexes, which affects TBP availability and selectivity. - Noted gas‑phase effects where dissolved $\mathrm{N_2}$ exceeded dissolved $\mathrm{O_2}$ primarily due to air composition and non‑equilibrium mass transfer (no aqueous O$_2$–consuming chemistry in the model). - Performed mass‑balance checks revealing large net increases in dissolved ionic species (not just conversion), consistent with feed/introduction of solutes during the simulated transient. - Implemented conditional use of AI (CortixAI) for explanatory and reporting assistance and documented the simulation code pattern for repeated parametric runs. ```text ========================================================================================== ``` ## User: Describe and show a single plot of stage efficiency in steady state at 40 C. ```text ========================================================================================== ``` ## Assistant: ### Stage efficiency — steady state at 40.0 °C - Plot summary: horizontal axis is time (units of $\tau$) from 0 to 6 and vertical axis is stage efficiency $E$ in percent, showing a smooth rise that asymptotes a little above 50%. - Key steady values: initial $E(0)\approx 15\%$ and long‑time asymptote $E_{\infty}\approx 50.5\%$. - Typical transient shape: monotonic, exponential‑like approach consistent with first‑order dynamics. - Compact model form: $E(t)\approx E_{\infty} - (E_{\infty}-E_{0}) e^{-t/\tau_{\text{eff}}}$. - Approximate model parameters: $E_{0}\approx 15\%$, $E_{\infty}\approx 50.5\%$, $\tau_{\text{eff}}\approx 0.6\,\tau$. - Timing / practical benchmarks: by $t\approx 2\,\tau$ efficiency is within ≈2% of final; by $t\gtrsim 3\,\tau$ it is within ≈1% (effectively settled). - Uncertainty behavior: very large error bars at early times (down into nonphysical negative values at the extreme lower bound), narrowing to about ±3 pp by $t\approx 2\,\tau$ and to about ±1 pp for $t\ge 3\,\tau$. - Operational takeaway: measurements or control actions taken after $\approx 2$–$3\,\tau$ will reflect the true steady performance (near 50–51%) with minimal variance. - Image availability: the plot image (img_21.png) is available and will be displayed after this response for visual reference. Image found in database 04-tbp-diluent-h2o-hno3-uo22plus-air-results-clean.md. ![ Stage Efficiency @ 40.0 C Steady-State 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```text ========================================================================================== ``` ## User: Describe and show a single plot of the concentration of species in the aqueous phase at steady-state. ```text ========================================================================================== ``` ## Assistant: ### Aqueous-phase concentrations at steady state (40.0 °C) Summary - Single-plot view: time history (0–5 min) of aqueous concentrations in g/L; tick labels use scientific-notation scalers on some panels. - Steady-state reached within a few minutes (≈3–4 min); most species show a first‑order–like approach to steady state. - Image: the steady‑state aqueous-phase plot (img_16.png) is available and will be displayed after this response. Steady-state concentrations (approximate, g/L) - $H_2O(a)$: $\approx 9.87\times10^{2}\ \mathrm{g/L}$. - $H^+(a)$: $\approx 5.0\times10^{-1}\ \mathrm{g/L}$. - $N_2(a)$: $\approx 9.1\times10^{-3}\ \mathrm{g/L}$. - $NO_3^-(a)$: $\approx 1.9\times10^{1}\ \mathrm{g/L}$. - $O_2(a)$: $\approx 5.48\times10^{-3}\ \mathrm{g/L}$. - $UO_2^{2+}(a)$: $\approx (8.5\text{–}8.8)\times10^{1}\ \mathrm{g/L}$. Key observations (high level) - Mass dominance: $UO_2^{2+}(a)$ and $NO_3^-(a)$ carry most of the added solute mass (tens of g/L), consistent with dissolution or inflow of a uranyl–nitrate–rich feed. - Acidification: rise of $H^+(a)$ to ≈0.5 g/L indicates significant acidity associated with nitrate uptake. - Gases: $N_2(a)$ and $O_2(a)$ change only slightly (mg/L scale) and equilibrate more slowly relative to ionic species. - Water behavior: bulk solvent concentration drops by a few g/L from ≈992 g/L to ≈987 g/L, reflecting solute addition; the water curve shows a transient undershoot before settling. Plot details - Horizontal axis: time (minutes). - Vertical axes: concentration in g/L (some panels use multipliers such as $\times10^{-3}$ or $\times10^{1}$ — read tick labels accordingly). - The plotted curves show monotonic increases for all solutes except $H_2O(a)$, which is briefly non‑monotonic during the early transient. Note - Values above are taken from the usecase data (run simulation outputs) and are approximate; if you want the final numeric table (time-stamped values) or a regenerated figure in a different format, I can provide that. Image found in database 04-tbp-diluent-h2o-hno3-uo22plus-air-results-clean.md. ![ Aqueous Phase Steady-State 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```text ========================================================================================== ``` ## User: Provide your own evaluation of this usecase work for an external auditor. Keep in mind sensitive information (technical and non-technical) is not included in the database. However do consult the Supporting Information sections in the database for additional data needed for this review. ```text ========================================================================================== ``` ## Assistant: ### Executive summary - The usecase implements a time‑dependent simulation of a three‑phase solvent‑extraction stage (aqueous, organic with TBP, and vapor/air) focused on extraction and co‑extraction of $\mathrm{H_2O}$, $\mathrm{HNO_3}$, and $\mathrm{UO_2^{2+}}$ and on the effect of residence‑time (tau) and inflow relative humidity on stage performance. - The work is a parametric, demonstration‑level study that produces time histories of per‑stage efficiency, aqueous and organic phase compositions, and species complexation in the organic phase; results show kinetically limited extraction on the simulated timescale. - From an audit perspective the work is well structured for an early development study, but it requires additional documentation, validation, and uncertainty quantification before being relied on for design or regulatory decisions. ### Positive aspects (strengths) - The simulation couples reaction chemistry and mass transfer across three phases and captures key organic‑phase chemistry (TBP hydration, HNO$_3$ complexation, neutral uranyl nitrate complexation). - Parametric sweep capability is demonstrated (multiple tau / RH cases archived and compared). - Outputs include time‑series with consistent sampling and per‑stage efficiency statistics (mean and standard deviation) enabling comparative analysis. - Supporting Information tables provide clear time histories and numeric summaries (e.g., stage efficiencies and standard deviations) that facilitate auditing of numerical trends. - The implementation documents mechanistic coverage (TBP hydrates, 1:1 and 1:2 HNO$_3$·TBP, UO$_2$(NO$_3$)$_2\cdot 2$TBP) and reports organic‑phase consumption/formation numbers useful for mass‑balance checks. ### Main findings (from provided data) - Stage steady efficiencies decline with increasing tau / RH: $\approx 91.8\%$ (tau=0.1), $\approx 69.3\%$ (tau=0.5), $\approx 53.2\%$ (tau=1.0), $\approx 43.7\%$ (tau=1.5). - Final standard deviations increase with tau: $\approx 2.13\%$ (tau=0.1) up to $\approx 7.76\%$ (tau=1.0), indicating larger variability at larger tau/RH. - Organic phase shows significant TBP association: free TBP declined (example: from 291.75 to $\sim$191.98 at 309.6 s) and the uranyl neutral complex UO$_2$(NO$_3$)$_2\cdot 2$TBP rose to $\sim$68.4 (same time), consistent with active extraction and TBP consumption into complexes. - On the simulated timescale the stage does not reach thermodynamic equilibrium (stage efficiencies well under 100% and time evolution still approaching a steady value), implying kinetic / mass‑transfer limitations dominate the transient. ### Limitations and gaps (what an auditor should note) - No explicit experimental validation or comparison to literature equilibrium/kinetic data is provided in the database; model fidelity relative to reality cannot be confirmed from the supplied material. - Key model inputs are not fully documented in the Supporting Information excerpt (e.g., rate constants, mass‑transfer coefficients, partition coefficients, detailed initial/boundary conditions, solver tolerances and time‑step control). - Sensitivity and uncertainty quantification appear limited to reporting mean and std. dev. of outcomes across cases; no formal propagation of input parameter uncertainties or global sensitivity analysis is shown. - Thermodynamic checks are only implicit; while mass‑balance trends are discussed, there is no explicit statement that closed mass balances were systematically verified for all cases and all phases over every run. - The database shows per‑stage single‑stage behavior; multi‑stage/countercurrent cascades are not demonstrated — relevant if the objective is process design rather than stage‑level study. - The treatment of vapor/air phase is included but gas–liquid transfer coefficients and possible effects of humidity on kinetics are not fully documented. ### Data quality and reproducibility assessment - Time grids and tabular outputs are consistent and reproducible within the provided Supporting Information; sampling is regular (17 time samples from 0 to ~247.7 s). - Numeric tables for stage efficiencies and organic‑phase snapshots are present and internally consistent (monotonic trends, plausible magnitudes). - Reproducibility would be improved if the repository included: - the full run scripts and parameter files used to generate each archived case; - versioned code and solver configuration (ODE integrator type, tolerances); - explicit lists of thermodynamic and kinetic parameters and units. - The conditional use of CortixAI for commentary is documented; this is a reporting aid and does not replace the need for deterministic code provenance. ### Technical recommendations (actionable) - Provide full model provenance: publish the simulation code, parameter files, and exact initial/boundary conditions used for each case in a versioned archive (e.g., git tag or DOI). - Add experimental or literature validation: compare steady‑state and transient results to lab single‑stage contactor data or published equilibrium/kinetic constants for TBP extraction and uranyl nitrate complexation. - Expand uncertainty quantification: perform sensitivity analysis (local and global) on key inputs (rate constants, mass‑transfer coefficients, TBP concentration, diluent properties, temperature). - Improve documentation: include solver settings, mass‑balance verification logs, and a clear description of how tau and RH are mapped to model parameters. - Consider multi‑stage simulations and longer runs to approach equilibrium where appropriate, and document when equilibrium assumptions are/are not justified. - Make plots and final numeric tables downloadable and machine readable (CSV/JSON) for independent review. - Record and report computational costs and runtimes for each parametric case to assist scaling and operational planning. ### Non‑technical recommendations (auditing practice) - Maintain an audit trail linking each reported figure/table to the exact simulation input and code version. - Provide a plain‑language summary of assumptions and limitations for non‑specialist reviewers. - Ensure separation of sensitive content from simulation artifacts; the current database appears not to include sensitive material, but continue to follow institutional data governance policies. ### Overall audit conclusion - The usecase demonstrates a sound early‑stage simulation capability for TBP‑based extraction with three‑phase coupling and presents coherent parametric results that reveal clear trends (efficiency vs. tau/RH and organic complexation). - For engineering decisions, regulatory assessment, or scale‑up the study requires additional validation, tighter documentation of inputs/solvers, and formal uncertainty/sensitivity analysis. - Recommendation to the auditee: treat the present results as informative and hypothesis‑generating rather than definitive; follow the technical recommendations above to elevate the work to audit‑ready status. If you would like, I can - itemize the specific Supporting Information tables and figures I consulted (IDs and brief description), or - prepare a prioritized checklist you can give to the development team to address the recommendations. ## AI Briefing Control Parameters + **Database:** CT_Solvex_db + **Database table:** usecase-04 + **RAG # records:** 10 + **RAG search:** vector + **Memory # questions:** 10 + **LLM:** gpt-5-mini + **LLM cleverness:** 1.0 + **Total # tokens:** 46156 + **Discussion history size:** 12 + **Embedded images:** 2