--- title: "Cortix Tech AI Assistant Briefing on DB: \\texttt{usecase-02}" author: - \textsc{Cortix Tech} - \texttt{https://cortix.tech} date: 15 Nov 2025 --- # Cortix Tech AI Assistant Briefing Transcript **SW Developer:** Valmor F. de Almeida, Ph.D.; 15 Nov 2025.
```text ========================================================================================== ``` ## User: 1. Please briefly describe your role. ```text ========================================================================================== ``` ## Assistant: ### Role - I am an AI assistant for Cortix Tech that answers questions about Solvex‑generated data, primarily using the usecase-02 knowledge base. - I produce and explain reports (for example aqueous and organic phase analyses), interpret stage/system behavior, and help analyze simulation outputs provided in the context. - I work only from the supplied context; if information is missing or uncertain I will say so and request or iterate on the available data. ```text ========================================================================================== ``` ## User: 2. Summarize the objectives of this usecase. ```text ========================================================================================== ``` ## Assistant: Objectives of Use-Case 02 (and 02.1): - Develop a use‑case scenario for water extraction by TBP (tributyl phosphate) including a vapor phase and staged solvent‑extraction system. - Perform and test simulations (including a parametric study of mass transfer and inflow relative humidity) to evaluate stage behavior and mixing. - Test the implementation, run fixed‑parameter and parameter‑variation simulations, and present results and figures. - Use AI assistants to help with information extraction, explanation, reporting, and iterative refinement of results. - Provide analysis and recommendations (e.g., on aqueous‑phase dissolved gases, stage efficiency, and model improvements) based on the simulation outputs. ```text ========================================================================================== ``` ## User: 3. 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 This usecase examined a staged, three‑phase mixing/extraction scenario in which an organic solvent (tributyl phosphate, TBP) contacts an aqueous stream and a vapor phase. The activity combined data extraction from the Stage Knowledge Base with targeted analyses to understand which species are present, what reactions or transfer processes dominate, and how far the system progresses toward equilibrium under the simulated conditions. - Data and scope: extracted time‑series, stage efficiency histories, and mass‑balance residuals for the organic, aqueous, and vapor phase entries in the stage KB and used those data as the sole source for the analyses. - Phase‑level analyses: produced separate, focused reports for the organic phase (TBP and TBP–H₂O complexes), the aqueous phase (water with dissolved N₂ and O₂ and its coupling to organic extraction), and the vapor phase (H₂O, N₂, O₂ with reversible vapor–liquid exchange). - Mechanistic interpretation: determined that organic‑phase chemistry is dominated by reversible complexation (TBP hydrates that extract water from the aqueous phase), while vapor and aqueous exchanges are reversible and controlled by non‑equilibrium transfer rather than full thermodynamic equilibration. - Stage performance and mass conservation: verified that mass‑balance residuals are effectively zero (numerically consistent), and showed the stage did not reach chemical equilibrium during the simulated interval — stage efficiency remained well below the ~100% value that would indicate equilibrium. - Key practical finding for gases: explained why simulated aqueous O₂ concentrations are lower than N₂ despite higher O₂ solubility — the result of gas‑phase composition and incomplete mass transfer (non‑equilibrium stage behavior), not oxy­gen consumption in the chemistry set. - Parametric/implementation work: set up a parameter‑study usecase to probe vapor‑phase effects and to test the simulation/analysis pipeline (e.g., sensitivity to vapor composition, residence time, or mass‑transfer parameters). - Deliverables and recommendations: delivered the three phase reports and recommended next steps for greater predictive power — record concentration units in the KB, add kinetic or equilibrium data for the TBP–H₂O reactions, and include explicit stage‑level mass‑transfer or hydraulic details if the goal is to approach or design for near‑equilibrium operation. Overall, the work clarified the dominant mechanisms (TBP hydration/extraction and reversible vapor–liquid exchange), established that observed behavior is transport‑limited under the simulated conditions, and identified the key missing data and model components needed to move from diagnostic analysis to predictive or design calculations. ```text ========================================================================================== ``` ## User: 4. 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 A concise description and the available plot for the steady‑state stage efficiency at 40.0 °C. - What is plotted - Horizontal axis: Time in units of the characteristic time constant, $ \tau $. - Vertical axis: Stage efficiency, $E$, in percent. - Main trend - Efficiency rises from about 23% at $t\approx0$ to a steady level near 51%, increasing rapidly at first and then tapering off in a first‑order‑like approach to steady state. - Representative waypoints - $t\approx0.3\,\tau$: $E\approx33\%$ - $t\approx0.6\,\tau$: $E\approx42$–$43\%$ - $t\approx1.0\,\tau$: $E\approx48$–$49\%$ - $t\approx1.5\,\tau$: $E\approx50\%$ - $t\ge3\,\tau$: $E\approx50.8$–$51.2\%$ (negligible drift) - Uncertainty - Early times show large, asymmetric error bars with a wide lower bound (down toward ~0–10%). - After about $t\approx1\,\tau$ the error bars narrow; for $t\gtrsim1$–$2\,\tau$ the spread is roughly $\pm 1$ to $\pm 1.5$ percentage points around $\sim$51%. - Key quantitative read‑offs - Initial efficiency: $E_0\approx23\%$. - Steady efficiency: $E_\infty\approx51\%$. - Time to 90% of rise ($E\approx48.2\%$): $\approx 1.0\,\tau$. - Time to 95% of rise ($E\approx49.6\%$): $\approx 1.2$–$1.3\,\tau$. - Time to $\sim$98% ($E\approx50.4\%$): $\approx 3\,\tau$. - Practical implication - The stage stabilizes quickly; allow about $2$–$3\,\tau$ to be safely within $\pm 1$–$1.5$ percentage points of steady state for operations or data collection. An image of the plot (img_21.png) is available and will be displayed after this message by the chat program. Caption: "Stage Efficiency @ 40.0 °C — steady‑state approach of $E(t)$ from ~23% to ~51% with uncertainty bars; time in units of $\tau$." If you want, I can also provide a numerical reconstruction of the mean curve (e.g., fit a first‑order exponential and return parameters), or export the plot data if needed. Image found in database 02-tbp-diluent-h2o-air-results-clean.md. ![ Stage Efficiency @ 40.0 C Steady-State 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```text ========================================================================================== ``` ## User: 5. Describe and show a single plot of the concentration of species in the aqueous phase at steady-state. ```text ========================================================================================== ``` ## Assistant: ### Aqueous phase concentrations — steady state (40.0 °C) A concise description of the aqueous‑phase steady‑state plot and the key steady values observed in the simulation. - What is plotted - Horizontal axis: time (0–5 min). - Vertical axes: species concentrations in g·L⁻¹ with axis scale multipliers: H₂O(a) shown with ×10², N₂(a) and O₂(a) shown with ×10⁻³. - Series: single labeled series "Aqueous Phase" plotted with square markers and smooth connecting lines. - Time behavior (qualitative) - H₂O(a): rapid small decrease during the first minute, then a near‑constant plateau. - N₂(a): monotonic rise, fastest in the first 1–2 minutes, then asymptotically approaching a plateau by ~4–5 min. - O₂(a): smooth increase similar to N₂ but with a slightly slower early rise and smaller absolute change. - Steady‑state snapshot (approximate values at 4–5 min) - H₂O(a) ≈ 986.075 g·L⁻¹ (i.e., ≈ $9.86\times10^{2}$ g·L⁻¹). - N₂(a) ≈ 9.0586×10⁻³ g·L⁻¹. - O₂(a) ≈ 5.4816×10⁻³ g·L⁻¹. - Timescale to practical steady state - H₂O(a): ≈ 1–1.5 minutes. - N₂(a) and O₂(a): ≈ 3–5 minutes to reach their plateaus. - Magnitude and interpretation - Water shows an absolute decrease of ~6 g·L⁻¹ (≈0.6% of its initial value), consistent with slight dilution/displacement as gases dissolve. - Gaseous species change by ~10⁻⁴–10⁻³ g·L⁻¹ (small absolute amounts), reflecting dissolution into the aqueous phase with saturation approached by 4–5 minutes. - Overall behavior matches classic approach‑to‑equilibrium dynamics: rapid initial change followed by diminishing rates as steady state is reached. - Image - The plot image (img_16.png) is available and will be displayed after this message by the chat program. Caption: "Aqueous Phase Steady‑State — H₂O(a), N₂(a), O₂(a) vs time (0–5 min); water axis ×10², gas axes ×10⁻³." Image found in database 02-tbp-diluent-h2o-air-results-clean.md. ![ Aqueous Phase Steady-State 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```text ========================================================================================== ``` ## User: 6. 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: ### External-auditor evaluation — Use‑Case 02 (TBP‑Diluent‑H₂O‑Air Mixing) - Scope and objectives - The work addresses a staged three‑phase mixing/extraction scenario (aqueous, organic (TBP), vapor) and includes a parametric study variant; objectives and high‑level deliverables are clearly stated in the KB and supporting files. - Data provenance and traceability - Source files referenced: 02-tbp-diluent-h2o-air-results-clean.md and 02-tbp-diluent-h2o-air-param-study-clean.md. - Stage Knowledge Base (Stg‑1) entries are cited for organic and aqueous phases (source keys: stg.organic_phase and stg.aqueous_phase). - Mass‑balance residuals and stage efficiency time histories are provided in the KB; the residuals are reported as effectively zero (numerical consistency). - I consulted the KB entries and the supporting report text included in the provided context. If additional supporting files exist in the database, they were not included in the supplied context. - Strengths - Clear problem definition and logical decomposition into phase‑specific analyses (organic, aqueous, vapor). - Mass conservation checks are present (stage mass‑balance residuals ≈ 0) — a strong internal consistency indicator. - Stage efficiency time histories and uncertainty (mean ± std) are reported, enabling quantitative assessment of approach‑to‑steady‑state. - Mechanistic interpretation (TBP hydration/extraction reactions) is present for the organic phase. - Limitations and missing information (impacting auditability) - Concentration units for species time series are not recorded in the KB (explicitly noted). This prevents unambiguous quantitative calculations, conversions, and independent verification. - No explicit equilibrium constants, partition coefficients, or forward/reverse rate constants for the TBP–H₂O reactions are available in the KB. This prevents thermodynamic or kinetic validation beyond qualitative interpretation. - Gas‑phase partial pressures (or vapor composition) and Henry‑law constants are not provided; these are needed to verify dissolved‑gas equilibrium and to interpret aqueous N₂/O₂ behavior quantitatively. - Stage‑level hydrodynamic / mass‑transfer parameters (interfacial area, kLa, mixing intensity) are not provided; stage efficiency is reported but its mechanistic origin (kinetic vs transport limitation) cannot be independently assessed without these parameters. - Solver/input provenance is incomplete in the supplied context (simulation code/version, initial/boundary conditions files, random seeds, numerical tolerances). This limits full reproducibility. - Supporting data files (raw time‑series CSVs, scripts used for plotting/analysis) are not included in the provided context. - Data quality and uncertainty - Numerical mass‑balance closure is good (residuals near machine precision). - Stage efficiency uncertainties are reported, which is valuable. - However, absence of concentration units and thermodynamic/kinetic constants means uncertainty quantification cannot be propagated to physically meaningful metrics (e.g., mole fractions, partition coefficients). - Technical risks and potential misinterpretations - Reporting aqueous O₂ lower than N₂ may be misread as chemical consumption; the KB notes this is likely due to vapor composition and non‑equilibrium transfer. Without gas partial pressures or reaction steps explicitly excluding O₂ consumption, an auditor should verify that no aqueous reactions consume O₂. - Any downstream design or scale‑up based on reported efficiencies would be risky without stage hydrodynamics and mass‑transfer parameters. - Recommendations for remediation / next steps (for a conclusive audit) - Add explicit concentration units for all time series in the KB. - Provide or link to: equilibrium constants/partition coefficients and forward/reverse rate constants for TBP–H₂O reactions, and Henry‑law constants or gas‑phase partial pressures used. - Include stage hydrodynamic/mass‑transfer parameters (interfacial area, kLa or mass‑transfer coefficients, mixing/residence time definitions). - Supply simulation inputs and environment provenance: solver name/version, input decks, initial/boundary conditions, numerical tolerances, and any random seeds. - Attach raw data files (CSV or equivalent) and plotting/analysis scripts to enable independent re‑plotting and re‑analysis. - If practical validation exists, include experimental data or benchmark cases and compare model outputs vs measurements. - Perform sensitivity analyses (units‑corrected) on key parameters (kLa, partition coefficients, stage efficiency) and provide results. - Suggested minimal audit checklist (items to request) - Concentration units for all species time series. - Reaction/thermodynamic parameter list (constants and units). - Gas‑phase composition or partial pressures and Henry constants. - Stage hydrodynamic parameters and definitions of "stage efficiency". - Simulation input files, solver/version, and raw output time‑series. - Plots/data with uncertainty propagation methodology described. - Overall assessment (auditor summary) - The work is methodical and internally consistent where data exist (mass balance closure, efficiency histories, mechanistic descriptions). It provides a solid conceptual and diagnostic analysis of TBP‑water extraction with vapor exchange. - However, for an external audit that must verify quantitative claims, reproduce simulations, or support design decisions, the KB as supplied is incomplete: key metadata (units), thermodynamic/kinetic parameters, gas‑phase data, and simulation provenance are missing. Addressing those gaps will convert a strong diagnostic report into an auditable, reproducible engineering deliverable. - Final note on sensitive information - Based on the supplied context, no sensitive technical or non‑technical information was observed. If any supporting files exist elsewhere in the DB that could be sensitive, please flag them and exclude from public audit distribution. ## AI Briefing Control Parameters + **Database:** CT_Solvex_db + **Database table:** usecase-02 + **RAG # records:** 10 + **RAG search:** vector + **Memory # questions:** 10 + **LLM:** gpt-5-mini + **LLM cleverness:** 1.0 + **Total # tokens:** 43261 + **Discussion history size:** 12 + **Embedded images:** 2