---
title: "Cortix Tech AI Assistant Briefing on DB: \\texttt{usecase-03}"
author: 
  - \textsc{Cortix Tech} 
  - \texttt{https://cortix.tech} 
date: 18 Nov 2025
---
# Cortix Tech AI Assistant Briefing Transcript
**SW Developer:** Valmor F. de Almeida, Ph.D.; 18 Nov 2025. <br>

```text
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```

## User: 1. Please briefly describe your role.

```text
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```

## Assistant: 

### My role

I am a Cortix Tech AI Assistant that answers questions about Solvex-generated data (the knowledge database table is named usecase-03).

- Provide concise, context-grounded answers about the Use-Case 03: TBP-Diluent-H₂O-HNO₃-Air Mixing dataset, code snippets, and AI reports.
- Reference and analyze entries from the usecase-03 table; if you ask, I can itemize available titles and IDs (the list may be incomplete).
- If the context lacks the needed information, I will say so and re-check the provided context before responding.

```text
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```

## User: 2. Summarize the objectives of this usecase.

```text
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```

## Assistant: 

### Objectives (summary)

- Develop a use-case scenario for extraction of water and nitric acid by TBP in a system that includes a vapor phase (TBP–diluent–H₂O–HNO₃–air mixing).
- Implement and test the simulation/runtime for that use case and present the results.
- Use AI assistants to help generate information, explanations, and reports for the use case.
- Support iterative AI interactions: some AI requests may need to be run multiple times until results are satisfactory or corrected.

```text
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```

## 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
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## Assistant: 

### High-level summary of work performed

- Overview: Simulated and analyzed a three-phase mixing/extraction scenario (organic phase containing tributyl phosphate, aqueous phase containing water and nitric acid, and a vapor phase of air) to study time-dependent transfer and chemical interactions among phases.  

- System characterization: Compiled species metadata (molar masses, phase assignments), a reaction mechanism that includes TBP complexes with water and HNO₃, time-series concentrations, and stage-module information (stage efficiency and mass-balance residuals).  

- Data scope and timing: Focused on discrete time points up to 309.62 s (0.0, 20.64, 41.28, 103.21, 206.42, 309.62 s); aqueous-phase concentrations are reported in g/L in the knowledge base.  

- Analyses performed: Produced separate phase-level reports (aqueous, organic, vapor) that (a) examine species behavior and reactions involving each phase, (b) track time-dependent concentrations, (c) evaluate stage efficiency history as a proxy for how close the system is to equilibrium, and (d) check stage mass-balance residuals for consistency.  

- Key findings (non-quantitative): Organic-phase chemistry is dominated by TBP forming hydrated and HNO₃-complexed species; stage efficiencies through the observation window remain well below the ~100% level needed for full chemical equilibrium; mass-balance residuals are essentially zero, indicating internally consistent bookkeeping in the simulation/data.  

- Practical implications: With stage efficiency significantly below equilibrium, observed concentrations likely reflect kinetically-limited mass transfer and incomplete partitioning, not final equilibrium compositions; TBP complexation alters solute partitioning behavior and is important for extraction performance.  

- Limitations identified: The observation window is short for reaching equilibrium under the given conditions; the KB lacks explicit equilibrium constants and kinetic rate constants and detailed stage-module transport parameters needed for full mechanistic or equilibrium modeling.  

- Recommendations from the work: Extend the monitoring time and/or increase contact/mixing to approach equilibrium, and provide equilibrium constants, rate constants, and stage-module details (transport resistances, interfacial area, mixing rates) to enable mechanistic or equilibrium predictions beyond the current descriptive analysis.  

- Provenance and authorship: All analyses were drawn from the Stage Knowledge Base (stg.aqueous_phase, stg.organic_phase, stg.vapor_phase) and summarized in phase-level AI reports; the developer listed in the KB is Valmor F. de Almeida (Cortix Tech).

```text
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```

## User: 4. Describe and show a single plot of stage efficiency in steady state at 40 C.

```text
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```

## Assistant: 

### Stage Efficiency @ 40.0 °C — steady‑state: description and plot

- Summary: The stage efficiency starts near 16.9% at t̃ = 0, increases rapidly at first and then slows, and asymptotically approaches about 50–51% by t̃ ≈ 3. Practical steady‑state (changes < ~1% and small uncertainty) is reached around t̃ ≈ 3–4.

- Key waypoints (approximate): E(t̃=0) ≈ 16.9%; E(t̃≈0.5) ≈ 30%; E(t̃≈1) ≈ 42–43%; E(t̃≈2) ≈ 48–49%; E(t̃≈3) ≈ 50%; final ≈ 50.4–50.5%.

- Uncertainty behavior: Large standard deviations at early times (error bars even dip slightly below 0% at t̃≈0), then steadily shrink. By the end of the recorded history (t̃ ≳ 3) std dev is ~1.4–1.8 percentage points.

- Interpretation: The response is well approximated by a first‑order exponential approach
  $E(\tilde{t}) = E_\infty - (E_\infty - E_0)\,e^{-\tilde{t}/\tau}$,
  with E₀ ≈ 17% and E∞ ≈ 50–51%. For operational decisions, measurements after t̃ ≥ 3 are both near final value and low uncertainty.

- Reproducible plot (Python/matplotlib) using the reported mean ± std points; an image file img_21.png is available and will be displayed after this response by the chat program.

  ```python
  import matplotlib.pyplot as plt
  import numpy as np

  # Data points extracted from the steady-state table (t̃, mean E [%], std [%])
  t = np.array([0.00, 0.06, 0.11, 0.17, 0.23, 0.29, 0.34, 0.40, 0.46, 0.52,
                2.58, 2.87, 3.15, 3.44, 3.73, 4.01, 4.30, 4.59, 4.87, 5.16])
  E_mean = np.array([16.875, 20.172, 23.258, 26.106, 28.693, 31.001, 33.063, 34.884, 36.484, 37.888,
                     49.669, 49.915, 50.092, 50.218, 50.309, 50.374, 50.420, 50.454, 50.478, 50.495])
  E_std  = np.array([22.212, 20.127, 18.305, 16.757, 15.478, 14.440, 13.593, 12.895, 12.303, 11.783,
                     2.304,  1.982,  1.776,  1.646,  1.565,  1.515,  1.484,  1.464,  1.451,  1.442])

  plt.figure(figsize=(8,3))
  plt.errorbar(t, E_mean, yerr=E_std, fmt='-o', color='C0', ecolor='gray',
               elinewidth=1, capsize=3, markersize=4)
  plt.xlabel('Time [\\~t]')
  plt.ylabel('Stage efficiency E [%]')
  plt.title('Stage Efficiency @ 40.0 °C — steady state')
  plt.grid(alpha=0.25)
  plt.xlim(-0.1, max(t)+0.4)
  plt.ylim(0, 60)
  plt.axhline(50, color='C3', linestyle='--', linewidth=0.8, label='~50%')
  plt.legend(loc='lower right')
  plt.tight_layout()
  plt.show()
  ```

- Image availability: The original figure file is img_21.png (referenced in the knowledge base). The chat program will display that image after this message so you can view the original plotted figure with the same data and error bars.

 Image found in database 03-tbp-diluent-h2o-hno3-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 @ 40.0 °C — steady state (brief description + plot)

Five dissolved species in the aqueous phase approach steady values by ≈4–5 minutes.  At steady state their relative magnitudes span orders of magnitude (water >> nitrate > proton >> dissolved N₂, O₂).  Ionic species (H⁺, NO₃⁻) reach most of their change within the first 1–2 minutes; gases (N₂, O₂) relax more slowly and remain at mg·L⁻¹ (10⁻³ g·L⁻¹) levels.

- Key steady-state concentrations (final recorded at t = 309.624 s):
  - H₂O(a): 986.207 g·L⁻¹
  - NO₃⁻(a): 29.0439 g·L⁻¹
  - H⁺(a): 0.47187 g·L⁻¹
  - N₂(a): 0.00905861 g·L⁻¹
  - O₂(a): 0.00548158 g·L⁻¹

- Interpretation:
  - Water remains the dominant component (≈10² g·L⁻¹).
  - Nitrate is the major dissolved solute (≈10¹ g·L⁻¹).
  - Proton concentration is at 10⁻¹ g·L⁻¹ (acidification).
  - Dissolved gases are trace (≈10⁻³ g·L⁻¹) and change little in absolute mass.

- Plot (single figure): time series to steady state (time in minutes).  The code below reproduces the plot shown in the knowledge base (the original image file is img_16.png and will be displayed by the chat program after this response).

```python
import numpy as np
import matplotlib.pyplot as plt

# Time (s) and concentrations (g/L) extracted from the Use-Case 03 steady-state table
t_s = np.array([0.0, 3.44027, 6.88054, 10.3208, 13.7611, 17.2013, 20.6416, 24.0819,
                27.5221, 30.9624, 34.4027, 37.843, 41.2832, 44.7235, 48.1638,
                51.604, 68.8054, 86.0067, 103.208, 120.409, 137.611, 154.812,
                172.013, 189.215, 206.416, 223.617, 240.819, 258.02, 275.221,
                292.423, 309.624])
t_min = t_s / 60.0

H2O = np.array([992.0, 990.807, 989.861, 989.109, 988.51, 988.031, 987.646, 987.338,
                987.09, 986.892, 986.733, 986.606, 986.504, 986.423, 986.358,
                986.307, 986.177, 986.147, 986.149, 986.159, 986.17, 986.18,
                986.187, 986.193, 986.197, 986.2, 986.202, 986.204, 986.205,
                986.206, 986.207])

H_plus = np.array([0.00100739, 0.0334101, 0.0636535, 0.0918464, 0.118107, 0.142555,
                   0.165308, 0.186476, 0.206168, 0.224483, 0.241517, 0.257359,
                   0.272092, 0.285794, 0.298538, 0.310391, 0.35837, 0.391903,
                   0.415441, 0.432034, 0.443777, 0.452114, 0.458047, 0.462278,
                   0.465299, 0.467458, 0.469002, 0.470107, 0.470898, 0.471465,
                   0.47187])

N2 = np.array([0.00815443, 0.00815849, 0.0081696, 0.00818633, 0.00820745, 0.00823196,
               0.008259, 0.00828785, 0.00831792, 0.00834873, 0.00837985, 0.00841097,
               0.00844182, 0.00847218, 0.00850188, 0.00853078, 0.00866082, 0.00876466,
               0.00884425, 0.0089038, 0.00894771, 0.00897976, 0.00900302, 0.00901983,
               0.00903195, 0.00904066, 0.00904692, 0.00905142, 0.00905464, 0.00905695,
               0.00905861])

NO3 = np.array([0.0620055, 2.05641, 3.91791, 5.65319, 7.26956, 8.77436, 10.1748, 11.4777,
                12.6897, 13.8171, 14.8655, 15.8406, 16.7474, 17.5908, 18.3752, 19.1048,
                22.0579, 24.1218, 25.5706, 26.5919, 27.3147, 27.8278, 28.193, 28.4535,
                28.6394, 28.7723, 28.8673, 28.9353, 28.984, 29.0189, 29.0439])

O2 = np.array([0.0053437, 0.00534437, 0.00534619, 0.00534891, 0.00535233, 0.00535628,
               0.00536061, 0.00536521, 0.00536998, 0.00537484, 0.00537973, 0.0053846,
               0.00538941, 0.00539412, 0.00539871, 0.00540316, 0.00542299, 0.00543859,
               0.00545041, 0.00545917, 0.00546558, 0.00547023, 0.00547359, 0.00547602,
               0.00547776, 0.00547901, 0.00547991, 0.00548055, 0.00548101, 0.00548135,
               0.00548158])

plt.figure(figsize=(9,4.2))
# Use a log y-scale to display the wide dynamic range
plt.semilogy(t_min, H2O, '-o', label='H2O (g·L⁻¹)', markersize=4)
plt.semilogy(t_min, NO3, '-s', label='NO3⁻ (g·L⁻¹)', markersize=4)
plt.semilogy(t_min, H_plus, '-^', label='H⁺ (g·L⁻¹)', markersize=4)
plt.semilogy(t_min, N2, '-d', label='N2 (g·L⁻¹)', markersize=4)
plt.semilogy(t_min, O2, '-v', label='O2 (g·L⁻¹)', markersize=4)

plt.xlabel('Time (min)')
plt.ylabel('Concentration (g·L⁻¹), log scale')
plt.title('Aqueous-phase species — time evolution to steady state (40.0 °C)')
plt.grid(which='both', alpha=0.25)
plt.legend(loc='upper right', fontsize='small')
plt.xlim(0, max(t_min)*1.02)
plt.tight_layout()
plt.show()
```

- Image availability: the original plotted figure is img_16.png and will be shown by the chat program after this message.

 Image found in database 03-tbp-diluent-h2o-hno3-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: 

### Executive summary

- Overall the use case provides a coherent, well-documented set of phase-level reports (aqueous, organic, vapor) and time-series data for a three-phase TBP–diluent–H₂O–HNO₃–air mixing/extraction scenario; it is suitable for high‑level assessment and for preliminary engineering judgement but lacks several mechanistic and metadata items required for a full audit or formal verification.

### Data provenance and traceability

- Source: Stage Knowledge Base entries referenced as stg.aqueous_phase, stg.organic_phase, and stg.vapor_phase; developer listed as Valmor F. de Almeida (Cortix Tech).  
- The KB supplies species metadata (molar masses, phase), a reaction mechanism, time histories of concentrations and stage efficiencies, and mass-balance residuals.  
- Note: I consulted only the provided context. The repository’s "Supporting Information" sections were not present in the supplied context; if they exist in the KB, please provide them for a fuller audit.

### Methods, assumptions, and limitations observed

- Time window: analysis limited to timestamps up to 309.62 s (discrete points listed).  
- Concentration units: aqueous-phase concentrations are stated in g·L⁻¹; organic-phase species time-series lack explicit units in the KB (reports flagged this).  
- Stage modeling: stage efficiency history is available and used as a proxy for approach to equilibrium; however, no explicit equilibrium constants, kinetic rate constants, or detailed stage transport parameters (interfacial area, mass-transfer resistances, mixing rates) were supplied.  
- Uncertainty: stage efficiency includes mean ± std; other uncertainties (species concentration measurement or model error) are not well characterized in the provided context.

### Key observations relevant to an external audit

- Internal mass-balance bookkeeping appears consistent: stage mass-balance residuals reported as essentially zero.  
- System not at thermodynamic equilibrium within the observation window: stage efficiencies remain well below ~100%, so concentrations reflect kinetically limited behavior.  
- Data coverage is adequate for descriptive trends (time series to ≈5 min) but short for confirming equilibrium or long-term stability.

### Strengths

- Phase-separated reporting (aqueous, organic, vapor) with species-characteristics and time-series is clear and organized for review.  
- Mass-balance residual reporting and stage-efficiency time history enable assessment of model internal consistency and dynamic progress toward steady state.  
- Representative plots and numerical tables (e.g., aqueous concentrations to steady state, stage-efficiency evolution) are provided, aiding rapid evaluation.

### Weaknesses and gaps (items an auditor should flag)

- Missing mechanistic parameters: equilibrium constants, rate constants, and explicit stage transport parameters are not present, preventing rigorous mechanistic or predictive modeling.  
- Partial metadata: some time-series units are unspecified (notably organic-phase), and simulation initial/boundary conditions and solver settings are not included in the supplied context.  
- Limited uncertainty quantification: propagation of measurement or model uncertainties for species concentrations and reaction parameters is not provided.  
- Limited temporal scope: observation window (~5 minutes) may be insufficient for systems with slow mass transfer to reach equilibrium.  
- Supporting Information not available in provided context: any experimental protocols, instrumentation calibration, or raw model input files are not visible.

### Risks and potential impacts

- If used for design or safety decisions, the absence of equilibrium/kinetic constants and transport parameters risks incorrect extrapolation beyond the supplied time window.  
- Unit ambiguities (missing units) in parts of the KB could lead to misinterpretation or numerical errors if data are reused without clarification.

### Recommendations (practical, prioritized)

- Short term (immediate)
  - Provide or confirm missing metadata: explicit units for all time-series (organic phase), initial and boundary conditions, and exact time stamps.  
  - Supply the Supporting Information sections (raw inputs, protocol, solver settings, and any calibration data) for the audit repository.
- Medium term
  - Add equilibrium constants and/or reaction rate constants for the TBP–H₂O and TBP–HNO₃ complexation steps, or indicate that they are intentionally omitted.  
  - Provide stage-module details: interfacial area, mass-transfer resistances, mixing rates, and stage geometry/operating conditions.
- Longer term / for model validation
  - Extend the simulation/measurement window until stage efficiencies approach equilibrium (or show asymptotic behavior), and collect more time points during the slow tail.  
  - Implement and document formal uncertainty quantification: input-parameter ranges, sensitivity analysis, and propagation to predicted concentrations and efficiencies.  
  - Archive raw model/config files and processed datasets with versioning and checksums for reproducibility.

### Reproducibility and audit deliverables to request

- Raw simulation input files, code or executable versions, and environment/container definitions.  
- Complete Supporting Information including experimental procedures or numerical solver settings.  
- A consolidated data dictionary listing every KB field, units, and variable descriptions.  
- Version history of the KB entries and any preprocessing scripts used to generate the AI reports and plots.

### Conclusion

- The use case is well structured for descriptive analysis and initial engineering interpretation: phase-level reports, mass balances, and stage-efficiency history are provided and internally consistent.  
- However, for an external auditor to reach a formal opinion (e.g., on model validity, predictive capability, or suitability for design use), the KB must include supporting mechanistic parameters, full metadata, raw inputs, and documented uncertainty analyses.  
- I can re-evaluate once you supply the Supporting Information sections, raw inputs, or any missing metadata noted above.

## AI Briefing Control Parameters 
  + **Database:** CT_Solvex_db 
  + **Database table:** usecase-03 
  + **RAG # records:** 10 
  + **RAG search:** vector 
  + **Memory # questions:** 10 
  + **LLM:** gpt-5-mini 
  + **LLM cleverness:** 1.0 
  + **Total # tokens:** 48556 
  + **Discussion history size:** 12 
  + **Embedded images:** 2