--- title: "Cortix Tech AI Assistant Discussion on DB: \\texttt{usecase-02}" author: - \textsc{Cortix Tech} - \texttt{https://cortix.tech} date: 11 Nov 2025 --- # Cortix Tech AI Assistant Discussion Transcript **SW Developer:** Valmor F. de Almeida, Ph.D.; 11 Nov 2025.
```text ========================================================================================== ``` ## User: 1. Please briefly describe your role. ```text ========================================================================================== ``` ## Assistant: ### My role I am a Cortix Tech AI assistant that answers questions about the Solvex-generated data for Use-Case 02 (TBP‑Diluent‑H₂O‑Air Mixing). I use the provided context (knowledge database table named `usecase-02`) to produce concise explanations, stage-level reports, and help interpret simulation results. If you want, I can list available knowledge-base titles (the list may be incomplete). ```text ========================================================================================== ``` ## User: 2. Provide a tabular summary describing this usecase and any sub-usecase available; use the usecase Id number and make the table concise (max. 3 columns) such that it fits in a letter-size PDF document. ```text ========================================================================================== ``` ## Assistant: ### Use-Case and Sub-usecase Summary Note: items taken from the knowledge table `usecase-02`; the list may be incomplete. | Usecase Id | Title | Concise description | |---|---:|---| | 02 | Use-Case 02: TBP‑Diluent‑H₂O‑Air Mixing | Water extraction by TBP with a vapor phase; stage-level mixing, start-up simulation and results (system schematic included). | | 02.1 | Use-Case 02.1: TBP‑Diluent‑H₂O‑Air Mixing Parameter Study | Parametric study varying tau and inflow relative humidity; reports time-history of stage efficiency (mean ± std) and mass-transfer comparisons. | ```text ========================================================================================== ``` ## User: 3. Elaborate on the objectives of the usecase and sub-usecase. ```text ========================================================================================== ``` ## Assistant: ### Objectives Below are the stated and slightly elaborated objectives for Use-Case 02 and its sub-usecase 02.1 (items taken from the knowledge table `usecase-02`; the list may be incomplete). - Use-Case 02: TBP‑Diluent‑H₂O‑Air Mixing — Primary objectives - Develop a usecase scenario for water extraction by TBP with an explicit vapor phase and stage-level mixing. - Test the simulation implementation (start‑up and stage behavior) and present results, including system schematic and time‑history outputs. - Produce AI-assisted information and reporting (interpretation, recommendations, and stage-level diagnostics). - Use-Case 02.1: TBP‑Diluent‑H₂O‑Air Mixing Parameter Study — Primary objectives - Perform a parametric study (e.g., varying residence time and inflow relative humidity) for the TBP–diluent–H₂O–air system to assess mass transfer and stage efficiency sensitivity. - Test and validate the fixed-parameter simulation framework (minimum framework/demo for testing) and present aggregated results (time histories, mean ± std of stage metrics). - Support interpretation with AI outputs to guide model improvements (e.g., recommendations for approaching equilibrium, required transfer coefficients, or additional reaction steps). ```text ========================================================================================== ``` ## User: 4. Provide a description and analysis of all phases present. Enumerate the chemical reactions in the reaction mechanism, and include key simulation results with Stage efficiency. Use markdown typesetting for species names; always inline LaTeX into markdown. ```text ========================================================================================== ``` ## Assistant: ### Phases present — description and analysis - The system contains three explicit phases: aqueous, organic, and vapor. Each phase and its role: - Aqueous phase (bulk liquid): contains $H₂O(a)$ plus dissolved gases $O₂(a)$ and $N₂(a)$. This phase participates in gas–liquid exchange (aeration) with the vapor and in hydration/extraction equilibria with the organic TBP species. The stage-level behavior is mass-transfer limited (stage efficiency ≪ 100%), so gas uptake and aqueous concentrations evolve over the start-up period toward a steady plateau. - Organic phase (diluent + TBP): dominated by tri-n-butyl phosphate, represented as $[C₄H₉O]₃PO(o)$, and its hydrated adducts (e.g., $H₂O·[C₄H₉O]₃PO(o)$, $[H₂O]₂·[[C₄H₉O]₃PO]₂(o)$, $[H₂O]₆·[[C₄H₉O]₃PO]₃(o)$). These reactions describe water extraction into the organic phase (complexation/hydration), and their efficiencies rise during start-up as the stage approaches steady-state. - Vapor phase (gas): contains $H₂O(v)$, $O₂(v)$, and $N₂(v)$. Vapor concentrations change over time (e.g., reported $O₂(v)$ increased ~0.25142 → 0.25792 g·L⁻¹ and $N₂(v)$ increased ~0.81754 → 0.90840 g·L⁻¹ in the analyzed interval), indicating ongoing gas-phase mixing and gas–liquid transfer that is not at full equilibrium at the stage scale. ### Reaction mechanism (enumerated reactions) - The reaction mechanism (ordered reactions 0–5) that involve the aqueous/organic/vapor phases is: - 0 : $[C₄H₉O]₃PO(o) + H₂O(a) <=> H₂O·[C₄H₉O]₃PO(o)$ - 1 : $2\;[C₄H₉O]₃PO(o) + 2\;H₂O(a) <=> [H₂O]₂·[[C₄H₉O]₃PO]₂(o)$ - 2 : $3\;[C₄H₉O]₃PO(o) + 6\;H₂O(a) <=> [H₂O]₆·[[C₄H₉O]₃PO]₃(o)$ - 3 : $H₂O(a) <=> H₂O(v)$ - 4 : $O₂(v) <=> O₂(a)$ - 5 : $N₂(v) <=> N₂(a)$ - Notes on mechanism completeness and kinetics: - Reactions 0–2 are organic-phase complexation/hydration (water extraction into TBP adducts). - Reactions 3–5 are reversible phase-transfer (evaporation/aeration) steps for water and diatomic gases. - The Stage Knowledge Base provides stoichiometries and short descriptions but not explicit rate constants or Henry constants; observed dynamics indicate mass-transfer/transport limitations at the stage scale (consistent with stage efficiencies well below 100%). ### Key simulation results (stage efficiency and reaction efficiencies) - Simulation time grid and context: - Start-up time points reported (s): 0, 6.88054, 13.7611, 20.6416, 27.5221, 34.4027, 41.2832, 48.1638 (uniform Δt ≈ 6.88054 s). - Steady-state results reported up to 309.624 s for reaction efficiencies and stage metrics. - Overall stage efficiency (steady-state): - Mean stage efficiency (steady-state plateau) ≈ 50.64 % with low sample-to-sample variation ≈ 0.10 %. - Reported steady-state measurement uncertainty ≈ ±1.58 % (relative uncertainty ≈ 3.1 % of the mean). - Individual reaction efficiencies — steady-state (examples from reported time history; percent): - Reaction 0 (TBP + H₂O → H₂O·TBP): ≈ 49.9998 % - Reaction 1 (2 TBP + 2 H₂O → di-adduct): ≈ 50.0000 % - Reaction 2 (3 TBP + 6 H₂O → hexa-adduct): ≈ 50.0002 % - Reaction 3 ($H₂O(a) <=> H₂O(v)$): ≈ 54.2308 % (notably larger than the other reactions at steady-state) - Reaction 4 ($O₂(v) <=> O₂(a)$): ≈ 49.9973 % - Reaction 5 ($N₂(v) <=> N₂(a)$): ≈ 49.9884 % - Start-up dynamics (concise): - Reactions 0–2: start at 0% and increase monotonically during start-up, approaching ~50% but not crossing during the earliest window; by ~48 s they approach mid-to-high 40% (entry to steady regime thereafter). - Reaction 3 ($H₂O$ phase exchange): begins at ~35% and increases past 50% during start-up (50% crossing ≈ 39.3 s by linear interpolation), reaching ~51–54% as steady-state is reached. - Reactions 4 and 5 (O₂ and N₂ aeration): remain near 50% throughout start-up and steady-state; O₂ slightly below or near 50% (small initial dip then ~49.66–49.99%), N₂ shows a small decline from 50% down to ≈48.6% then small rebound. - Observations linking phases, mechanisms, and efficiencies: - The larger steady-state efficiency for the $H₂O(a) <=> H₂O(v)$ step (reaction 3) relative to the diatomic-gas exchanges suggests water vaporization/condensation at the stage is more active (or otherwise has greater reaction throughput) than the simple aeration exchanges for $O₂$ and $N₂$ under the simulated conditions. - All gas–liquid exchange steps (reactions 3–5) lack explicit rate constants in the KB; observed concentration trends and efficiencies imply mass-transfer-limited behavior at the stage scale rather than instantaneous equilibrium (consistent with reported stage efficiency ≪ 100%). - Organic-phase hydration reactions (0–2) progressively extract water into TBP adducts as start-up proceeds; their approach toward ~50% efficiency at steady-state indicates substantial but incomplete conversion/complexation under the simulated stage conditions. - Relevant numeric highlights for vapor-phase species (from vapor-phase analysis): - $O₂(v)$ increased modestly over the reported interval (≈ 0.25142 → 0.25792 g·L⁻¹). - $N₂(v)$ showed the largest absolute increase among vapor species (≈ 0.81754 → 0.90840 g·L⁻¹). - Short interpretation (operational implication): - The stage is not at full equilibrium; increasing the stage-level transfer efficiency (e.g., by improving contact/mass-transfer or residence time) would push reaction efficiencies closer to 100% and reduce the observed steady-state differences among phase-exchange steps. The relative prominence of the water phase-exchange suggests prioritizing water transfer pathways when targeting faster equilibration or stronger water extraction into the organic phase. - Data provenance: - Values and trends above are taken from the Use-Case 02 KB (table `usecase-02`) reports (start-up and steady-state histories). The KB provides stoichiometries, time histories, and stage-efficiency summaries but not explicit kinetic rate constants. ```text ========================================================================================== ``` ## User: 5. Comment on the Stage module (i.e. its key methods), describe the simulation setup including initial conditions, inflow, and time-integration including elapsed runtime. ```text ========================================================================================== ``` ## Assistant: ### Stage module — key methods and behavior - Overview - The Stage class implements a single solvent‑extraction mixing stage that holds three phasic state containers (aqueous, organic, vapor), inflow parameter containers, volumetric flowrate configuration, and a ReactionMechanism instance used to build source terms for the mass‑balance ODEs. - __step(time) - Advances the internal time by one self.time_step. - Builds the current state vector via __get_state_vector, integrates the ODEs on the short interval [time, time+time_step] using scipy.integrate.odeint and the private RHS __mbal_rhs_func, validates integration success, optionally checks mass conservation and flowrate monitors, updates phase containers (via __update_state_variables), and returns the advanced time. - Integration pattern: t_interval = linspace(time, time+time_step, num=2); odeint(..., full_output=True); assert info_dict['message'] == 'Integration successful.'. - __mbal_rhs_func(u_vec, params) - Private mass‑balance right‑hand side that computes time derivatives from advective inflows/outflows, reaction source terms (mapped from the ReactionMechanism), and phase transfer terms. This function is passed to odeint. - __get_state_vector() and __update_state_variables(u_vec, time) - __get_state_vector constructs the ODE state vector from the three Phase containers (species masses, volumes, timestamps). - __update_state_variables writes the integrated solution u_vec back into the Phase containers and advances their internal time stamps. - __update_mass_inflow_rates() and __perturb_mass_inflow_rates() - __update_mass_inflow_rates derives species mass inflow rates from the inflow phase containers combined with configured volumetric flowrates (vol_flowrate_* attributes). - If perturb_flowrates is True, __perturb_mass_inflow_rates may modify inflows to exercise robustness/uncertainty scenarios. - __call_ports(time) - Performs inter-module communications via named ports (e.g., 'aqu-inflow', 'aqu-outflow'). - Typical pattern: if an inflow port is connected, send(self, 'aqu-inflow') then recv('aqu-inflow') to get (check_time, mass_inflow_rates) and validate timestamp and vector length. - Inflow ports are treated as incoming (one‑way from upstream); outflow/product ports can be requested by downstream modules and the Stage will send computed outflow mass rates. - __check_mass_conservation(u_vec, write=False) and __compute_total_mass_flowrates(u_vec) - __check_mass_conservation computes the global mass residual (rate density) and optionally writes diagnostics. The stage prints a small residual (example: -2.84e‑17 g·L⁻¹·s⁻¹). - __compute_total_mass_flowrates returns total mass inflow/outflow rates used for monitoring and printed in g/min (conversion uses unit.gram/unit.min). - run() - Orchestrates the time loop from initial_time to end_time in increments of time_step. - Before entering the loop it calls __update_mass_inflow_rates(). - Each loop iteration optionally perturbs inflows, calls __step(time) to integrate one step, then __call_ports(time) to exchange port data. - Designed to operate inside a Cortix network (modules can run in parallel and are closed via system.close()). ### Simulation setup — initial conditions, inflow, time-integration, and elapsed runtime - Phases and initial conditions - Three explicit phases: aqueous_phase, organic_phase, vapor_phase. Initial state is stored in these Phase containers and __get_state_vector uses their stored species masses and volumes as the initial condition for integration. - The ReactionMechanism instance (rxn_mech) is mapped into the phase containers so reaction stoichiometries and species indices are known to the RHS. - Inflow specification - Inflows come from inflow phase containers (inflow_aqueous_phase, inflow_organic_phase, inflow_vapor_aqueous_phase, inflow_vapor_organic_phase) plus configured volumetric flowrates (vol_flowrate_org, vol_flowrate_aqu, vol_flowrate_vap_org, vol_flowrate_vap_aqu). - __update_mass_inflow_rates computes species mass inflow rates from those inflow phase compositions and volumetric flowrates. Port connections (e.g., 'aqu-inflow') may override or supply inflow mass rates when connected. - The Stage supports optional perturb_flowrates to test sensitivity. - Time‑integration setup (example patterns used in the runs) - Time stepping uses fixed steps: time_step, initial_time, end_time, show_time control the time loop and display cadence. - Integration per step uses scipy.integrate.odeint on the sub-interval [time, time+time_step] with two time points, i.e., a single-step integrator call that returns the state at the end of the step. - The time_step is often chosen from reaction/mechanism time scales. Example: for start‑up they set ave_tau = mean([data['tau'] for data in stg.rxn_mech.data]) and time_step = ave_tau / 15. - For start‑up they used end_time = 1 * stg.flow_residence_time_avg (one residence time) and time_step derived as above. For longer steady runs they extended end_time by multiples of flow_residence_time_avg and adjusted time_step accordingly. - Example numeric configuration and diagnostics (values taken from the usecase logs) - Flow residence time (average): flow_residence_time_avg = 5.160e+01 s (≈ 51.60 s). - Volume fractions: aqueous = 4.300e‑01, organic = 5.160e‑01, vapor = 5.393e‑02. - Typical printouts during runs: Stg-1::run():time[m]=0.9, 3.7, 5.2 (minutes) showing progress markers; printed total mass inflow/outflow rates (example: total mass inflow rate = 6.711e+02 g/min, outflow = 6.711e+02 g/min) and a very small net mass flow imbalance (e.g., -1.359e‑05 g/min). - Mass balance residual examples: Total mass rate density (mixture volume) residual ≈ -1.49037e‑16 to -2.84061e‑17 g·L⁻¹·s⁻¹ (indicating numerical conservation within tight tolerances). - Elapsed runtime (wall clock) from example runs - Start-up run (one residence time) elapsed wall clock time ≈ 717.74 s (see run() log). - Longer continuation / steady run elapsed wall clock time ≈ 1236.65 s (see run() log). - After runs, system.close() is called to shut down Cortix; Stage objects can be recovered from system_net.modules for continuing simulations. - Operational and numerical notes - The integrator is called for each fixed time step rather than using an event adaptive loop; this simplifies port synchronization and inter-module communication. - The module performs explicit checks on integration success and mass conservation and prints monitoring info when monitor flags are set. - ReactionMechanism supplies reaction stoichiometries; the KB as provided does not include explicit numeric rate constants/Henry constants in the reported snippets — observed dynamics therefore reflect the configured mechanism data and mass‑transfer parameterization contained in the Stage and rxn_mech objects. If you want, I can: - Provide a compact sequence of the exact attribute names and methods for quick reference. - Extract the specific time grid or simulated state vectors shown in the logs. ```text ========================================================================================== ``` ## User: 6. Discuss briefly the equations solved. Inline LaTeX formulae in the markdown output. Keep it very basic because you are not proficient in LaTeX; show no plots or graphs. ```text ========================================================================================== ``` ## Assistant: ### Equations solved Brief summary of the mathematical model solved by the Stage module and how it is integrated. - The system is written as a vector ordinary differential equation: $d\mathbf{u}/dt = \mathbf{f}(\mathbf{u},t)$, where $\mathbf{u}$ is the state vector of species masses (or concentrations) across the aqueous, organic, and vapor phases. - Species mass balance in a phase (generic species $i$ in phase $p$): - $\dfrac{d m_i^{(p)}}{dt} = \dot{m}_{i,\mathrm{in}}^{(p)} - \dot{m}_{i,\mathrm{out}}^{(p)} + \sum_r \nu_{i,r}\,R_r + \sum_{q} T_{i}^{(p\leftrightarrow q)}$ - Terms: $\dot{m}_{i,\mathrm{in/out}}^{(p)}$ are advective inflow/outflow mass rates, $\nu_{i,r}$ are stoichiometric coefficients for reaction $r$, $R_r$ are reaction rates (mass per time), and $T_{i}^{(p\leftrightarrow q)}$ are interphase transfer terms. - Reaction rates and parameterization: - Reactions are provided by the ReactionMechanism (stoichiometry and reaction identifiers). A common practical parameterization used in the Stage is a relaxation/first‑order form: - $R_r \approx \dfrac{C_{\mathrm{eq},r} - C_r}{\tau_r}$, - where $C_r$ is a driving concentration (or extent), $C_{\mathrm{eq},r}$ the local equilibrium target, and $\tau_r$ a relaxation (residence) time taken from mechanism data. This produces the observed exponential approach to steady state. - Interphase (phase transfer) modelling: - A linear driving‑force form is used for phase transfer in practice, e.g. - $T_{i}^{(p\to q)} = k_{pq,i}\,\bigl(C_i^{(p)} - C_i^{(q)}/H_i\bigr)$, - where $k_{pq,i}$ is a mass‑transfer coefficient, $H_i$ a partition/Henry factor when relevant, and concentrations $C$ are phase‑specific. This captures evaporation/condensation and gas–liquid exchange. - Global mass conservation and diagnostics: - The module computes a global mass residual (rate density) to check conservation: - residual $\approx \sum_p \sum_i \dfrac{d m_i^{(p)}}{dt} - \bigl(\sum_p \dot{M}_{\mathrm{in}}^{(p)} - \sum_p \dot{M}_{\mathrm{out}}^{(p)}\bigr)$, - reported residuals are numerically near zero (examples: 10⁻¹⁶ to 10⁻¹⁷ g·L⁻¹·s⁻¹). - Stage efficiency (reported quantity): - Stage efficiency is a diagnostic percentage computed from transfer or reaction throughput, typically expressed as - $\eta_{\mathrm{stage}} = 100\% \times \dfrac{\text{mass reacted or transferred}}{\text{mass available}}$, - and reported as time histories (mean ± std) for each stage/parameter set. - Numerical time integration: - The RHS $\mathbf{f}(\mathbf{u},t)$ is passed to scipy.integrate.odeint and integrated stepwise: each step solves on the short interval $[t, t+\Delta t]$ to produce the new state at $t+\Delta t$. - Time step selection is linked to mechanism time constants (example: $\Delta t \approx \text{mean}(\tau)/15$). Integration success and mass balance are checked each step; the implementation uses fixed steps to simplify port synchronization. ```text ========================================================================================== ``` ## User: 7. What were the elapsed times for startup and steady-state? Show one Stage efficiency plot at start-up and an additional plot of Stage efficiency at steady-state. Include the image metadata. ```text ========================================================================================== ``` ## Assistant: ### Elapsed wall‑clock runtimes - Start‑up run (one residence time): elapsed wall‑clock time ≈ 717.74 s (≈ 11.96 min). - Steady‑state / longer continuation run: elapsed wall‑clock time ≈ 1236.65 s (≈ 20.61 min). ### Stage efficiency plots (images will be displayed after this response) - Start‑up: Stage Efficiency @ 40.0 C Start‑Up plot - Key points: Efficiency rises from ≈ 22.5% to ≈ 49% over the startup window (0 → 1·$ \tau $), with largest uncertainty early on and narrowing error bars as the system stabilizes. Behavior matches a first‑order approach to a steady value near 50%. - Image metadata: - DB table title: Use-Case 02: TBP‑Diluent‑H$_2$O‑Air Mixing - DB file_name: 02-tbp-diluent-h2o-air-results-clean.md - img_filename: img_08.png - VLM: gpt-5 - Source note: 02-tbp-diluent-h2o-air-results-clean.md - img_08.png (image available for external display). - Steady‑state: Stage Efficiency @ 40.0 C Steady‑State plot - Key points: Efficiency approaches a steady level around ≈ 51%; 90% of total rise is reached by about 1.0·$ \tau $, ~95% by ~1.2–1.3·$ \tau $, and practical steady‑state (≈50.8–51.2%) by $t \gtrsim 3\; \tau$. Early uncertainty is large but narrows to ±1–1.5 percentage points at steady‑state. - Image metadata: - DB table title: Use-Case 02: TBP‑Diluent‑H$_2$O‑Air Mixing - DB file_name: 02-tbp-diluent-h2o-air-results-clean.md - img_filename: img_21.png - VLM: gpt-5 - Source note: 02-tbp-diluent-h2o-air-results-clean.md - img_21.png (image available for external display). Image found in database 02-tbp-diluent-h2o-air-results-clean.md. ![ Stage Efficiency @ 40.0 C Start-Up 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Image found in database 02-tbp-diluent-h2o-air-results-clean.md. ![ Stage Efficiency @ 40.0 C Steady-State plot.](data:image/png;base64,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```text ========================================================================================== ``` ## User: 8. Describe any parameter variation study setup and results, if available, and summarize lessons learned. ```text ========================================================================================== ``` ## Assistant: ### Parameter variation study — setup and results - Overview: A 4‑case parametric sweep was run varying relaxation/residence time multiplier ($\tau$ factor) and inflow relative humidity (RH) together. Each run produced time histories of stage efficiency (mean and std) from $t=0$ to $t=246.765\ \mathrm{s}$ (start‑up → approach to steady state). - Setup (what was varied and how): - Number of cases: 4 (zipped pairs of factors). - $\tau$ factors: [0.1, 0.5, 1.0, 1.5] (applied to per‑reaction tau parameters via `params['tau-factor-*']`). - Inflow RH factors: [0.1, 0.5, 1.0, 1.5] applied to base inflow RH = 25 → RH = 2.5, 12.5, 25.0, 37.5 (%). - Implementation: loop zipping `param_tau_factors` and `param_inflow_rh_factors`, update `params`, call `run(params)`, collect `stages`. Four stage efficiency histories (`efficiencies`) were plotted and analyzed. - Metrics recorded: time series of stage efficiency mean and std; convergence times; final (t = 246.765 s) mean and std per case. - Primary quantitative results (final values at $t=246.765\ \mathrm{s}$): - $\tau=0.1$: mean = 75.3369%, std = 16.4582% (mean increase vs initial 22.5% = +234.8%; std change = −28.6%) - $\tau=0.5$: mean = 63.7898%, std = 6.8755% (mean +183.5%; std change = −70.2%) - $\tau=1.0$: mean = 55.8647%, std = 7.7048% (mean +148.4%; std change = −66.6%) - $\tau=1.5$: mean = 51.1360%, std = 11.6832% (mean +127.3%; std change = −49.3%) - Shared initial condition: mean = 22.5%, std = 23.0489% at $t=0$. - Time to ~95% of final mean: approximately 60–65 s for all RH/cases; std reaches near‑final by ~100 s. - Sensitivity to inflow RH (summary): - Increasing RH shifts the steady mean upward modestly and increases the final spread (std) noticeably. - RH had little effect on convergence time; it mainly adjusted the steady‑state level and uncertainty. - Behavior and tradeoffs observed: - Monotonic time trends: for each $\tau$ the mean increased monotonically over time and the std decreased monotonically (no reversals in recorded histories). - Tradeoff between mean and variability: - Lowest $\tau$ (0.1) gives highest steady mean but retains the largest variability (high std). - Intermediate $\tau$ (0.5) provides the best reduction in variability (lowest steady std) while maintaining a relatively high mean. - Increasing $\tau$ beyond 0.5 reduces the mean and increases variability again relative to $\tau=0.5$ (non‑monotonic std dependence on $\tau$). - Convergence speed: smaller $\tau$ converges faster; larger $\tau$ converges slower. - Lessons learned and practical recommendations: - Tradeoff management: choose $\tau$ based on whether maximizing mean extraction (favor small $\tau$) or minimizing variability (favor $\tau\approx0.5$) is more important for the application. - Robustness: $\tau=0.5$ is a practical operating choice here — good compromise: high mean with minimal variability. - RH control: increasing inlet RH raises mean efficiency but tends to increase uncertainty; if consistent performance is required, control RH or compensate with process design (longer residence time or improved mass transfer). - Monitoring and design targets: target reductions in std (process consistency) may provide more operational benefit than marginal gains in mean if product specifications are tight. - Further studies recommended: expand the grid (more $\tau$ values, independent RH sweeps), vary contactor geometry or mixing parameters, and quantify sensitivity to reaction rate constants / mass‑transfer coefficients (not present explicitly in the current KB snippets). - Caveat: results and recommendations are derived from the provided Use‑Case 02 data (knowledge table `usecase-02`) and reflect the parametric choices and mechanism parameterization contained therein; the table may not contain full kinetic constants or broader design details. ```text ========================================================================================== ``` ## User: 9. Were there any surprising results from the simulations? or noteworthy aspects? ```text ========================================================================================== ``` ## Assistant: ### Surprising / noteworthy results - Large scale difference: $H_2O$ concentrations dominate (orders of magnitude larger) so the reported $H_2O$ drop (≈ −5.925 g·L⁻¹) overwhelms gas changes and dominates mass balances. - Mass‑bookkeeping mismatch: the small combined increase of $N_2$ + $O_2$ (~+0.00104 g·L⁻¹) does not account for the full $H_2O$ decrease, implying either other species or pools changed, a phase change/evaporation, measurement rounding, or that reported numbers come from different pools/units — this is a key red flag to investigate. - Water exchange stands out: the $H_2O(a) \!<=>\! H_2O(v)$ step shows a noticeably higher steady efficiency (~54%) versus ~50% for most other reactions, indicating water phase transfer/condensation is more active than simple diatomic gas aeration under these conditions. - Organic complexation dominates kinetics: reactions involving $[C_4H_9O]_3PO(o)$ (reactions 0–2) have rates ~0.116–0.807 mM/s, one to two orders of magnitude larger than gas↔adsorbed exchange rates, so the organic‑phase hydration controls much of the transient behavior. - Strong, fast start‑up settling: mean stage efficiency more than doubles during start‑up (≈ +113%) while uncertainty drops ≈ 89.8%, consistent with a rapid exponential approach to a reproducible steady state. - Nearly uniform steady efficiencies (~50%): five reactions converge near 50% efficiency at steady state — this could reflect real symmetric partitioning or an artifact/normalization in the diagnostic; worth checking how efficiency is defined and computed. - Fractional vs absolute change caveat: $N_2$ shows the largest fractional increase (~+11%) despite tiny absolute change; avoid relying only on percent change when comparing species with very different absolute concentrations. - Non‑monotonic parameter sensitivity: the tau sweep showed a tradeoff — smallest $\tau$ (0.1) gives the highest mean but largest variability, while $\tau=0.5$ gives the best variance reduction; variability does not change monotonically with $\tau$, which is operationally important. - Numerical / performance notes: runs had substantial wall‑clock cost in the provided logs (≈ 718 s for start‑up, ≈ 1237 s for steady continuation), so computational expense may be notable if running many scenarios or finer grids. - Practical next steps suggested: verify units/pools and mass bookkeeping, check for omitted species or phase‑change accounting, confirm how efficiencies are normalized, and if needed expand parametric scans (tau, RH, transfer coefficients) and add explicit rate/Henry parameter reporting to reduce ambiguity. ```text ========================================================================================== ``` ## User: 10. How would you go about simulating the shutdown of the mixer? Show examples of Cortix Solvex code snippets. ```text ========================================================================================== ``` ## Assistant: ### Simulating mixer shutdown — Cortix / Solvex examples Brief: two common shutdown strategies are shown — abrupt turn‑off of a species inflow, and a controlled (ramped) shutdown. Use the Stage attributes shown in the Use‑Case 02 context (e.g., `flow_residence_time_avg`, `time_step`, `initial_time`, `end_time`, `inflow_organic_phase.set_value(...)`, and `system.run()`). - Abrupt shutdown (turn off TBP inflow and continue for a few residence times) ```py # extend simulation by 3 residence times and set step size end_time = stg.end_time + 3 * stg.flow_residence_time_avg time_step = 3 * stg.flow_residence_time_avg / 20 show_time = (True, 10 * time_step) stg.time_step = time_step stg.initial_time = stg.end_time # continue from current state stg.end_time = end_time stg.show_time = show_time # Abruptly zero TBP in the organic-phase inflow (mass concentration) tbp_mass_cc_org = 0.0 stg.inflow_organic_phase.set_value(tbp_org_name, tbp_mass_cc_org) # optionally reduce monitoring if running many modules in parallel stg.monitor_mass_flowrates = False stg.monitor_mass_conservation_residual = False # run and then close Cortix system.run() system.close() # after close, recover the stage object (if needed) stg = system_net.modules[0] ``` - Controlled/ramped shutdown (linearly reduce TBP inflow over N sub-intervals) ```py # user-specified ramp parameters n_ramp_steps = 10 ramp_duration = 3 * stg.flow_residence_time_avg # total ramp interval step_duration = ramp_duration / n_ramp_steps # time stepping for each sub-run (fine sub-steps inside each step) inner_steps = 10 inner_dt = step_duration / inner_steps # replace with measured/known current inflow concentration tbp_start = 1.0 # starting TBP mass conc in organic inflow (g/L) — replace as appropriate for k in range(1, n_ramp_steps + 1): tbp_val = tbp_start * (1.0 - k / n_ramp_steps) # linear ramp: tbp_start -> 0 stg.inflow_organic_phase.set_value(tbp_org_name, tbp_val) # advance simulation by one short interval stg.time_step = inner_dt stg.initial_time = stg.end_time stg.end_time = stg.end_time + step_duration stg.show_time = (True, 5 * inner_dt) # run the network for this sub-interval system.run() # final cleanup system.close() stg = system_net.modules[0] ``` - Post‑shutdown diagnostics and recommended checks ```py # recover stage (if system closed) stg = system_net.modules[0] # mass-balance residual history (method shown in context) residual_hist = stg.mass_balance_residual_history() print("Residual samples:", residual_hist.samples) # example attribute; inspect object to find fields # examine organic-phase timestamps (confirm windows) print("Organic phase time stamps:", len(stg.organic_phase.time_stamps), "entries") ``` - Notes: - The context shows `mass_balance_residual_history()` is available; method names for other histories may vary. If you need reaction‑rate histories or species time series, inspect the Stage/Phase objects (e.g., `stg.rxn_mech`, `stg.organic_phase`) to locate available history methods/attributes. - To capture more detailed output during shutdown set `stg.monitor_mass_flowrates = True` and `stg.monitor_mass_conservation_residual = True` before `system.run()` to print inflow/outflow and residual diagnostics. - If running in a parallel Cortix network, turning off monitors (set False) may reduce I/O overhead and speed up runs (context examples show both patterns). - Suggested shutdown practice (operational checklist) - Plan to simulate for multiple residence times after changing inflow to observe approach to new steady state (common choice in examples: 3 × `flow_residence_time_avg`). - Use `stg.initial_time = stg.end_time` before continuing so the integrator uses the current state as initial condition. - Choose `time_step` relative to mechanism timescales (examples use `time_step = some_multiple * flow_residence_time_avg / N`). - Verify mass conservation residuals after shutdown (expected residuals in examples were O(1e‑17) g·L⁻¹·s⁻¹). - Recover the Stage from `system_net.modules[0]` after `system.close()` for post‑processing. If you want, I can: - Provide a ready-to-run shutdown script tailored to your current Stage state (I can read `stg.flow_residence_time_avg` and the present TBP inflow value and produce the exact ramp schedule). - Show example code to save time-series outputs (species histories, reaction rates) to CSV for offline analysis — tell me which histories you need and I’ll check available methods in the `usecase-02` KB. ```text ========================================================================================== ``` ## User: 11. Describe and show one species concentration plot, with multiple species charts, for each of the phases, namely organic, aqueous, and vapor, at steady-state; include image metadata. ```text ========================================================================================== ``` ## Assistant: ### Steady‑state species concentration plots (one multi‑chart figure per phase) Below are concise descriptions for one multi‑species concentration figure for each phase. Images are available and will be displayed after this response. - Organic phase — species plotted: $[C_4H_9O]_3PO(o)$, $H_2O\cdot[C_4H_9O]_3PO(o)$, $[H_2O]_2\cdot[[C_4H_9O]_3PO]_2(o)$, $[H_2O]_6\cdot[[C_4H_9O]_3PO]_3(o)$; steady‑state (approx., g·L⁻¹): free TBP $\approx 228.085$, monomer hydrate $\approx 12.176$, dimer hydrate $\approx 45.883$, trimer hexahydrate $\approx 10.542$; interpretation: rapid equilibration (≈1–2 min) with dimer hydrate the dominant associated form while free TBP remains the largest pool. Image metadata: DB table title: Use-Case 02: TBP‑Diluent‑H$_2$O‑Air Mixing; DB file_name: 02-tbp-diluent-h2o-air-results-clean.md; img_filename: img_14.png; VLM: gpt-5. - Aqueous phase — species plotted: $H_2O(a)$, $N_2(a)$, $O_2(a)$; steady‑state (approx., g·L⁻¹): $H_2O(a)\approx 9.86\times 10^{2}$, $N_2(a)\approx 9.1\times 10^{-3}$, $O_2(a)\approx 5.57\times 10^{-3}$; interpretation: water remains the dominant mass (small % drop on equilibration), while dissolved gases reach low steady concentrations (quick approach within a few minutes). Image metadata: DB table title: Use-Case 02: TBP‑Diluent‑H$_2$O‑Air Mixing; DB file_name: 02-tbp-diluent-h2o-air-results-clean.md; img_filename: img_16.png; VLM: gpt-5. - Vapor phase — species plotted: $H_2O(v)$, $N_2(v)$, $O_2(v)$; steady‑state (approx., g·L⁻¹, from final reported timestep): $H_2O(v)\approx 0.0276972$, $N_2(v)\approx 0.908398$, $O_2(v)\approx 0.257921$; interpretation: N₂ is the dominant vapor mass, O₂ and H₂O are smaller but H₂O shows the largest relative change during transients; vapor traces are monotonic and reach steady values within a few minutes. Image metadata: DB table title: Use-Case 02: TBP‑Diluent‑H$_2$O‑Air Mixing; DB file_name: 02-tbp-diluent-h2o-air-results-clean.md; img_filename: img_18.png; VLM: gpt-5. Image found in database 02-tbp-diluent-h2o-air-results-clean.md. ![ Vapor Phase Steady-State 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ITE9N07ubNm/Xrr7/e9XqZNWLECIf25MmTFRcXd8/zvv32W4WHh9vbxYoVU+fOnZ2aDQAAAAAA/KtChQp64403dPToUW3evFmDBw+Wu7t7qoU0CQkJWr58uXr37q3y5cvrlVde0aFDh4y+DQAAAAAA8pQcXzyDm4oXL65Ro0Zp9erVioqK0pkzZ7Rnzx6FhobKYrEoJCRErVu3djhn165dCggIyFeZkDn79+9XTEyMihUrpgceeMDoOAAAAACAHK5w4cIaNGiQNmzYoBMnTujNN99U5cqV77qi6t69e/X888+rbNmy6tu3r37++Wd70Y3RBg4c6ND+66+/NGrUqHuu+Hrs2DE98cQTDn3Vq1dX8+bNUz0nMDDQ4c8nLTvB9O/fX3Xq1LG3jxw5ohEjRtw1386dOzVu3DiHvlGjRqW48AkAAAAAAHC+1q1bKygoSOHh4Zo1a5batGkjKfVFSMLDw/Xxxx/rwQcfVPPmzfXtt98aGR8AAAAAgDyD4pkcrnLlypoxY4bOnTunL7/8Ut26dVPRokUdjnFxcZGfn59CQkL01FNPOXy3ePFihYSE5PlMcI7t27dLkpo3by4XFxeD0wAAAAAAcpNKlSrpzTff1PHjxxUSEqJBgwapSJEiqRbSxMXFadGiRerRo4cqVKigCRMmGH0L6ty5s30XnWTffvut2rZtqw0bNtyxC43FYtEnn3yixo0b69y5cw7fvffee07/3dpsNuuzzz6TyWSy982ZM0edO3fW77//7nDs5cuX9emnn6pjx44OuwDff//9eu2115yaCwAAAAAA3FuRIkUUEBCgTZs26dixY3r99ddVsWLFuy5CsnPnTo0cOdLo6ACcyMvLSxEREYqIiNBDDz0kSRo5cqS9L6WPl5eXwakBAACAvIHimRxs0qRJOnz4sIYNG6bChQvf83gXFxdNmzZNjRs3duifMWNGns4E59mxY4ckqUWLFgYnAQAAAADkZm3bttWcOXMUHh6uGTNmqHXr1vZJH9KdK6qeO3dOH3zwgcGpb5o/f76qVKni0Ldt2zZ17NhRJUqUUN26ddWsWTPdd9998vb21ksvvaTLly87HP/iiy/qsccey5J8HTp00Pvvv+/QFxwcrMaNG6tMmTJq0qSJateuLR8fH7344osOhTNeXl5atGjRHYugAAAAAACA7FWlShW99dZb+ueffxQcHKwBAwaocOHCdxTSAMh7zGazvL29FRsbq+DgYEnSmDFj5O3tnerHbGaKHwAAAOAM/Jd1Dta9e3e5ubml6xwXFxeNGzfOoW/t2rV5OhOcJ3nnmZYtWxqcBAAAAACQF7i7u2vo0KHavHmzjh07pgkTJqh8+fI5eiKIr6+vNm/eLD8/vzu+i4mJ0f79+7Vr1y4dP37cXgyUzNXVVR988IEmT56cpRlfeeUVTZ06Va6urg794eHh2rNnjw4dOqT4+HiH72rUqKFff/1VderUydJsAAAAAAAgfdq3b6/vvvtO58+f1/Tp01WjRo073jkAyHtmzJghq9UqPz8/1ahRw+g4AAAAQL5gSPFM//79derUKSOGThebzaZZs2blul1SWrdu7dC2WCy6fv26QWluyomZ4CgsLEynT5+W2WxWs2bNjI4DAAAAAMhjqlatqrffflsnT57UunXrVKtWrRw7EaRChQrasGGDFi5cKD8/v3uu7Ojp6amnn35a+/fv1yuvvJItBUHPPvus9u3bp759+95RRHOrKlWqaMqUKdq3b5+qV6+e5bkAAAAAAED6xcXFadWqVVq4cKGOHTuW4xYbAeBciYmJ9vloI0eONDgNAAAAkH8UMGLQH3/8UcuWLdOoUaP0yiuvyMfHx4gYd7VixQq9/vrrOnDggN58802j46RLiRIl7ui7fPmyihQpYkCam3JiJjjasWOHJKlevXry8PAwOA0AAAAAIC86d+6c5syZo9mzZ+f4iSBms1l9+vRRnz59dPXqVe3Zs0cnTpxQdHS0YmNjVaxYMXl5ealu3bqqXbv2PQtsbhcYGKjAwMBMZaxZs6Z++OEHXblyRTt27NDRo0d1+fJleXh4qHTp0mrYsCGrVgIAAAAAkIPt3LlTQUFBWrhwoS5fvixJ9sVGcvJ7EwCZs2rVKp07d07e3t56+OGHjY4DAAAA5BuGFM9IUnx8vD7//HN99dVXGjp0qF5++WVVqlTJqDiSpKSkJP3www/68MMPdeDAAdlstlz5MuLs2bN39Hl5eRmQ5F85MRMcbd++XZLUsmVLg5MAAAAAAPKS+Ph4LV26VEFBQdqwYYOsVmuumwRStGhRtWvXTu3atTM6SoqKFSumLl26qEuXLkZHAQAAAAAA93D+/Hl99913mj17tg4fPixJDrvz3v6+5G47zgLInb755htJ0tChQ+Xm5mZwGgAAACD/MKx4JllsbKy++uorffPNN+rWrZtGjBihrl27ZuvkidOnT+vbb7/VrFmzFB4efscEjtwykSPZ1q1bHdqVKlUy/BetnJgpP7JarbJYLCl+t2nTJklSnTp1dPHiRXu/l5dXulfPBQAAAABg165dmj17tn744Ye7rpya3FeqVKnsDwkAAAAAAJANEhIStHz5cgUFBWndunUOi4tIKb8refDBBzVkyBANHDgw2/MCyDr//POP1q5dK0l68sknDU4DAAAA5C+GFM+sXbtWzz77rI4cOWJ/AZCUlKRVq1Zp1apV8vX11SOPPKLHHntMbdq0yZLilbCwMC1evFiLFy/Wjh07ZLPZHCZw2Gw2mc1mjRo1Ss8//7zTx89Ks2bNcmh369bNoCT/yopMERERDkUeaXHs2DGHdkxMjK5cuZLpLBl17dq1u7adLTIyUtWqVbvrMSNGjNCIESPs7ePHjzOBKYfJ7ucGeQPPDTKC5wYZwXODjMhPz01MTIzREYAsdeHCBX333XcKCgrS33//LSn1lVNtNptcXFzUtWtXDRkyRP7+/tmeFwAAAAAAICv9/vvvmj17thYsWKCoqChJd19cpHjx4urfv7+GDBmixo0bZ3/gNLLZbDp58qT279+vsLAwRUdHq2DBgipRooSqV6+uJk2aqFChQkbHBAxxt4VtJWnKlCmy2Wxq27atihUrposXL7KwLQAAAJBNDCme6dSpk/bv369PPvlE77//vq5evWp/KWCz2XT+/HlNmzZN06ZNU7FixdSqVSv5+fmpcePGqlu3rkqUKJGu8ZKSkvT3338rNDRUW7duVUhIiI4ePWr//vaiGZvNpjZt2mjq1KmqW7eu8248G6xZs0Zbtmxx6AsICDAmzP/LqkzTpk3TpEmTMnWNXbt2KTw8PNNZnGXXrl1Zev3klX7TY9u2bfL09MyCNHCWrH5ukDfx3CAjeG6QETw3yIi8/NycPn3a6AiA0yUkJGjFihWaPXu21q5dq6SkpHuunFqzZk0NGTJEgwYNkq+vb7ZnBgAAAAAAyCoXL17Ud999p9mzZ+vAgQOS7r64iNlsVseOHTVkyBA9/PDDKliwYLZnTouoqCgtW7ZMv/zyizZu3KjIyMhUj3V1dVX37t01ZswYtW3bNsuzBQYGZmr+yODBgzV79mznBZLUv39//fDDDw59lSpV0smTJ506DnIei8UiHx+fex63efNm+3ERERHy9vbO6mgAAABAvmdI8Yx08xfl8ePH66mnntJ7772nadOmKTY21qGIRro52X/NmjVas2aN/VxfX19VqlRJ5cqVk6+vr9zd3VW4cGG5uLgoNjZWN27c0KVLlxQWFqawsDD9888/SkhIsJ9/+0uJW4tmGjZsqHfeeUddunTJpj8J57l06ZLDjiGS1Lt3bzVt2tSgRDkzEwAAAAAAyLw///xTQUFBWrBggS5duiTp7iunFitWTH379tWQIUP0n//8J/sDA5AkeXl5KSIiQnFxcapbt66ioqI0d+7cu74P9fLyysaEAAAAAJD7JCUlaeXKlZo9e7Z+/vlnJSYm3nNxkapVqyogIEABAQEqX758tmdOj2eeeUYzZsxQfHx8mo5PSEjQsmXLtGzZMv33v//V//73PxUrViyLU+YcK1euvKNwBgAAAABgPMOKZ5KVLFlSH3/8sV588UV9/vnn+vbbbxUdHZ3ii4Nk58+fT/NuIbefK6X8UqJNmzZ68cUX5e/vn5HbMJzVatXAgQMVFhZm7/P09NTUqVPJBAAAAAAAnCIyMlLff/+9Zs+erf3790u6+8qpJpNJ7dq105AhQ/Too4+qcOHC2Z4ZgCOz2Sxvb2/NmzdPUVFRKl++vPr3768CBQx/VQwAAAAAuc6+ffsUFBSk+fPn23diudviIkWKFNFjjz2moUOHqk2bNtkfOIN27tyZYuGMi4uLypQpo9KlSyshIUGnTp3S5cuXHY6ZO3eu/v77b23YsEEeHh7ZFdkwly9f1tNPP210DAAAAABACnLM34iWKVNGH374oSZOnKjZs2crKChIf/zxh/37W18qJEupMOZ2dzuvWLFi6tOnj0aOHKlGjRplIr3xXn75Zf38888Ofd98840qVKhgUKKszzRq1Cj16dMnXeccO3ZMvXv3trebNm2qWrVqOSVPRly7dk27du1yyOPu7p5l491t2+TUtGrVSqVKlcqCNMio7H5ukDfw3CAjeG6QETw3yIj89NwcOnTI6AhAuiUlJWn16tUKCgrSmjVr0rRyaqVKlTR48GAFBASocuXK2R0ZQBp8/fXXkqSnnnqKwhkAAAAAyICGDRsqNDRU0t0XF5GkFi1aaOjQoXr88cdzfQFJ8eLF9cQTT6h79+5q3bq1ihYtav8uKSlJW7du1cSJE7V161Z7/65duxQQEKBFixZlS8aPP/5Y9erVS/PxZcuWddrYL7/8ss6ePStJcnd317Vr15x2bQAAAABA5uS4vxV1d3fXM888o2eeeUYHDx7UDz/8oNWrV2vv3r13vGxIqTDmdrcX2JQsWVKdO3dW79691bNnTxUsWNDp95Ddpk6dqk8//dShb9y4cerbt69BibInk4+Pj3x8fDJ1DQ8Pjxy1NbC7u3uW5omLi0v3OUWLFs1Rf0a4U1Y/N8ibeG6QETw3yAieG2REXn5ucvtfjCN/KleunC5evCjp7iunFi5cWA8//LCGDh2q9u3bZ39QAGm2f/9+bdu2TS4uLho2bJjRcQAAAAAgV9q7d6/955TelZQtW1b//e9/FRAQoPvvvz+74zld5cqV9frrr+uJJ55IdXdhFxcX+fn5KSQkRKNGjdL06dPt3y1evFghISFq165dlmdt1KiR/Pz8snyc223atEkzZsyQdHPn1zfffFPjxo3L9hwAAAAAgJTluOKZW9WuXVtvvfWW3nrrLV24cEFbtmzR7t27tWfPHh05ckTnz5+/6+4zRYsWVZUqVVS3bl01adJE//nPf9S4ceM0Fd3kFvPnz9eYMWMc+gICAvTBBx8YE0g5MxMAAAAAAMiYiIgImUwm2Wy2FCeCNG3aVEOHDlW/fv3ybOEbkNd88803kqTevXs7dXVdAAAAAMhvkt+VJL8ncXNzU8+ePTVkyBB17txZZrPZyHhOM2nSJHXq1Elubm5pOt7FxUXTpk3TH3/8oT179tj7Z8yYkS3FM0a4ceOGhg8fbn8Wnn32WTVp0sTgVAAAAACAW+Xo4plblS5dWn369FGfPn3sfQkJCTp79qyuXLmi69evKykpSYULF5a7u7t8fHxUokQJAxNnvVWrVmnw4MEOBUSPPPKIZsyYYViBUE7MBAAAAAAAMi+5gEa6+Z5m0KBBGjJkiGrVqmVwMgDpERMTo7lz50qSRo4caXAaAAAAAMjdkt+V1K9fX0OGDNGAAQNUsmRJg1M5X/fu3dN9jouLi8aNG6fHH3/c3rd27VpnxspR3njjDR0/flySVLFiRb3zzjsOhUMAAAAAAOPlmuKZlLi6uqpy5cpGxzBESEiI+vTpo8TERHtfp06dtGDBArm4uJAJAAAAAAA4jc1mk6urq7p3764hQ4aoW7du/K4P5FILFizQ1atXVb16dbVv397oOAAAAACQa3l5eemJJ57QkCFDVL9+faPj5EitW7d2aFssFl2/fl1FihQxKFHW2L17tz7//HN7+8svv5SHh4dxgQAAAAAAKcrVxTP51c6dO9WzZ0/Fxsba+1q0aKGlS5emeYvc/JAJAAAAAABk3oMPPqghQ4Zo4MCB8vb2NjoOgHuwWq2yWCwpfmez2TR16lRJ0oABA+zHeXl5yWw2Z1tGAAAAAMgLzp07J1dXV6Nj5GglSpS4o+/y5ct5qngmISFBw4YNU1JSkiSpT58+6tGjh8GpAAAAAAApoXgml9m3b5+6du2qmJgYe1+DBg20Zs0aubu7kwkAAAAAADjVvn37jI4AIB0sFot8fHzueVxgYKACAwMlSRERERTHAQAAAEA6UThzb2fPnr2jz8vLy4AkWef999/X/v37JUnFixe3L1oBAAAAAMh5KJ7JRQ4fPqxOnTopKirK3lerVi2tXbtWnp6eZAIAAAAAAAAAAAAAAECOsHXrVod2pUqV5Obmli1jx8XF6cSJE7JYLHJ1dZWXl5fKli3r1F1vDh48qHfffdfe/vDDD+Xr6+u06wMAAAAAnIvimVzi1KlT6tixoyIiIux9VapU0fr16w1bFTInZsLdeXl5Ofz/9eqrr2rmzJl66qmn9M4776R6DgAAAAAAAAAAAAAAQHrMmjXLod2tW7dsGfeZZ57RiRMnFBsb69BfoEABNWrUSF27dtWoUaMyNbfFarVq2LBhio+PlyS1bt1aTz75ZKZyp1dERIQuXryYrnOOHTvm0I6JidGVK1ecGSvdrl27dtd2bnP16tUMnVOwYMEsSJN35bXnBtmD5wYZwXODjOC5QXrlt2cmJibGsLEpnskFzp8/rw4dOigsLMzeV65cOW3YsEHlypUjE9LMbDY7vPw5ePCgJKlNmzYUPAEAAAAAAAAAAAAAAKdYs2aNtmzZ4tAXEBCQLWMnz4W4XWJionbu3KmdO3fqww8/1EsvvaQ333xTLi4u6R5j6tSp+u233yRJbm5umj59ukwmU6Zyp9e0adM0adKkTF1j165dCg8Pd1Ii59i1a5fRETLFarVqzpw5kqT58+dr7dq1atKkiUaPHp3qOaGhoTKbzdkVMU/K7c8NjMFzg4zguUFG8NwgvfL6M3P69GnDxs5VxTN///23/vjjDx0+fFhnzpxRVFSUbty4IZvNpiJFiqhEiRIqX768atSooYYNG6pWrVpGR860S5cuqVOnTjp+/Li9z9vbW+vXr1eVKlXIhAxLTEzU3r17JUmNGjUyNgwAAAAAAAAAAAAAAAY7ePCgateubXSMNElMTNTJkyd13333GR3lDpcuXdKIESMc+nr37q2mTZsalOhON27c0Ntvv62tW7dq5cqV8vDwSPO5//zzj15//XV7+9VXX1XNmjWzIiZyIbPZLE9PTyUmJtoLrB566CF5enoanAwAAABAji+e2bFjh4KCgrRmzZp0r3RQunRpdevWTUOGDFHLli2zKGHWuXr1qrp06aIDBw7Y+4oXL65169Y5vTAoMDDQYTWKtm3batOmTYZmQtY6dOiQbty4IQ8PD91///1GxwEAAAAAZKO1a9eqc+fORsdIk6ioKB0/flyNGzc2OgoAAAAAAMjj6tWrp6FDh2rSpEny9fU1Ok6qFi5cqAkTJmjQoEGaOHGi0XEcWK1WDRw4UGFhYfY+T09PTZ06NUvHNZlMat68ubp3766mTZuqVq1aKlmypMxmsywWi/744w+tWrVKc+bMUWxsrP28TZs2qV+/flq+fHmad6B56qmndO3aNUlSzZo19dprr2XJPSF3++OPP3T58mUVL15cDRo0MDoOAAAAAOXg4pn169drwoQJ+v333yVJNpst3dcIDw9XUFCQgoKC1KhRI7377rvq1KmTs6NmmZ49e2r37t0OfWPHjlVkZKSCg4PTda1GjRqpRIkSeTITMib5n62GDRuy9SsAAAAA5DNdu3ZVp06d9NFHH6levXpGx0lRXFycpkyZog8++EBjxoyheAYAAAAAAGS5pKQkzZgxQ/PmzdNTTz2lF198UeXKlTM6lt2iRYv0/vvva+/evUZHSdXLL7+sn3/+2aHvm2++UYUKFbJszIceekhPPPFEqguHli1bVmXLllWPHj30+uuvq1+/ftq+fbv9+9WrV2vatGl69tln7znWzJkz7fNjTCaTpk+fLjc3N+fcSDqNGjVKffr0Sdc5x44dU+/eve3t5EIjI127dk27du2yt5s2bSp3d3cDEznHzJkzJUkDBw7MVfPVcou8+twga/HcICN4bpARPDdIr/z2zBw6dMiwsXNc8UxUVJRGjRqlhQsXSvq3aMZkMmXoesnn79mzR126dFHfvn315Zdf5oqijZR2fsnoiiEhISHy8/PLXCDlzEzImOTimUaNGhmcBAAAAABghODgYDVq1Eg9e/bUa6+9lmOKU65du6avvvpKn332Wbp3IQYAAAAAAHCG69eva8qUKZo2bZoGDRqkp59+Wg0bNjQky9WrV/Xdd99p6tSpOnr0qKSbc2EyOo8mK02dOlWffvqpQ9+4cePUt2/fLB23RYsWaT62fPnyCg4OVvv27fXrr7/a+9955x0NGzZMRYoUSfXc8+fP66WXXrK3hw8frtatW2cstBP4+PjIx8cnU9fw8PBQsWLFnJTIOdzd3XNcpvSyWCz65ZdfJElPPvlkrr+f3CAvPDfIfjw3yAieG2QEzw3SK68/Mx4eHoaNnaO2mzh48KCaNGmihQsXymaz2X/Zv/0X/uTvUvvc6tbzbTabfvzxRzVp0sTQiiUgJ9izZ48k5ZjJUQAAAACA7Ge1WrV8+XI1a9ZMHTt21NKlS5WUlGRIlmPHjmncuHGqWLGiXnnlFZ0/fz5DOxEDAAAAAABkVMuWLR0KU+Lj4zVr1iw1adJETZo00TfffKOLFy9meQ6r1aqNGzdq2LBhKlu2rJ599lkdOXLE4V1JkSJF1KBBgyzPklbz58/XmDFjHPoCAgL0wQcfGBPoLgoVKqS5c+eqQIF/1xyOiIjQunXr7nreM888o+joaEmSr6+vPvroo6yMiVxswYIFSkhIUIMGDVS3bl2j4wAAAAD4fzlm55l9+/apQ4cOslgsku7caSb5BYCHh4caNGigcuXKydPTU8WLF5fJZFJ0dLSio6N1/vx5/fHHH7p69ar93NsLaE6cOKE2bdooJCREDz74YDbdIZBzJCYmKjQ0VBI7zwAAAABAftS/f38tWLDA4X1JSEiIQkJCVLp0aQ0ePFiPPfZYlv/OeOnSJS1btkzz5s2z73Z76y7ENptNZcqUUbt27bI0BwAAAAAAgCRt3bpVc+bM0fjx43XhwgWHdye///67/vjjD40ePVqtWrVSjx495Ofnp4YNGzplF5gLFy4oJCREwcHBWrFihX3+zO3vSmw2mx599FF99tlnKl++fKbHdYZVq1Zp8ODBDsU9jzzyiGbMmJEjd8iRpPvuu089e/bUkiVL7H3r1q1T7969Uzz+p59+0tKlS+3tKVOmqHjx4lmcErnVnDlzJEmDBw82OAkAAACAW+WI4pnz58+rS5cuslgsDr80J/9SXbt2bQ0bNkxdunRRzZo17/mLtc1m0+HDh7V27VrNnDlTf/31l/2c5P+1WCzq0qWL9uzZI19f3yy6s8zJztVVAwMDFRgYeM/jWPE1bzh06JBu3LihokWLqnr16kbHAQAAAABks3nz5umpp57SM888o4MHDzpMBAkPD9dHH32kjz76SJUqVVL37t3l5+entm3bqlSpUpka12q1avfu3faJIFu2bLHvdHP7RBAXFxc9//zzCgwMlLu7e+ZuGAAAAAAAII0GDx6shx9+WB988IH+97//6dq1aw7vTpKSkrRlyxZt2bJFklSsWDHVr19fdevW1YMPPqhKlSqpXLly8vX1lbu7uwoVKiTp5iKXN27c0KVLlxQWFqawsDAdPnxY+/fvV2hoqI4fP27PcOvcjFuLZpo1a6b3339ffn5+2fcHcg8hISHq06ePEhMT7X2dOnXSggUL5OLiYmCye+vQoYND8czhw4dTPfbll1+2/9y9e3c9/vjjWZoNudeBAwe0Z88eFShQQE888YTRcQAAAADcIkcUz/Tv31/h4eF3FM7UqVNH//vf/9SmTZt0Xc9kMqlmzZqqWbOmnn/+eW3btk3PPvusQkNDHcY4d+6c+vfvr5CQEKfdC5Ab7NmzR5LUsGFDmc1mg9MAAAAAAIzQtm1bhYaGaubMmXrnnXcUFhbmMBFEkk6ePKlp06Zp2rRpkqSKFSumOhGkcOHCcnFxUWxsbKoTQQ4ePKjY2Fh7hlsLZpIngkjS448/rrfffpsFHwAAAAAAgCGKFSum9957T2PGjNG7776rmTNn6vr16ykuCHv58mWHYpqU3PreIyW3f3frOxqbzaaGDRtq4sSJ6tmzZ2Zuy+l27typnj17OrzvadGihZYuXSo3NzcDk6VNhQoVHNoXL15M9djo6Gj7z6tXr87QjjqnTp2647w///xT9evXT/e1kHMl7zrTvXt3eXt7G5wGAAAAwK0ML56ZNWuWtmzZ4vCLv8lk0ltvvaXx48erQIHMR2zVqpX27NmjDz/8UBMnTrSPYbPZtGXLFs2aNUtDhw7N9DhAbvH7779Lkho1amRwEgAAAACAkVxcXPTUU09p8ODBmjZtmj777DOHIhrJcfLGqVOndPr0aa1atSrdY6U0CeTW90Fms1m9e/fWxIkTVa9evQzeEQAvLy9FREQoMTFR9evXV0REhIKCgtS9e/e7ngMAAAAAuJOPj4+mTJmiN998U1999ZW+/PJLhYeHS9IdRRDpKY65XUrXMplM6tGjh8aOHZujdppJtm/fPnXt2lUxMTH2vgYNGmjNmjW5ZhdhV1dXh3ZCQoJBSZCbWK1WWSyWFL9LTEy0F888/PDD9oIsLy8vFrcFAAAAcgBDi2cSExP11ltv3TFRYvr06U4vZnFxcdFrr72msmXLavjw4Q4FNG+99ZYGDx6c47eLBZwleecZimcAAAAAAJJUsGBBvfDCC3r22Wf1448/asqUKfbfHdMzEeRuUrtO0aJFNXjwYD3//POqVq1ahq4N4F9ms1ne3t5avXq1IiIiVKpUKT3xxBO5YsVfAAAAAMipSpYsqQkTJmj8+PH6+eefNXfuXK1atcq+48qti4RkxK3vW2rVqqWBAwdq4MCBd+yMklMcPnxYnTp1UlRUlL2vVq1aWrt2rTw9PQ1Mlj7JhVDJ2CUEaWGxWOTj43PP4wICAuw/R0RE8HwBAAAAOYChxTNLlizR6dOn7UUsJpNJH330UZbuAhMQEKDo6GiNHTvW/uLizJkzWrJkifr06ZNl4wI5RWJiokJDQyVJjRs3NjgNAAAAACAnKVCggAYMGKABAwbowIEDmj17thYuXKgzZ87Yj8noRJBbJ4G4ubmpQ4cOGjhwoB5++GEVKlQo09kBOAoKCpIkDRw4kMIZAAAAAHASFxcX9ejRQz169ND169e1YcMG/fzzz9q8ebMOHz4sq9Wa7muWLFlSzZo100MPPaRu3bqpevXqWZDceU6dOqWOHTsqIiLC3lelShWtX78+1xUHbNu2zaF9t2Kl5cuXp3tnmtDQUL300kv2dunSpfX99987HHPfffel65oAAAAAgIwztHjmhx9+sP9sMpnUuHFjjR07NsvHff7557VgwQLt3r3b3jd//nyKZ5AvHDx4ULGxsSpatCgvYQAAAAAAqXrggQc0efJkTZ48Wfv377dPBNm9e7ciIyPTdS1XV1c9+OCD+s9//qOHHnpIHTt2lLu7exYlBxAZGakVK1ZIkoYMGWJwGgAAAADIm4oUKSJ/f3/5+/tLkmJiYvTHH3/oyJEjOnnypMLCwnTlyhVdv35dSUlJKly4sNzd3eXj46NKlSqpatWqqlu3rqpWrWrwnaTd+fPn1aFDB4WFhdn7ypUrpw0bNqhcuXIGJku/6OhoLV682KGvQ4cOqR7ftm3bdI9RoIDjtKxChQqpY8eO6b4OAAAAAMA5DCuesVqt2rhxo8OuM5MnT86WsZPH8vPzs4+/ceNGew4gL9uzZ48kqWHDhjKbzQanAQAAAADkBnXq1FGdOnU0btw4SdLp06fTNRGkVq1aKliwoMF3AeQf8+fPV0JCgho2bKi6desaHQcAAAAA8gUPDw+1adNGbdq0MTpKlrh06ZI6deqk48eP2/u8vb21fv16ValSxcBkGfPSSy8pOjra3nZzc1PXrl2NCwQAAAAAyHKGFc8cPHhQV65csRer+Pr6ZusLhDZt2qhMmTIKDw+XdHMFkAMHDujBBx/MtgyAEX7//XdJUuPGjQ1OAgAAAADIrSpWrKiKFSsaHQNAKmbNmiWJXWcAAAAAAM5x9epVdenSRQcOHLD3FS9eXOvWrVOtWrWcOlZgYKAmTZpkb7dt21abNm1K9fgPPvhAnTp1UqNGjdJ0/cTERL3yyiuaOXOmQ//IkSNVpkyZDGUGAAAAAOQOhhXPHD161P6zyWRSjx49sj2Dv7+/pk+fbm8fOXKE4hnkecnFM2l9cQQAAAAAAIDc488//1RoaKjc3Nz0xBNPGB0HAAAAAJAH9OzZU7t373boGzt2rCIjIxUcHJyuazVq1EglSpRwWrZffvlFr776qlq0aKHHH39cHTp0UM2aNVWggOOUqMuXL2vNmjX66KOPtHfvXofvqlWrpokTJzotEwAAAAAgZzKseObs2bOSJJvNJpPJpGrVqmV7hqpVqzq0z507l+0ZgOyUkJBgfwlE8QwAAAAAAEDeExQUJEnq1auXSpYsaXAaAAAAAEBekNLOLxktNgkJCZGfn1/mAqVgx44d2rFjhySpYMGCKl++vDw9PeXi4iKLxaKTJ0/KarXecZ6vr69+/vlneXl5OT0TAAAAACBnMax45tq1aw5tI7Y+TR7TZDJJkmJiYrI9A5CdDh48qLi4OBUrVkz33Xef0XEAAAAAAADgRHFxcZo3b54kaejQoQanAQAAAADAGHFxcTp+/Pg9j+vWrZuCgoLk4+OTDakAAAAAAEYzrHjm9tUcXF1dsz3D7Vu02my2bM8AZKfff/9dktSwYUOZzWaD0wAAAAAAACC9rFarLBZLit+tWLFCly5dkq+vr+rXr6+LFy9Kkry8vHgXBAAAAADIkyZMmKBatWpp69at+vvvv5WUlHTX4z08PNS1a1eNHj1abdq0yaaUAAAAAICcwLDiGXd3d4f2+fPnsz1DeHi4pJtFMyaTSUWKFMn2DEB22rNnjySpUaNGBicBAAAAAABARlgslnuuiBseHu6w03dERIS8vb2zOhoAAAAAII/KzsVoAwMDFRgYmObjO3XqpE6dOkmSrl+/roMHD+rkyZM6f/68YmJiZLVaVbx4cZUoUUK1a9dWnTp15OLikkXpHfn5+bGQLwAAAADkIIYVzyT/Ba/JZJIknTlzJtsz3D4m27Air0veeaZx48YGJwEAAAAAAAAAAAAAAHCeIkWKqHHjxsyJAAAAAACkyGzUwNWqVbP/bLPZ9Msvv2R7hrVr19qLd27PBOQ1CQkJCg0NlcTOMwAAAAAAAAAAAAAAAAAAAACA/MOwnWfq1q0rNzc3JSQkSJIOHz6sQ4cOqVatWtky/qFDh3To0CF78Yyrq6vq1auXLWMDRjhw4IDi4uLk6elJoRgAAAAAIEcIDw9XfHy8vV2xYkUD0wAAAAAAAKQuNjZWa9eu1apVq3TixAmFh4ercOHCKlOmjJo0aaJevXo5bd5JgwYNtG/fPkmSyWRSYmKiU64LIPO8vLwUEREhSZo4caK+/vpr9e7dW9OnT7/rOQAAAACMZ1jxTMGCBfWf//xHW7ZssfcFBgbqxx9/zJbxAwMD7T+bTCY1a9ZMBQsWzJaxASP8/vvvkqT/Y+++w6Oq8j+Of2ZSIB0ICb2LNLHQXBCkBulSRQEBQVZBFhvoqoiguy7IyipKUxDUFVBAUWkKIaGIEhBFwIALoUiRhDTSCElmfn/wy8iQQspM7iR5v55nns05c+85nwxnMbnc7z2tWrWS2WzYplMAAAAAABd3/vx5rVmzJt8bQfr06SMPD49iz9W7d29uBAEAAAAAAC7vo48+0vPPP2+7YV6SrFarpGvXNDZt2qRZs2apTZs2mj17trp27VrsObPHB+BazGazgoKCZLFY9PXXX0uSxo4dq6CgIIOTAQAAALgZQ++gHzp0qKRrFxKsVqvWrl2rLVu2OH3eLVu2aM2aNbZ5JWnYsGFOnxcw0v79+yVJrVu3NjgJAAAAAMAVZWRk6NVXX1Xjxo31zDPPaPv27Tp58qTS0tIUFxenI0eO6MMPP9TgwYPVsGFDrVixwiHzWq1W2wsAAAAAAMCVWCwWjR49Wo888oguXryY63WM6/v27dunHj166JFHHlFycnKx5jaZTMWND8CJvvvuO507d04BAQHq1auX0XEAAAAAFIChxTOjRo2Sn5+fpD8LaEaOHKkDBw44bc4DBw5o5MiRdhcZfH19NWrUKKfNCbiC7J1n2rRpY3ASAAAAAICrSU1NVZ8+fTRr1iylpaXZbvgwmUy2l/TnzSDnzp3T+PHj1b17d509e7ZYc3MjCAAAAAAAcFVjxozRf//73xzXSfJ6Sdeun3z00Udq1aqVDh8+bPB3AMBZVq9eLUkaNGiQKlSoYHAaAAAAAAVhaPFMpUqVNHnyZLutbOPj49WjRw/t3LnT4fPt3r1bPXv2VHx8vCTZLm488cQTqlSpksPnA0qaxWJRTExMjte5c+d08OBBSVL9+vXt3rNYLAanBgAAAAAYyWq16v7771doaGiuBTPZbrwZxGq1KiwsTHfddZe2bt1qUHoAAAAAAADnWLlypT755JMc10mu32nmxp1orr9ucvz4cbVv357rJkAZlJmZqTVr1kiSHnzwQYPTAAAAACgoQ4tnJOmll15S3bp1bW2TyaSEhAR169ZNkyZNUlJSUrHnSE5O1uTJk9WlSxfFxcXZXdSoU6eOpk+fXuw5AFcQGx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dOktSsWTMNGTIkz/MmTpyYa1GMJLvdEsLDw3M9JrfCmetVrlxZ//jHPyRdK2z65Zdf7N6Pj49XRkaGJKlLly75Fnz5+PjkWCvr1q3TkSNHJEkffvhhjsKZbL6+vrYb2uPi4rR27dp8cwMov268npHX3yu5ufHBGAkJCY6IVGQ3FoHExsaWyM8HpZHVarUVz9x7770GpwEAAAAAACh/sneead++vcFJAAAAAJQlFM+4oO+++06DBw9WrVq1VLt2bQ0ePFhz5sxRWFiYkpKSDM1msVj097//Xe3atdMnn3yi9PT0fI8/d+6cXn31VTVv3lxbtmwpoZSlU3bxTKNGjQxOAgAAUDR16tSx7Wry22+/ad++fTmO2bRpky5evCjp2hP5AwIC8h3zypUr+v333xUZGanDhw/r8OHDOnr0qGrXri1JOnDgwE1zjR8/Ps/36tevr5CQEEnSL7/8YvuZTJKio6N18OBBSdcKOZo2bZrnOL1797YVTYWGhspiseR57JgxY26aubBOnz5tV2RvNptVrVo1PfTQQzpx4oSkaz9nbtiwwVYsVNhsLVq0kI+PjyTZxryZ+Ph4RUVF6ciRI7Y/v+sLxW/88wsMDLQVxKxatUrJyckFmidbdjFVw4YNdffdd+d7bMuWLRUYGCjp2u9gAJCbG/87lZaWVuBzbzw2u/DTKJUrV87Rl5iYaEAS13f8+HFFR0erQoUKBd5pCAAAAAAAAI4RExNj28X5L3/5i8FpAAAAAJQled81BcPs27dPX3zxhdExcvXYY49p6dKlOfq9vb3VqFEjeXl5KTY2VlFRUbJarbb3L168qPvvv1/r169X7969SzJyqZF9E2nDhg0NTgIAQNnn7e1d6JvyCyMmJqbQu8+cPHlSQUFBTkp07XsuCePGjdO3334rSfrkk0/UrVs3u/c/+OADu2NzEx0drbfffltffPGFfvvtN2VlZeU536VLl/LN06BBA1WrVi3fY+6++25t3rxZknTw4EFVr15dkux2RenQoUO+Y0jXnn528uRJXb58WSdPnsyzKPrOO++86ViO4u7urpYtW+qhhx7SE088cdN10KxZszzfM5lMqlKlilJSUnT58uU8j9u1a5cWLlyo0NBQxcTE5DvfjX9+np6eevDBB7VixQrt3btX9erV05AhQ9S9e3e1bdv2pj8rR0RESJKioqLy3bXmRhcuXCjwsQDKlxv/zo6MjFRKSoqtmDA/2X8nZWvXrp0joxVa9i5k18suIoS97KLKv/zlL6pQoYLBaQAAAAAAAMqX7IezNW/eXJUqVTI2DAAAAIAyheKZUsbX19epN3rmZ+3atTkKZ5o3b665c+eqZ8+edk+wjomJ0aJFi/TPf/5TV69elSRdvXpVY8aM0bFjx3J92ml5x84zAACUHJPJVKCbXosqNTW10Of4+Pg4NVNJGTBggKpUqaK4uDh9/vnnevfdd+Xr6yvpWlHMxo0bJV0rGO7SpUuO83fu3KkBAwYU+En4N/usb1Y4I8lWLCPZF3PExsbmekxeatSoYXduXj/XValS5aZjFVbNmjX1zTff2Npms1m+vr6qVq1aoW76vdkaNJuvbV6aV0HTM888o//85z8Fni+3P7/XX39dFotFH3/8seLi4vT+++/r/fffl3Ttz7NXr14aN26c7r333hznRkdHF3jum+UAAEmqXbu2OnTooD179kiS0tPTNX/+fL3wwgv5npeenq633nrLri+/ndBKwq5du+za9erVk6enp0FpXFv2n3du/60BAAAAAACAc+3du1dSwR5sBgAAAACFQfGMC/Pz81Pr1q3Vtm1btWvXTm3bttXJkyfVtWtXQ/LMmjXLrt2mTRuFh4fneoNdUFCQZsyYoY4dO+q+++5TZmampGtFNYsXL77pTSblUXbxDDvPAACA0szT01MPPPCAFi9erKSkJH3++ecaPXq0JOnjjz+2/Vw4bty4HDuDxMfHa8iQIUpMTJSbm5sef/xxDRw4UE2aNFFQUJAqVKhgO+fee+/Vrl277HY7LC3c3NwcPqaHh4duu+02h49bGP/9739thTN169bVM888o06dOqlevXry8/Oz3aAdFRVlKyzK7c/Py8tLH3zwgV566SV9+umnCg8PV0REhJKTk3Xx4kV9+OGH+vDDDzV48GB98sknqlixou3c7PXVokULrV69usDZy0LhGgDnmTNnjjp37iyLxSJJmjFjhmrWrKkxY8bkenxCQoIefvhhRUZG2vr69++v/v37l0jevFy/+5sk9enTp9hjRkdH33SXsRsdP37crp2cnJzvjmYlISUlxa6dvfNM69atDc8G13XjurmxDdyINYOiYN2gKFg3KIrytG6MelAlAKDgsneead++vcFJAAAAAJQ1FM+4oP79+6tnz55q2rSp7cnO2U6ePGlIpqioKB0+fNiub+HChTe9yaxbt24aP368lixZYuv7+uuvKZ7JBcUzAACgrBg1apQWL14sSVqxYoWteGb58uWSru1ektsNx2vWrLHt/PLOO+9o4sSJec4RFxdXoCwXL1686THZP4dJUtWqVW1fBwYG5npMQca5/tzyYuHChZKkgIAA7d27N8/degr6Z3frrbfq5Zdf1ssvv6ysrCz99NNP2rBhgxYtWqTo6Gh9/vnneumll/Tmm2/azgkKCtLZs2eVlJRkeDERgLKjY8eOevfdd/XEE0/IarUqMzNTY8eO1YIFCzR48GA1adJEXl5eunTpkvbu3auVK1fa/V0XEhKiVatWGfgdSJs2bdLOnTvt+saOHVvscRcuXJjjYSuFFRERUaD/zpaU6OhonT17Vm5ubrpy5YrCwsKMjoRSIiIiwugIKGVYMygK1g2KgnWDoijL6+bMmTNGRwAA5CMzM1MHDhyQxM4zAAAAAByP4hkXlP0UZldy7Ngxu3bt2rXVtm3bAp07ZMgQu+KZG58wCiktLU2JiYmSXPPPHwAAoDBatGihu+66Sz/99JPCw8N16tQpXbx4UUeOHJEk3Xfffapdu3aO8w4dOmT7euTIkXmOf/ny5Rw/n+bl5MmTunjxoqpVq5bnMXv37rV9fccdd9i+vv32221f79mz56ZzZR/j7++vBg0aFChfWZL959etW7c8C2ekot184ebmpjZt2qhNmzYaO3asWrRoodTUVK1cudKueKZ169Y6e/aszpw5o9OnT6tevXqF/0YAIBcTJ05UkyZNNGXKFNt/z/bt22d7CmZuGjZsqOeee04TJkzI8XCUkhQXF6fHHnvMrm/gwIFq166dQYlc26+//irp2vWZ63c3AwAAAAAAgPOdOnVKaWlpqly5sm699Vaj4wAAAAAoY4z7l3uUKjc+HbpOnToFPrdu3bp27YSEBEdEKlOyn4heuXJlBQQEGJwGAAAUV2BgoKKjowv1Kms7lYwaNUqSZLVatWLFCtuuM5I0fvz4XM/JzMy0fZ2SkpLn2AsXLrQ79maWLVuW53unTp3S1q1bJV0rlrm+6CM4OFh33XWXJCk8PFxHjx7Nc5xvvvnGtktkSEiIoTdJGyX7zyS/P7vMzEwtWrSoWPPUr19fTZo0kSTFxMTYvTdkyBDb13Pnzi3WPABwo27dumnfvn2aOnWq3Nzc8j22bt26mjp1qkaMGGHofxMsFotGjRqls2fP2voCAgI0f/58wzK5uuziqObNmxucBAAAAAAAoPzJ/reY9u3bl8t/awEAAADgXOw8gwK5saAjLS2twOfeeGzVqlUdkqks+eOPPySpXD6hHACAsshsNisoKMjoGIYaMmSIpk+frrS0NC1fvty2y17VqlU1YMCAXM/JLoiQrhW8TJ8+PccxoaGhmjlzZqGyvP766+rZs6fatGlj13/lyhWNHTvWVvTx5JNP5jj36aef1ujRo2WxWPTwww9r+/bt8vPzszvm/Pnzevzxx+3OKY+aNGmigwcPavfu3Tp27Jjdn6d07QbuyZMn6/Dhw3mOcerUKZ06dUr9+/fP95jIyEhJOXdtfOihh/T666/r6NGjWrBggVq0aKGJEyfmOdbVq1f18ccfq1+/fvnuTgQAkrR48WK9+uqrunDhwk2PPXPmjCZNmqTp06dr7ty5GjduXAkkzGnatGnavHmzXd+SJUsK9VCU/EyaNEnDhg0r1DnHjx/XwIEDbe127dqpWbNmDslTVCkpKbad0bJ3nnnooYfUtWtXI2PBxV2/bqRra9nHx8fARHB1rBkUBesGRcG6QVGUp3WTfV0JAOCajh07Jula8QwAAAAAOBrFMyiQO++8064dGRmplJSUAl00vf5Cq3TtYivsZe88U79+fWODAAAAOEhAQID69++vzz77TGfOnLH1P/zww/Lw8Mj1nOHDh+ull15SamqqXnnlFZ04cUJDhw5VjRo1dOHCBX3xxRf68MMPFRgYqEqVKtn+ASU/jRs31qVLl9S5c2c99dRTuu++++Tt7a1Dhw7pzTfftD1dvkuXLnrkkUdynD9q1Ch9+umn2rhxo/bv36877rhDU6dOVZs2bZSVlaXvvvtOc+fOVXR0tCRpypQpuueee4rykZV648aN05NPPqnU1FR17txZ06ZNU9u2bVWxYkUdOXJEixYt0r59+3Tvvfdq586duY5x9uxZDRo0SA0bNtSAAQPUrl071atXT15eXrp06ZJ++OEHLVq0SFeuXJF07fO+nru7uz7//HN16NBBCQkJmjRpklauXKlRo0bptttuk4+Pjy5fvqzffvtNe/bs0Zdffqm4uDj973//o3gGQJ4yMjL00EMPad26dba+KlWqaPLkyerXr58aN24sHx8fXbp0Sfv379fy5cu1fv16Wa1WxcXFafz48YqMjCzxHbHmz5+vefPm2fU999xzGj58uMPmCA4OVnBwcLHG8PX1lb+/v4MSFU9CQoLOnTsnk8mkkJAQl8mF0sHHx4c1g0JhzaAoWDcoCtYNiqIsrxtfX1+jIwAA8nH9zjMAAAAA4GgUz6BAateurQ4dOmjPnj2SpPT0dM2fP18vvPBCvu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Image found in database 02-tbp-diluent-h2o-air-results-clean.md. ![ Species Generation Rate Density @ 40.0 C Steady-State plot.](data:image/png;base64,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Image found in database 02-tbp-diluent-h2o-air-results-clean.md. ![ Organic Phase Steady-State 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```text ========================================================================================== ``` ## User: 12. What are the top 5 pressing technical questions I should ask about this work? ```text ========================================================================================== ``` ## Assistant: ### Top 5 pressing technical questions 1. Are the mass‑transfer parameters and thermodynamic partitioning data (Henry constants, partition coefficients, and phase mass‑transfer coefficients k_{pq,i}) documented, physically justified, and validated against experiment or literature for the TBP–diluent–H₂O–air system? 2. Is global mass bookkeeping and unit consistency verified (phase inventories, units g·L⁻¹ vs g, and any implicit pools), and can the reported H₂O decrease be fully traced through species / phase changes in the simulation output? 3. How exactly is "stage efficiency" defined and computed (numerator, denominator, time/window used), and how sensitive are efficiency values and rankings to that definition and to the diagnostic averaging (mean ± std)? 4. Which model parameters dominate prediction uncertainty (reaction τ values, residence time, inflow relative humidity, kLa/Henry constants), and is there a targeted sensitivity / uncertainty analysis (local or global) to quantify their impact on key outputs? 5. Are the numerical integration and coupling choices (fixed Δt tied to mean τ, odeint settings, port synchronization, and monitor diagnostics) robust for long runs and finer spatial/temporal resolution — and what are the computational cost and reproducibility implications for larger parametric sweeps or scale‑up? ```text ========================================================================================== ``` ## User: 13. What is your evaluation of the content of this usecase? Feel free to consult the Supporting Information sections of the database. ```text ========================================================================================== ``` ## Assistant: ### Evaluation of Use‑Case 02 content Below I summarize strengths, weaknesses/gaps, noteworthy aspects, and prioritized recommendations based on the provided Use‑Case 02 (TBP‑Diluent‑H$_2$O‑Air Mixing) reports and supporting sections. I consulted the Stage KB fragments (aqueous/organic/vapor reports, stage diagnostics, parameter sweep summary, and runtime logs) when preparing this evaluation. - Strengths / positive aspects - The KB provides complete stoichiometries for the reaction mechanism (organic hydration complexes and phase-transfer steps) so mass‑balance structure is explicit. - Stage mass‑balance residuals are reported and are effectively zero (10⁻¹⁶–10⁻¹⁷ g·L⁻¹·s⁻¹), demonstrating numerical conservation checks are implemented and pass. - Time‑history outputs for species and stage efficiency (mean ± std) and plots are available for start‑up and longer runs, enabling dynamic analysis and parametric comparisons. - A parametric study (4‑case τ / RH sweep) was executed and summarized, showing sensitivity behavior and giving actionable insight (tradeoffs between mean efficiency and variance). - The Stage code exposes clear methods (step, RHS, inflow updates, port calls) and prints useful run diagnostics (mass flows, residuals, elapsed wall time), which help reproducibility and debugging. - Key weaknesses / important data gaps - Missing numeric transfer/thermodynamic parameters: the KB does not expose explicit rate constants, Henry/partition coefficients, or kLa/interfacial‑area values used for gas–liquid/organic–aqueous transfer. That prevents rigorous thermodynamic checking or independent replication of kinetics. - Concentration units and some quantity metadata are not consistently recorded (KB notes: “Info: null‑quantity‑info” and recommendations explicitly request concentration units). Units ambiguity undermines absolute comparisons and mass tracing across phases. - Stage efficiency definition is not fully documented numerically (exact numerator/denominator and time/window used for averages), making cross‑case comparisons and interpretation ambiguous. - Some numeric inconsistencies/bookkeeping flags: the reported large drop in aqueous $H_2O(a)$ mass is not fully explained by the small increases in $N_2$/$O_2$ vapor pools, suggesting either missing pools, unit mismatches, or reporting from different diagnostics. - Reaction kinetics appear represented by relaxation/τ style parameters rather than mechanistic rate laws; τ values are present in the workflow but not traceable to physical mass‑transfer or reaction coefficients. - Computational cost is nontrivial (start‑up ≈ 718 s, steady continuation ≈ 1237 s in logs) — scaling parametric sweeps could be expensive without optimization. - Noteworthy / high‑value observations drawn from the KB - Water phase exchange ($H_2O(a) \!<=>\! H_2O(v)$) consistently shows higher steady efficiency (~54%) than diatomic gas exchange (≈50%), implying either stronger driving force or different transfer parametrization for water. - Organic‑phase hydration reactions (monomer/dimer/trimer adduct formation) reach ~50% efficiency, indicating significant but incomplete extraction of H$_2$O into TBP complexes under the modeled conditions. - The τ sweep reveals a practical operational tradeoff: very small τ yields higher mean extraction but larger variability; an intermediate τ (≈0.5×) offers a good compromise — a useful process‑design insight. - Short‑term recommended actions (low effort, high impact) - Add explicit metadata for concentration units (g·L⁻¹, mol·L⁻¹, mass vs. mass fraction) for every time series and inflow/outflow vector. - Record and expose the numerical definitions used for stage efficiency (formula and time window) and the exact equality checks used for mass residuals. - Provide the Henry/partition constants and any kLa or interfacial area values used. If unknown, document the assumption (e.g., τ‑relaxation surrogate) clearly next to each reaction entry. - Medium/long‑term recommendations (model improvement & validation) - Replace or augment τ‑style relaxation terms with physically derived kinetic expressions where possible: explicit kLa·a, gas‑side film coefficients, or mechanistic reaction rate constants for organic complexation, plus temperature dependence. - Run mass‑tracing diagnostics that output cumulative mass fluxes per species and per phase (integrated transfers) to reconcile the apparent aqueous H$_2$O loss and confirm closed‑system accounting. - Perform targeted experimental validation for partitioning (TBP–H$_2$O complexation equilibria) and gas solubility (Henry constants) at the operating temperature to anchor the model. - Expand the sensitivity/uncertainty study into a designed global sensitivity analysis (Sobol or Morris) to rank parameter importance quantitatively. - Suggested verification / sanity checks before new studies - Recompute a closed mass audit: sum of all phases + cumulative outflow should match cumulative inflow ± numerical tolerance; if not, identify reporting vs. modeled pool mismatches. - Confirm time units and volume normalization used for the "Total mass generation rate density" diagnostics so residuals are interpreted correctly. - Cross‑check stage efficiency computation by recomputing it from raw throughput/transfer integrals for one case to eliminate diagnostic normalization artifacts. - Overall judgment (concise) - The usecase is well organized and includes many useful diagnostics and parametric runs that give actionable insights into stage behavior and operational tradeoffs. However, critical physical parameters (units, partition/Henry data, kLa or rate constants) are missing or not exposed, which prevents rigorous thermodynamic/kinetic validation and full reproducibility. Fixing those metadata and parameter exposures will elevate the work from a qualitatively informative study to a quantitatively reproducible engineering model. If you want, I can: - Produce a prioritized checklist (concrete file/attribute names and exact diagnostics to add) that you can hand to a model developer for rapid augmentation. - Attempt a targeted mass‑audit using the KB time series (if you provide the raw time‑series tables or point me to the exact dataset ids in the usecase table). ## AI Discussion 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:** 165369 + **Discussion history size:** 26 + **Embedded images:** 5