Desenvolvimento de uma ferramenta computacional para a modelagem e simulação de processos oxidativos avançados.
Bibliographic record
Abstract
In this work, a computational tool for the modeling and the parameter estimation of advanced oxidation processes is proposed. The tool, developed in Matlab , contains four main modules: process modeling module (Module I), two modules dedicated to the model parameter estimation from available experimental data (Modules II and III) and the simulation module (Module IV). It is based on a general phenomenological structured model formulated in Module I. In Modules II and III, the objective function is minimized by using the trustregion-reflective algorithm implemented in the lsqnonlin Matlab function and the particle swarm optimization method, respectively. When using the trustregion-reflective algorithm, the model differential equations and forward sensitivity equations are integrated employing the CVODES solver of SUNDIALS. The sensitivities are used to compute the analytical Jacobian matrix of the residuals that is supplied to the optimization algorithm. When the optimization problem is solved by means of particle swarm optimization method, the model equations are integrated by using the ode15s Matlab solver. Finally, in Module IV, process simulations with the estimated parameters or parameters supplied by the user can be carried out. The proposed tool was applied to the phenol degradation by electro-Fenton process. Process simulations demonstrated that the model with the adjusted parameters was able to predict relatively well the experimental concentrations of phenol and its aromatic intermediates. The tool was also applied to the treatment of ciprofloxacin (CIP) in aqueous solution by ozonation process. The model parameters: volumetric mass transfer coefficient (kLa), liquid holdup () and kinetic constants were estimated by neglecting and considering the ozone concentration in the gas phase in the objective function. The model with the adjusted parameters
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".