Chloroform aerobic cometabolic biodegradation in a continuous‐flow reactor: Model calibration by means of the gauss‐newton method
Bibliographic record
Abstract
Abstract Chlorinated solvents are toxic and poorly biodegradable pollutants frequently found in contaminated aquifers. Experimental data of chloroform (CF) aerobic cometabolic biodegradation in a sand column with butane as growth substrate were simulated with a system of non‐stationary second‐order partial differential equations with non‐linear kinetic terms. A MATLAB optimization code based on the Gauss‐Newton method and coupled with the Comsol Multiphysics finite elements solver was developed to calibrate the model. For each experimental phase, the best‐fit quality was evaluated by an innovative multi‐variable model adequacy test. The proposed code solved systems of up to 5 partial differential equations and optimized up to 6 unknown parameters, leading to statistically acceptable best‐fits. The optimization of the butane/oxygen pulsed feed led to an 82 % CF biodegradation and to a 0.27 gCF/gbutane transformation yield. When the substrate/pollutant ratio was minimized, the standard model of aerobic cometabolism initially tested required additional terms aimed at taking into account the depletion of reducing energy, in order to attain a statistically acceptable best‐fit. This is the first work in which a model of aerobic cometabolism taking into account reducing energy availability was applied to a continuous‐flow process. The proposed optimization code can be used for model calibration in a wide range of physical problems described by non‐stationary, non‐linear partial differential equations, a task that no commercial software can perform. The developed code is made available in the Supplementary Material.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".