Forecasting the performance of membrane bioreactor process for groundwater denitrification
Bibliographic record
Abstract
A mathematical model was developed for performance prediction, simulation, and design of membrane bioreactor (MBR) process for biological denitrification of two groundwaters. The process mass transfer chronologically involved the following aspects: biological reaction in bulk liquid phase, film transfer from liquid phase to biofilm, biofilm diffusion and biological reaction, adsorption at the biofilm–adsorbent interface, and adsorbent particle diffusion. The liquid film and biofilm transport equations constituted a time-varying moving boundary problem. Model biokinetic parameters for denitrification using ethanol as electron donor were estimated from batch reactor studies. Laboratory-scale MBR experiments showed that steady state was reached rapidly and over 99% nitrate removal was consistently achieved for influent concentrations in the 16 to 45 mg/L range. Furthermore, comparison of experimental data and model predictions established the accuracy, reliability, and utility of the model. The MBR experiments also demonstrated that powdered activated carbon (PAC) did not particularly improve nitrate removal or permeate flux. Model sensitivity studies illustrated the dependence of process dynamics on parameters related to influent nitrate concentration, biomass concentration, biodegradation kinetics, and hydraulic residence time. Key words: bioreactor, membrane bioreactor, denitrification, biodegradation, membrane filtration, water treatment, membrane fouling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".