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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".