Mathematical Modeling of Hollow-Fiber Membrane System in Biological Wastewater Treatment
Bibliographic record
Abstract
A set of mathematical models were derived based on the bio-kinetics and material balance principles to describe the performance of membrane system in this research. A synthetic wastewater and a meat packing wastewater were processed through a lab-scale membrane bioreactor system to generate experimental data for calibration and verification of the derived models. For the synthetic wastewater treatment, a high and stable Total Organic Carbon (TOC) removal was achieved with volumetric organic loading from 0.2 to 24.2 kg TOC/m3ƒ(d). It was found that the derived system models fit the experimental data well. The bio-kinetic coefficients of k, Ks, Y and kd in the models were found to be 0.16 d-1, 1.0 mg/L, 1.75 mg Mixed Liquor Volatile Suspended Solids (MLVSS)/mg TOC and 0.11 d-1, respectively. For the meat packing wastewater treatment, the bio-kinetic coefficients of k, Ks, Y and kd were found to be 0.48 d-1, 56.3 mg/L, 0.53 mg MLVSS/mg COD and 0.04 d-1, respectively. F/M ratio of 0.08 was found to be the proper operating condition for the system. Based on the proposed system models, the optimum MLSS concentration and F/M ratio can be computed to yield minimum cost of a membrane bioreactor system without excess biomass production.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".