Operational Optimization and Mass Balances in a Two-Stage MBR Treating High Strength Pet Food Wastewater
Bibliographic record
Abstract
A two-stage membrane bioreactor (MBR) system was evaluated for the treatment of high strength pet food wastewater characterized by oil and grease, chemical oxygen demand (COD), biochemical oxygen demand (BOD)5 , total suspended solids (TSS), total Kjeldahl nitrogen (TKN), NH4–N , and TP concentrations of 2,800, 25,000, 10,000, 4,500, 1,650, 1,300, and 370mg∕L , respectively, to meet stringent surface discharge criteria of BOD5 , TSS, and NH4–N of <10mg∕L , and TP of <1mg∕L . Pretreatment of the dissolved air flotation effluent with FeCl3 at a dose of 3.5g∕L , corresponding to a Fe:P molar ratio of 1.3:1 affected TP, TSS, volatile suspended solids (VSS), COD, BOD5 , and TKN reductions of 88, 72, 75, 11, 11, 36, and 17%, respectively. The two-stage MBR operating at a total hydraulic retention time of 5.3days comprising 2.5days in the first stage and 2.8days in the second stage, and solids retention time of 25days in the first stage consistently met the criteria despite wide variations in influent characteristics. Very high COD and BOD5 removal efficiencies of 97.2 and 99.8% were observed in the first stage, with an observed yield of 0.14gVSS∕gCOD . A modular approach for the quantification of simultaneous nitrification denitrification (SND) in the first-stage MBR was developed and verified experimentally. The model indicated that on average, 21% of the influent nitrogen was removed by SND and predicted nitrogen loss with an accuracy of 72%. Complete nitrification of the residual organic nitrogen and ammonia was achieved in the second-stage MBR.
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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".