Engineering Application of MBR Process to the Treatment of Beer Brewing Wastewater
Bibliographic record
Abstract
According to the characteristics of beer brewing wastewater, Up-flow Anaerobic Sludge Bed and Membrane Bio-Reactor integrated technics (UASB+MBR) was applied. This paper investigated various operating parameters during the process of wastewater treatment in MBRs. The sludge loading and the membrane fouling were analyzed by detecting the sludge concentration and sludge characteristics. The results showed that when CODCr, NH4-N, T-P and T-N concentrations of the feed water were 500~1000mg/L, 20~30mg/L, 0.6~14mg/L and 19.5~41.1mg/L, respectively, it got some conclusions in the process.(1)The CODCr , NH4-N , T-P and T-N of MBR effluent could reduce to 40mg/L, 2.3mg/L , 0.3mg/L, 3mg/L, respectively. The quality of the effluent water in this system met the reuse of urban recycling water—Water quality standard landscaping water according to GB/T18921-2002; (2)The DO of the aerobic pool should be controlled at the range of 2~4mg/L, which could increase the removing efficiency of the NH4-N. (3)Appropriate adjustments to the volume of sludge and maintain the sludge concentration of membrane pool at 6~8g /L, which could reduce the velocity of membrane fouling. (4) With 1000mg/L of sodium hypochlorite and 2000mg/L hydrochloric acid alternate cleaning, the recovery of membrane flux can maintain above 95%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".