Treatment of Effluent of Upflow Anaerobic Sludge Blanket Bioreactor for Water Reuse
Bibliographic record
Abstract
The low-pressure reverse osmosis (LPRO) process is a recent development of reverse osmosis (RO) technology for the reduction in RO energy consumption and operation cost. The goal of this study was to investigate the performance of LPRO processes for the treatment and reuse of effluent discharged from brewery upflow anaerobic sludge blanket bioreactors (UASB). In this study, three different commercially available LPRO membranes were tested to evaluate the water quality that can be achieved under different operational and pretreatment conditions. It was found that the filtration performance and the effluent quality of the LPRO membranes can be considerably affected by the operation conditions and the selection of the pretreatment processes. The ultrafiltration (UF) pretreatment and the control of the operation pressure were found to be essential for mitigating LPRO membrane fouling, which could be caused by Ca2+ associated precipitates and organic gelation, in the treatment of the brewery UASB effluent. Water quality analyses showed that an integrated process of the UASB + UF + LPRO could achieve an effluent quality characterized by concentrations of 10.4–12.5 mg/L of chemical oxygen demand (COD), 1.8–2.1 mg/L of total nitrogen (TN), 1.3–1.8 mg/L of ammonia nitrogen (NH3-N) and 0.8–1.2 mg/L of total phosphorus (TP). The effluent quality and the LPRO performance could be further improved by adding a granular activated carbon (GAC) adsorption process between the UF and LPRO processes, which reduced the concentration of COD to 7–10 mg/L and those of TN, TP, NH3-N to below 1 mg/L. For the treatment of the UASB effluent tested in this study, the UF, UF + GAC (retention time 4 hrs), UF + LPRO, and UF + GAC + LPRO, respectively, achieved overall COD removal efficiencies of 89.6–93.7%, 94.5–96.7%, 99.3–99.1% and 99.3–99.4%; TN removal efficiencies of 73.0–78.2%, 89.2–97.2%, 97.1–98.2% and 94.3–99.7%; and TP removal efficiencies of 29.3–46.2%, 77.0–95.4%, 95.9–97.6z% and 98.0–98.3%. This study showed that both UASB + UF + LPRO and UASB + UF + GAC + LPRO are effective treatment processes for treating brewery wastewater toward reuse water quality standards set by the United States Environmental Protection Agency (US EPA). Therefore, the results of this study would help to answer whether a LPRO can treat the brewery UASB effluent to meet the requirements of wastewater reuse standards.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".