Effect of organic loading rate on the performance of a submerged anaerobic membrane bioreactor (SAnMBR) for malting wastewater treatment and biogas production
Bibliographic record
Abstract
Abstract BACKGROUND Most malting plants discharge their wastewater to the sewer system and have to pay high discharge fees. Malting wastewater is rich in nutrients and contains high soluble COD (mostly sugars and organic acids). Hence, it is suited for anaerobic treatment without nutrients addition and can produce biogas simultaneously. The main objective of this study was to investigate the treatability of malting wastewater in a submerged anaerobic membrane bioreactor (SAnMBR) and biogas production under mesophilic temperature conditions (36 ± 1 °C) and variable organic loading rates (OLRs). RESULTS As the OLR was increased from 1.36 to 3.18 kg COD m‐3 d‐1, the COD removal efficiency decreased from 94.1 ± 2.5% to 90.2 ± 1.4%, the effluent COD increased from 283 ± 121 mg L‐1 to 506 ± 68 mg L‐1, and the biogas production yield decreased from 0.345 ± 0.007 to 0.308 ± 0.025 L g‐1 CODremoved. The BOD5 removal efficiency was consistently above 99%. Methane accounted for 70.9 ± 2.0% of the biogas. Membrane permeability measurements, scanning electron microscopy (SEM), and energy dispersive X‐ray (EDX) spectrometry indicated that the membrane fouling that occurred during operation of the SAnMBR could be removed by a series of physical and chemical cleaning steps. CONCLUSIONS Malting wastewater was successfully treated using a SAnMBR for the first time. The SAnMBR adapted quickly to both gradual and sudden changes in OLR. © 2017 Society of Chemical Industry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".