Effect of Organic Loading Rate on the Biological Performance of the Thermophilic Anaerobic Membrane Bioreactor Treating Pulp and Paper Primary Sludge
Bibliographic record
Abstract
Abstract Waste-to-energy or value-added products have been increasingly considered in many pulp and paper mills (PPMs) worldwide. However, developing appropriate conversion technologies is a major challenge in transforming PPMs wastes into biofuels or value-added biomaterials. In the present study, a long-term (320 d) anaerobic digestion of primary sludge of a thermomechanical pulp mill (TMP) was carried out for the first time in a thermophilic anaerobic membrane bioreactor (ThAnMBR). Effect of organic loading rate (OLR) in the range of 2.5–6.8 kg-COD/m3 d and hydraulic retention times (HRT) of 3–8 d on the process performance was investigated. Under various OLRs, stable biogas productions were obtained, and the best results were achieved with lower OLR (2.5 kg-COD/m3 d) and higher HRT (8 d), at biogas yields of 189 L biogas/kg MLSS fed. However, it was found that biogas production and sludge biomass degradation decrease when the organic loading rate increases. The proportion of sludge reduction ranged from 28.9 to 46.7% depending on the applied OLRs. Despite varying OLRs, stable membrane performance was obtained, where the required membrane flux was easily maintained during the reactor operation. In this study, also the properties of digestate and membrane permeates were studied under different operating conditions, and they fluctuated to some extent with OLR. ThAnMBR is a promising new technology for pulp and paper mill primary sludge treatment.
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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 | 0.000 |
| 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 | 0.001 |
| 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".