Targeted Removal of Dissolved Organic Matter in Boiler-Blowdown Wastewater: Integrated Membrane Filtration for Produced Water Reuse
Bibliographic record
Abstract
The efficacy of coagulation and membrane filtration was studied for the treatment of boiler-blowdown (BBD) wastewater to enable reuse and minimize the overall water consumption in steam-assisted-gravity-drainage (SAGD), thermally enhanced, oil recovery operations. Direct nanofiltration of chemically unadjusted BBD at its original pH was the optimal treatment option with respect to the flux stability and the removal of dissolved organic material and salinity, which if not removed would result in the fouling and failure of downstream process equipment. The naturally high solute hydrophilicity allowed for prolonged operation with an elevated flux of 60 L m −2 h −1 (LMH) and recovery up to 85% while maintaining solute removal as high as 80% and 45% for dissolved organic carbon and total dissolved solids, respectively. Comparatively, neither precoagulation nor preacidification improved the rejection of dissolved organic material or salinity and consistently resulted in increased membrane surface fouling and flux decline. The proposed filtration treatment solution would result inasmuch as a 4-fold reduction in the volume of makeup water required and BBD wastewater disposed compared to a conventional SAGD facility.
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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) | 0.000 | 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 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".