Novel Membrane Pretreatment to Increase the Efficiency of Ozonation-BioOxidation
Bibliographic record
Abstract
The effects of membrane pretreatment on the ozonation and ozone-biotreatment of the wastewater from the alkaline bleach plant of Kraft pulp mills were investigated. Membrane pretreatment involved using a ceramic membrane with nominal cutoff of 1,000 g/mol, that separated organics based on their molar mass and structure. The retentate stream, consisting of concentrated high molar mass organics, was treated in an ozonation bubble column contactor. Different parameters including chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total carbon (TC), pH, color, and ozone concentrations in the gas and liquid phases were monitored. The pretreatment process separated the low molar mass and more biodegradable constituents of the alkaline bleach plant wastewater. Hence, more effective removal of high molar mass and recalcitrant organics was achieved in the subsequent ozonation stage. Also, the biodegradability of the wastewater during the ozone oxidation increased significantly (by up to 200%) with the implementation of membrane pretreatment. The ozone demand and consumption for improving the quality of wastewater (i.e., BOD5 enhancement and TC, COD, and color removal) increased by up to about 10-fold compared to the control process involving standalone ozonation. Furthermore, membrane pretreatment reduced the consumption of ozone per unit COD removal from the alkaline effluent.
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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) | 0.001 | 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 | 0.001 | 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".