Hydrothermal treatment and recycling of organic by-products from sludge – confirmation of by-products availability for biological nutrient removal
Bibliographic record
Abstract
The main objective of this study was to confirm the technical feasibility of using hydrothermal processing of sludge to combine partial treatment and generation of useful organic by-products for internal recycling to support biological nutrient removal (BNR). The experimental program involved assessing hydrothermal sludge treatment and production of organic by-products containing volatile fatty acids, especially acetic acid, under a variety of reaction temperatures, durations, and uses of oxidant. Three sludge types were treated; primary, secondary, and a mixture of the primary and secondary. The organic by-products were then used to provide the main carbon source to support BNR in two short sets of sequencing batch reactor experiments. The results confirmed the bio-availability of the by-products to support simultaneous biological phosphorus removal and denitrification. The BNR microorganisms readily used the dissolved by-products, especially acetic acid, for active biological phosphorus release and uptake and denitrification. Mass balance analysis confirmed that hydrothermal generation of by-products from sludge can be adequate for internal recycling in BNR wastewater treatment plants. Key words: wastewater sludge, hydrothermal treatment, recycling of organic by-products, organic carbon source, volatile fatty acids, acetic acid, biological nutrient removal, phosphorus removal, denitrification.
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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) | 0.000 | 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".