Beneficial reuse of precast concrete industry sludge to produce alkaline stabilized biosolids
Bibliographic record
Abstract
The precast concrete industry generates waste called concrete sludge during routine mixer tank washing. It is highly alkaline and hazardous, and typically disposed of by landfilling. This study examined the stabilization of municipal sewage sludge using concrete sludge as an alkaline agent. Sewage sludge was amended with 10 to 40% of concrete sludge by wet weight, and 10 and 20% of lime by dry weight of the sludge mix. Mixes containing 30 and 40% of concrete sludge with 20% lime fulfilled the primary requirements of Category 1 and 2 (Canada) biosolids of maintaining a pH of 12 for at least 72 hours. The heavy metals were below Category 1 regulatory limits. The 40% concrete sludge mix was incubated at 52 degrees C for 12 of the 72 hours to achieve the Category 1 and 2 regulations of less than 1000 fecal coliform/g solids. The nutrient content of the biosolids was 8.2, 10 and 0.6 g/kg of nitrogen, phosphorus and potassium respectively. It can be used as a top soil or augmented with potassium for use as fertilizer. The study demonstrates that concrete sludge waste can be beneficially reused to produce biosolids, providing a long-term sustainable waste management solution for the concrete industry.
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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) | 0.001 | 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".