Chemical conditioning approach to post-treat temperature-phased anaerobic digestate to improve resource recovery, odour reduction and biosolids quality
Bibliographic record
Abstract
Biosolids has several challenges, such as its high water content, huge volume, odour, and pathogen presence. Regulations require biosolids to be reused and disposed of safely. Polymer conditioning focuses on volume reduction, leaving pathogen and odour reduction unaddressed. This study evaluates the use of polymer alone and in combination with ferric chloride (FeCl3) and hydrogen peroxide (H2O2) at pH 8.0 to increase the post-treatment efficiency of temperature-phased anaerobic digestate (TPAD). The goal is to reduce volume, recover phosphorus, reduce odour, and eliminate pathogens. This investigation examined various dewatering indices after treating TPAD with cationic polymer alone, polymer and FeCl3, and with polymer, FeCl3, and H2O2 combined at pH 8.0. A combination of 2.5 g/kg dry solids (DS) polymer, 2.1 g/kg DS FeCl3 and 600 mg/l H2O2 at pH 8.0 produced the shortest capillary suction time (CST) of 11.5 s, lowest turbidity of 11 NTU, and lowest specific resistance to filtration (SRF) of 0.08 Terra m (Tm)/kg. Compared to raw TPAD, the combined chemical dose improves dewatering by 99%, odour reduction by 90%, 100% centrate P removal, and a 40% increase in cake solids with 57 MPN/g DS fecal coliforms in the treated cake. There was a 100% reduction in pathogens compared to raw cake. TPAD must be post-treated to reduce volume and odour while producing P rich ‘class A’ biosolids with a greater range of reuse.HighlightsA combination of polymer, FeCl3, and H2O2 at pH 8.0 improves TPAD post-treatment.Enhancing TPAD dewatering by 99% reduction in CST and SRF.Combined chemical treatment increases TPAD cake solid content by 40%.‘Class A’ biosolids production with less than 57 MPN/g DS fecal coliform.Phosphorous recovery, odour reduction with polymer, FeCl3 and H2O2 treatment at pH 8.0.
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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".