Ammonia recovery via stripping from hydrothermal liquefaction aqueous from sludge for anaerobic co-digestion pretreatment
Bibliographic record
Abstract
Hydrothermal liquefaction (HTL) is a thermo-chemical method of processing wastewater solids that could be preferential over anaerobic digestion (AD) because it produces a greatly reduced volume of solids. Instead, the effluent can be separated into biocrude refined into fuel, hydrochar a carbon-rich solid and HTL aqueous waste. Unfortunately, HTL aqueous is high in ammonia, phenolics, and nitrogen heterocyclic compounds that can inhibit AD if used for treatment. Ammonia stripping was tested for pretreatment of HTL aqueous from dewatered mixed sludge at 350 °C, 15 min and recovering ammonia. Seven stripping reactor conditions were run, and the best results were seen at 85 °C, a pH of 9.3 and 500 mL/min air flow rate. This case achieved ammonia and total phenolic compounds removal of 79.5 ± 0.1 and 32 ± 1 %, respectively, in 4 h. The ammonia stripping was coupled with acid adsorption to produce an ammonium sulphate salt with fertilizer value. The ideal ammonia stripping conditions produced a salt with a purity of 98.0 ± 0.05 % and represented an ammonia recovery of 73.9 ± 0.04 %. Semi-continuous flow bench-scale thermophilic anaerobic co-digestion of HTL aqueous with municipal sludge found that 12 % of influent chemical oxygen demand (COD) can come from HTL aqueous without inhibition if pretreated with ammonia stripping, while the digester fed with a similar COD from non-pretreated HTL aqueous showed inhibition. Under mesophilic conditions, semi-continuous flow anaerobic co-digestion was successful for non-pretreated and pretreated HTL aqueous even with 24 % and 22 % of influent COD provided by HTL aqueous, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".