Effect of Thermal Pretreatment on the Solubilization of Organic Matters in a Mixture of Primary and Waste Activated Sludge
Bibliographic record
Abstract
The increased demand for advanced techniques in anaerobic digestion over the last few years has led to the employment of various pre-treatment methods prior to anaerobic digestion to increase gas production. These pre-treatment methods alter the physical and chemical properties of sludge in order to make it more readily degradable by anaerobic digestion. Although the thermal pre-treatment presents high energy consumption, the main part of this energy to heat can be recovered from the biogas produced in the anaerobic process. In this research a mixture of primary and waste activated sludge was thermally pretreated at 100, 125, 150, 175 and 200 oC in order to determine the reaction kinetics for the increase of soluble organic fraction (expressed as CODs and VFAs). Experimental results proved that the solubilization of sludge is a 1st order reaction with respect to both CODs and VFAs, KCODs (reaction rate constant of CODs solubilization) increased from 4.59*10-3 (min-1) to 7.55*10-3 (min-1) as the temperature increased from 100 to 200 oC, with a reaction activation energy of 7447.21 (J/mole) and frequency factor of 0.051 (min-1), While KVFAs (reaction rate constant of VFAs solubilization) increased from 5.33*10-3 (min-1) to 7.97*10-3 (min-1) for the same increase in temperature, with a reaction activation energy of 5947.22 (J/mole) and frequency factor of 0.0364 (min-1).
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".