Effect of pyrolysis temperature on chemical and physical properties of sewage sludge biochar
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of pyrolysis temperatures (300, 400, 500, 600 and 700°C) on properties of biochar produced from an urban sewage sludge. Biochar yield significantly decreased from 72.5% at 300°C to 52.9% at 700°C, whereas an increase in temperature increased the gas yield. Biochar pH and electrical conductivity increased by 3.8 and 1.4 dS m⁻¹, proportionally to the increment of temperature. Biochar produced at low temperatures had higher total nitrogen and total organic carbon content but a lower C/N ratio, calcium carbonate equivalent, and total P, K and Na contents. Total and diethylene triamine penta acetic acid (DTPA)-extractable concentrations of Fe, Zn, Cu, Mn, Ni, Cr and Pb increased with increment of temperature. Lower DTPA-extractable concentrations of Fe, Zn, Cu, Mn, Ni and Pb were found in biochars compared to the sewage sludge. Pyrolysis decreased bulk density, whereas particle density and porosity increment was observed upon pyrolysis with increment of temperature. Sewage sludge saturated water content (θs ) was 130.4 g 100g⁻¹ and significantly greater than biochar, but biochar θs significantly increased with temperature (95.7 versus 105.4 g 100g⁻¹ at 300 and 700°C, respectively). Pyrolysis decreased the biochar's water repellency, assessed by molarity of ethanol droplet (MED), compared to the sewage sludge. The lowest MED of 0.2 and water repellency rating of 3 were found for the biochar produced at 700°C. Based on our results and considering the energy consumption, pyrolysis temperature in the range of 300-400°C may be suggested for sewage sludge pyrolysis.
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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".