Vermicomposting Smart Closed Reactor Design and Performance Assessment by Using Sewage Sludge
Bibliographic record
Abstract
Abstract This study aims to design a smart closed reactor of vermicomposting to convert sewage sludge and any organic waste to high-quality vermicompost. In this reactor design, all aspects of growth and reproduction of Eisenia Fetida worms, such as aeration, temperature, light, and moisture, were considered. We analyzed the physicochemical, bacterial, and microstructural of produced vermicompost and growth rate of worms in a substrate of 70% sewage sludge, 20% cow manure, and 10% sugarcane bagasse in a container and the smart reactor. The results show that vermicomposting in the smart reactor took 50% less time and 30% more worm growth rate to produce the same quality as in a container. After vermicomposting in the reactor, the parameters of pH, fecal coliform, phosphorus, organic matter, and C/N decreased whereas the parameters of carbon, nitrogen, nitrate, ammonia nitrate, and EC increased, slightly. Although, the EC amount of the reactor production is more than the container one, the amount of moisture, phosphorus, and organic matter of the vermicompost in the container is more than the reactor one. Based on the odor absorption and leachate elimination of this reactor, we recommend that it be utilized for vermicompost production, including out of smelly organic wastes such as sewage sludge, even in any public zone and personal houses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".