The Effects of Process Control Strategies on Composting Rate and Odor Emission
Bibliographic record
Abstract
Poor compost quality and odor emission are often significant problems in the composting industry. Composting process control can potentially help reduce both of these problems. In spite of the recent development of a number of process control strategies, very few direct comparisons have been made between these, particularly in terms of compost quality and odor emission. To help address this need, a series of experiments were conducted to evaluate the effects of several in-vessel process control strategies on organic matter conversion, nitrogen transformation and pH, and odor emission. The strategies focussed on aeration control. Fixed aeration, temperature feedback, oxygen feedback, and combined temperature/oxygen feedback algorithms were tested. A modified algorithm called linear temperature feedback was also developed and tested. Results showed that the compost temperature profiles were quite similar for the various feedback control algorithms, whereas fixed rate aeration led to significantly higher temperature, as expected. Compost properties such as C:N ratio and organic matter loss were also similar between process control methods. However, oxygen content was maintained more consistently using oxygen feedback or linear temperature feedback algorithms. Linear temperature feedback is preferable to oxygen feedback in that it does not require oxygen sensors to operate. Mass emission rates of odorous gas (methyl mercaptan and dimethyl sulfide) were typically found to increase with higher aeration rates, such as those used to limit temperature, though the gas concentration was lower. For maximum retention of nitrogen, adequate supply of readily biodegradable carbon in the feedstock is vital.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".