Effect of C:N Ratio on Microbial Activity and N Retention: Bench-scale Study Using Pulp and Paper Biosolids
Bibliographic record
Abstract
The effect of C:N ratio on the performance of bench-scale composting systems treating pulp and paper biosolids was investigated. The biosolids used were obtained from the Pine Falls Paper Company located in Manitoba. The biosolids, on a wet basis, consisted of 41% primary biosolids, 21% secondary biosolids, and 39% deinking plant sludge. The biosolids were mixed with bark to achieve an initial moisture content of 60%, resulting in a recipe consisting of 1.09 kg of bark per kg of biosolids on a dry basis. Four reactors (treatments) were run with C:N ratios of 107 (control; no N supplement), 55, 29, and 18. Each treatment was replicated three times. Sulfur coated urea was used as the N supplement. Parameters monitored included C:N ratio, N recovery, material compaction, temperature, qualitative odor observations, and volatile solids reduction. The relative microbial activity was observed in-directly using volatile solids removal and the relative heat generation data. The data showed a strong negative linear relationship between C:N ratio and relative heat generation (r2=0.98) and between C:N ratio and volatile solids removal (r2=0.84 for all four treatments; and r2=1.0 for C:N = 29 to 107). The data also showed a strong nonlinear relation between N retention and C:N ratio (% retention = 101(1-0.92C:N); r2 = 0.71; n = 12). Qualitative odor observations and N losses suggested that a C:N ratio of 18 was too low, therefore a performance comparison was made between the C:N-107 and the C:N-29 treatments. It was observed that the mean volatile solids removal was 28.6% higher in the C:N-29 treatments as compared to the C:N-107 control. While this difference is significant from a bench-scale perspective, the authors question the practical significance of the difference and recommend further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".