Influence of phosphorus, potassium, and copper on methane biofiltration performance
Bibliographic record
Abstract
In addition to a carbon source, bacteria require for growth a variety of nutrients such as phosphorus, potassium, and several other micronutrients including copper. The study described in this paper was conducted with the aim of determining the influence of phosphorus, potassium, and copper on methane elimination in a biofilter. The study revealed that the particular phosphorus concentration leading to the greatest methane elimination capacity, which was 44·7 g m −3 h −1 at a methane inlet load of 75 g m −3 h −1 , was 3·1 g/L. The influence of the phosphorus concentration on the methane elimination capacities was also investigated for methane inlet loads of between 8 and 95 g m −3 h −1 . The optimum range of the nitrogen – phosphorus mass ratios, determined during this study ranged from 0·5 to 2·5. It was established that, in comparison with phosphorus, potassium does not seem to be a determining element for the biological removal efficiency and does not significantly affect the microorganisms’ behaviour. However, a concentration of 0·076 g/L of potassium is recommended in the irrigation nutrient solution for an inlet load of 75 g m −3 h −1 . The influence of the copper concentration was also studied by varying its concentration between the values of 0 and 0·006 g/L. The results have also shown that copper has a minor impact on the biofiltration of methane. This paper is the first report describing the influence of several nutrients in a biofilter. The knowledge provided by this study is necessary for the achievement of a biofilter indebted to methane control.
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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 | 0.000 | 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) | 0.000 | 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".