Determination of the hydraulic residence time in a trickling biofilter filled with organic matter
Bibliographic record
Abstract
Biofiltration process using peat as media has been shown to be efficient for the treatment of agroindustrial, chemical and municipal effluents. However, determining the hydraulic retention time of this process is difficult due to many factors. Generally tracer techniques are used, but they measure the molecular retention time instead of the hydraulic retention time; and depending on the case, the results can be wrong. In order to contribute to solving this problem, the objective of this research was to find a trustworthy and adequate technique to determine the hydraulic retention time for a process using a very adsorbent filter bed material (peat). An overview of the hydraulic behaviour of a peat bed filter related to their particular structural composition is presented. Then, based on these characteristics, two different techniques to determine the hydraulic retention time were developed and tested. The first, an indirect modified tracing technique and, the second, a direct one which determines the volume of liquid in treatment into the filter bed through the establishment of a relation between the volume of porosity and the volume of empty spaces (or not occupied by liquid) into the filter bed. The results obtained showed that the proposed techniques gave similar results and proved adequate.
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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 | 0.001 |
| 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 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".