Dynamic Simulation of Trickling Filter Process with Hydraulic Stress Tests
Bibliographic record
Abstract
Trickling filter process is focused on developing Asian countries in recent years as an alternative energy-saving wastewater treatment to conventional activated sludge systems. This paper investigated influential hydraulic operational parameters on the process performance using a nitrification reactor. The trickling filter process was expressed as 2-dimensional biofilm layers where the influent flowed to the bottom of the filter bed while oxygen was dissolved from the ambient air. The 4 m-height pilot-scale trickling filter filled with tubular plastic media (1.5 cm × 1.5 cm, 371 m2/m3-reactor volume) was installed at a municipal wastewater treatment plant in Da Nang, Vietnam. During the 2-day hydraulic stress test, the nitrogen and hydraulic loadings were changed in a step-wise manner. Focusing on the nitrification which was a dominant oxygen uptake, the process performance was dynamically simulated with a modification of IWA Activated Sludge Model (International Water Association). The effluent concentrations of dissolved oxygen and inorganic soluble nitrogenous compounds were reasonably calculated under the hydraulic loading ranging between 2.4 and 18 m/h. The reactive wetted surface area of the filter seemed to increase when high linear velocity of the fluid was applied, which resulted in the increase of volumetric biological reaction rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".