Evaluation of the effects of AlkylPhenolic compounds on kinetic parameters in a moving bed biofilm reactor
Bibliographic record
Abstract
Abstract Alkylphenols (APs) received great attention in the past decade because they were on the priority hazardous substances list. The kinetics of a lab‐scale moving bed biofilm reactor (MBBR) that was fed with synthetic wastewater containing 4‐NonylPhenol (4‐NP) and 4‐tert‐OctylPhenol (4‐t‐OP) was investigated in this paper. The MBBR reactor was evaluated under different APs, organic loading rates, and hydraulic retention times (HRT). The substrate removal rate was predicted with the first‐order, second‐order, Stover‐Kincannon, and Monod substrate removal models. 4‐NP and 4‐t‐OP pollutants were removed in the different percentages of 87.1 to 99.9 % and 83.2 to 99.9 %, respectively. Biokinetic parameters, like Y, KS, k, μmax, and kd, that would be favourable to design an MBBR were evaluated. Based on the results, the second‐order (Grau), Stover‐Kincannon, and Monod models were observed to be the most suitable for this reactor. These models showed high correlation coefficients of about 99.6, 99.1, and 92.9 %, respectively. Consequently, these models could be utilized in anticipating the performance and design of MBBR reactors.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".