Using a Statistical Model to Examine the Effect of COD: SO42− Ratio, HRT and LA Concentration on Sulfate Reduction in an Anaerobic Sequencing Batch Reactor
Bibliographic record
Abstract
Taguchi statistical design, an orthogonal array (OA) method, was used to study the impact of the COD/SO42− ratio, hydraulic retention time (HRT) and linoleic acid (LA) concentration on sulfate (SO42−) reduction in an anaerobic sequencing batch reactor using glucose as the electron donor. Based on the OA, optimum condition for maximum SO42− reduction was evaluated. Increasing the COD/SO42− ratio and HRT caused decreasing SO42− reduction while increased SO42− reduction was observed with increasing LA concentration (1 g L−1). In control (not fed LA) cultures, higher SO42− reduction (87% ± 3%) was observed at a low COD/SO42− ratio of 0.8. This indicates that increasing SO42− reduction was observed at increasing SO42− loading rates. In general, results from this study reveal that limiting the substrate concentration with high SO42− levels (low COD/SO42− ratio) favors high SO42− removal. Surface plots were used to evaluate the significant interactions between the experimental factors. Accuracy of the model was verified using an analysis of residuals. Optimum conditions for maximum SO42− reduction (97.61%) were observed at a COD/SO42− ratio of 0.8 (level 1), 12 h HRT (level 1) together with 1000 mg L−1 LA addition (level 3). In general, the Taguchi OA provided a useful approach for predicting the percent SO42− reduction in inhibited mixed anaerobic cultures within the factor levels investigated.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".