Experimental validation of hindered settling models and flux theory applied in continuous flow process for harvesting <i>Acutodesmus obliquus</i>
Bibliographic record
Abstract
ABSTRACT Batch settling and flux theory were used to predict the best working conditions in a continuous flow process for havesting Acutodesmus obliquus using a tannin‐basis flocculant. From batch tests, a settling velocity model was proposed. Three different models were tested: two exponential functions; and one power‐law function. The power‐law function presented a lower variance (1.356 × 10−9) and R2 equal to 0.995. This suggested model was applied in flux theory, and the system performance was predicted by feed flowrate and initial microalgae concentration in continuous flow process. In experimental tests, a pilot settler (0.045 m3) was used to validate the proposed model. Nine different experimental conditions of feed flowrate and initial microalgae concentrations were used to evaluate the settling behaviour of the unit. The flux theory predicted better results when the applied solids flux was near limiting the solids flux. Experimental results showed that in the proposed working condition of 10−5 m3 s−1 feed flowrate and 58 kg · m−3 initial microalgae concentration, the highest biomass removal was 98.3 %, and sludge concentration profiles could stabilize over time. Overall, these results demonstrate a potential application of the batch tests and theory flux to predict a continuous flow process, considering biomass purification.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".