Maximum microalgae biomass harvesting via flocculation in large scale photobioreactor cultivation
Bibliographic record
Abstract
Abstract This study evaluated the ability of Tanfloc SG flocculation to recover microalgae biomass cultivated in a tubular photobioreactor using swine wastewater effluent as the culture media in a pilot‐scale microalgae production plant. The objective function was the flocculation efficiency (ηf), which was evaluated by central composite design (CCD) experiments which varied the Tanfloc concentration (TC) and pH. Subsequently, the biomass recoveries of highly efficient flocculants recommended by the literature and the CCD conditions of Tanfloc were compared. The maximum flocculation efficiency (96.7 ± 1.0 %) was obtained for the following optimal conditions: 210 mg/L Tanfloc concentration, pH 7.8. After jar test experiments, the scale‐up of the process was performed by using the best obtained results and applying Tanfloc in a 1 m3 flocculator where the complementary analyses demonstrated efficient nitrogen, carbon, and biomass removal. The flocculation efficiency obtained with Tanfloc was equivalent to that of most conventional flocculants currently used. However, Tanfloc presented the following economic advantages with respect to other flocculants: i) its nontoxic nature allows for low‐cost disposal; and ii) its low market price makes it a promising alternative for harvesting microalgae biomass.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".