High cell density culture of microalgae in horizontal thin-layer algal reactor: Modeling of light attenuation and cell growth kinetics
Bibliographic record
Abstract
This study delved into the light intensity effects and light attenuation modeling in high cell density culture (HCDC) of green alga Neochloris oleoabundans. The research primarily focused on how different light intensities influence cell growth in terms of cell division and cell mass, and the biochemical composition within the cells. Recognizing the need to avoid overestimating light path by excluding data point in the data zone where the light intensity was zero, we proposed and verified a novel modified Beer-Lambert model, which was superb in fitting experimental data and predicting light attenuation in both low and high cell density cultures. Taking advantage of the prediction power of the modified Beer-Lambert model, we devised an approach to maintain constant mean light intensity (Imean) and by adjusting the incident light intensity (I0). The results indicate that at an Imean of approximately 66.67 μmol/m2/s and 6 mm culture thickness, maximum volumetric and areal biomass productivities of 2.72 g/L/day and 16.32 g/m2/day, respectively, were achieved. Whereas the highest biomass concentration of 28.20 g/L was produced in 20 days at Imean 50 μmol/m2/s. Photoinhibition to cell division become evident at I0 750 μmol/m2/s and above. Using the data generated at constant Imean in the constant specific growth rate range, superb fitting to the Monod model with a R2 of 0.9910 was demonstrated, highlighting the importance of generating reliable data for the modelling of the kinetics of photoautotrophic growth of microalgae, which had considered to be challenging.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".