Increasing productivity of <i>Spirulina platensis</i> in photobioreactors using artificial neural network modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although production of microalgae in open ponds is conventionally practiced due to its economy, exposure of the algae to uncontrollable elements impedes achievement of quality and it is desirable to develop closed reactor cultivation methods for the production of high value products. Nevertheless, there are several constraints which affect growth of in closed reactors, some of which this study aims to address for the production of Spirulina. Periodic introduction of fresh medium resulted in increased trichome numbers and improved algal growth compared to growth in medium that was older than 4 weeks in 20 L polycarbonate bottles. Mixing of the cultures by bubbling air and use of draft tube reduced the damage to the growing cells and permitted increased growth. However, there was better growth in inclined cylindrical reactors mixed with bubbling air. The oxygen production rates were very similar irrespective differences in the maintained cultures densities. The uniformity in oxygen production rate suggested a tendency towards homeostasis in Spirulina cultures. The frequency of biomass harvest on the productivity of Spirulina showed that maintenance of moderate culture density between 0.16 and 0.32 g/L resulted in about 14% more productivity than maintaining the cell density between 0.16 and 0.53 g/L or 48% more than by daily harvest above 0.16 g/L. An artificial neural network based predictive model was developed, and the variables useful for predicting biomass output were identified. The model could predict the growth of Spirulina up to 3 days in advance with a coefficient of determination >0.94.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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 it