A comparative evaluation of design factors on bubble column operation in photosynthetic biogas upgrading
Bibliographic record
Abstract
Studies attempting to optimise photosynthetic biogas upgrading by simultaneous investigation of the bubble column-photobioreactor setup have experienced considerable variability in results and conclusions. To identify the sources of such variation, this work quantitatively compared seven design factors (superficial gas velocity; liquid to gas flow rate (L/G) ratio; empty bed residence time; liquid inlet pH; liquid inlet alkalinity; temperature; and algal concentration) using the L16 Taguchi orthogonal array as a screening design of experiment. Assessments were performed using the signal to noise (S/N) ratio on the performance of CO2 removal (CO2 removal efficiency, CO2 absorption rate, and overall CO2 mass transfer coefficient) and O2 stripping (O2 concentration in biomethane and O2 flow rate in biomethane). Results showed that pH and L/G ratio were the most critical design factors. Temperature and gas residence times had minimal impact on the biomethane composition. The interactive effect between pH and L/G ratio was the most impactful, followed by the interactive effects between superficial gas velocity and L/G ratio and pH on CO2 removal efficiency. The impact of L/G ratio, algal concentration, and pH (in that order of impact) caused up to a 90% variation in oxygen content in biomethane. However, algal concentration had a diminishing role as the L/G ratio increased. Using only the statistically significant main effects and interactions, the biomethane composition (CO2% and O2%) was predicted with over 95% confidence through regression equations for superficial gas velocity up to 0.2 cm/s.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".