Combined Production of Biofuels from Locally Grown Microalgae
Bibliographic record
Abstract
The purpose of this study was to assess the potential of combining the production of three types of biofuels (i.e., biodiesel, bioethanol, and biogas) from Nannochloropsis sp. microalgae. Cellulase enzyme, produced from Trichoderma viride fungi was used to convert algal hydrocarbons into sugars for bioethanol production and to improve lipids extraction for biodiesel production. The procedure for producing the three biofuels consisted of initially mixing predetermined quantities of cellulose with the algae to hydrolyze the complex hydrocarbons and produce sugars, followed by oil extraction from the hydrolyzed algae, and finally producing biogas through co-digesting the hydrolyzed and oil-extracted algae residual with wastewater treatment sludge. Without enzyme, the total sugar yield following hydrolysis for 6 hours with distilled water at 37°C and pH 5 reached 38 mg/g algae. Using the enzyme, total sugar production increased in the range of 42 mg/g algae to 74 mg/g algae with increasing enzyme use. Similarly, the reducing sugar production increased from 36 mg/g algae without enzyme to 60 mg/g algae with increasing enzyme dose. Following hydrolysis, the lipids yield reached 59% of the extracted algae weight, which represents almost all of the lipids in the algae. The residual algae mixed with sludge generated significant amounts of biogas in excess of the quantities generated by sludge alone. The results demonstrated the technical feasibility of producing three biofuels from algae; however the economic aspects of the process require significant further careful consideration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".