Screening of a wide range of pre-treatments for improving the algal biomass solubilization and biogas potential
Bibliographic record
Abstract
The microalgal biomass conversion into methane as a biofuel offers the best energetic balance among the different biomass–to-biofuel scenarios for microalgae containing less than 40% lipids. The anaerobic degradation limitation of algae at around 50% emphasizes the need for pre-treatment to obtain higher methane production from algae. This study was performed using Scenedesmus sp. AMDD, green microalgae, as a model strain. Over 20 series of different pretreatments were evaluated, alone, or in sequence. The enzymatic pretreatments were performed with pectate-lyase and cellulase at incubation time from 2 to 24 hours. Chemicals pretreatments were done with H2SO4, NaOH or H2O2, at 0.2N and 2N and 2 to 24 h of reaction time. Thermal treatments were completed in an oven or a pressure vessel at 121 – 180°C or using a microwave (175 – 300°C). The enzymatic hydrolysis of Scenedesmus sp. AMDD followed with a three hours incubation in NaOH 0.2N resulted into a 75% solubilization. Similar results were found with incubation in 0.2N NaOH followed with short thermal treatment. Caustic and thermal pretreatments improved the methane production by around 12% compared with the anaerobic digestion of untreated algal biomass, at 335 ± 28 ml CH4 STP/g volatile solid (VS) added. The results from the enzymatic pretreatment were less encouraging with improvement of 2-7% of the methane production only. However, a combination of enzymatic with a thermal treatment successfully solubilized up to 75% of Scenedesmus sp. AMDD biomass. The resulting methane production, although up to 15% higher than for the control biomass, did not fully correlate with the increased dissolved organic matter. In anaerobic digesters continuously fed with solubilized biomass after combined enzymatic and thermal pre-treatment, the CH4 yield was improved by up to 35% in some operational conditions, while the degradation rate was faster, allowing for lower retention time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".