Hydrothermal liquefaction of a wastewater native <i>Chlorella sp</i>. bacteria consortium: biocrude production and characterization
Bibliographic record
Abstract
Along the recent developments of biofuel production, the hydrothermal liquefaction (HTL) of microalgae appears as a major process step which could take advantage of converting the whole microalgae content instead of only extracting its lipid content. This study presents the HTL conversion of a microalgae Chlorella sp. bacteria consortium using a batch type reactor. The experiments were carried out using a full factorial experimental design approach, where the main chosen factors were the temperature, the residence time at maximum temperature and the dry-matter-to-water ratio. The biocrude oil is the targeted product characterized along this work; its production and its composition were studied. The yields of biocrudes approach other microalgae studies using pure strains of microalgae and batch-type reactors. Maximum yields (37%daf) and lowest O/C ratio (0.167) were obtained at the highest severity conditions (310 °C for 55 min and 0.15 dry-matter-to-water ratio). The energy return on energy invested (EROEI) of the process has been determined. The simulated distillation by thermogravimetric analysis (Sim-Dis-TGA) was conducted on the different obtained biocrude and no significant effect was observed with respect to five evaporation ranges among the studied conditions. GC-MS characterization of biocrude presents the 10 relatively most abundant components for each studied condition.
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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 | 0.000 |
| 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 | 0.000 | 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".