Biocrude production by hydrothermal liquefaction of olive residue
Bibliographic record
Abstract
Hydrothermal liquefaction (HTL) converts biomass into a crude bio-oil by thermally and hydrolytically decomposing the biomacromolecules into smaller compounds.The crude bio-oil, or biocrude, is an energy dense product that can potentially be used as a substitute for petroleum crudes.Liquefaction also produces gases, solids, and water-soluble compounds that can be converted to obtain valuable chemical species or can be used as energy vectors.The process is usually performed in water at 250°C-370°C and under pressures of 4-22 MPa: depending on the adopted pressure and temperature the process can be carried out in sub-critical or super-critical conditions.In the conditions reached in hydrothermal reactors, water changes its properties and acts as a catalyst for the biomass decomposition reactions.One of the main advantages of this process is that the energy expensive biomass-drying step, required in all the thermochemical processes, is not necessary, allowing the use of biomass with high moisture content such as microalgae or olive residue and grape mark.In this work, the feasibility of a hydrothermal process conducted under sub-critical conditions to obtain a bio-oil from the residue of olive oil production is investigated.The experimental tests were performed at 320°C and about 13 MPa, using a biomass to water weight ratio of 1:5.The influence of two different catalysts on the bio-oil yield and quality was investigated: CaO and a zeolite (faujasite-Na).CaO allows the increase of bio-oil yields, while the selected zeolite enhances the deoxygenation reactions, thus improving the bio-oil quality in terms of heating value.
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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) | 0.001 | 0.001 |
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".