Enhancement of fast pyrolysis oil fuel properties through co-pyrolysis and improved analysis
Bibliographic record
Abstract
Fast pyrolysis is a thermochemical process converting biomass into fast pyrolysis biooil (FPBO, 50-75 wt%), non-condensable gases (13-16 wt%), and biochar (12-20 wt%) at 450-550°C in an inert atmosphere with short residence times and high heating rates. FPBO is a complex organic mixture of lignocellulose degradation products with high water (20-30 wt%) and oxygen content (35-40 wt%) causing chemical instability and corrosion to storage tanks and burners. In this work, the improvement of FPBO quality was investigated through co-pyrolysis of forestry residues with waste mussel shells and through improved understanding of phase behaviour and composition of forestry based FPBO using an advanced distillation curve analysis. Co-pyrolysis with waste mussel shells was studied by: (1) direct contact with the forestry residues in the reactor and (2) contacting only the hot vapours with the mussel shells at the reactor exit. The impact of temperature, residence time, mussel shell loading, and type of contact (operational mode) on the FPBO and biochar were studied. There was a reduction in FPBO oxygen and acid content through dehydration and decarboxylation with mussel shell addition and an increase in biochar pH and functionality (O- and N-containing functional groups) for soil amendment and adsorption applications, respectively. The FPBO phase behaviour was studied using an advanced distillation method and a model developed to simulate the distillation curves of the whole FPBO. The 17 surrogates used in the model to represent the range of functional groups and boiling points of FPBO components showed a good fit of simulated and experimental distillation curves and some bulk properties. GC analysis of the vacuum distillate fractions concluded six distillable steps as a basis for chemical separation procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".