Catalytic pyrolysis of pine needles: Role of zeolite structure and <scp>SiO<sub>2</sub></scp>/<scp>Al<sub>2</sub>O<sub>3</sub></scp> ratio on bio‐oil yield and product distribution
Bibliographic record
Abstract
Abstract Renewable and sustainable energy production has gained significant attention to meet sustainable development goals (SDGs). Pine needles, an abundant typical forestry residue, can be used as a renewable biomass source for sustainable energy production. Pyrolysis is a well‐established and commercialized technique for the thermochemical valorization of lignocellulosic biomass. The present work focuses on improving the bio‐oil yield by introducing SiO2‐Al2O3‐based catalysts, including different zeolites and SiO2‐Al2O3 materials with varying SiO2‐Al2O3 ratios, during the pyrolysis. Bio‐oil yield increased from 45.2 wt.% to 47.2 wt.% with the introduction of SiO2‐Al2O3 catalysts and increased to 51.2 wt.% and 50.6 wt.% with HZSM‐5 and Y‐zeolite, respectively, and decreased to 40.0 wt.% with β‐zeolite catalyst. The pyrolysis experiments of physically mixed biomass and catalyst were carried out in a fixed‐bed down‐flow reactor. Various process parameters such as temperature, retention time, and catalyst‐to‐biomass ratio were examined to evaluate their effect on product yield. The catalyst's introduction slightly decreased phenolic compound content, enhancing carbonyl and hydrocarbon production. Maximum improvement in bio‐oil yield by 6 wt.% was achieved using an H‐ZSM‐5 catalyst at 450°C temperature and 30 min residence time with a catalyst‐to‐biomass ratio of 1:4.
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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".