Optimization of pyrolysis conditions for production of rice husk-based bio-oil as an energy carrier
Bibliographic record
Abstract
Bio-oil is an eco-friendly energy source with potential to substitute fossil-derived fuels. This study optimized pyrolysis conditions for production of bio-oil from rice husks. Response surface methodology based on central composite design was employed to maximize bio-oil yield and high heating value (HHV) while minimizing water and ash contents. The pyrolysis process conditions were; temperature (400–650 °C), heating rate (6000–9750 °Ch-1), and holding time (600–1800 s). Analysis of variance revealed that the linear model best fits the responses of bio-oil yield and water content. On the other hand, the quadratic model best fits the responses of HHV and ash content. Pyrolysis temperature had the greatest influence on each of the studied responses, followed by holding time and lastly heating rate. Optimum pyrolysis conditions were found to be; temperature (650 °C), heating rate (9750 °Ch-1), and holding time (1800 s), leading to bio-oil yield, HHV, water and ash contents of 38.13%, 23.40 MJ/kg, 18.27%db and 0.16%db, respectively. These results fall in the range of standard quality values for bio-oil in published literature where >15 MJ/kg, 20–30%, 0.15–0.25% are the recommended ranges for HHV, water and ash contents, respectively. Results from the FTIR spectroscopy revealed that phenolic compounds contributed the most to bio-oil composition. Phenolic compounds positively influenced the quality of bio-oil due to their high calorific values. Gas chromatograph and mass spectrometry results showed peaks continuing to spill up to the maximum retention time indicating good thermal stability and bio-oil quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".