Efficient Bio-Oil Production from Coconut Shells Using Parabolic Solar Pyrolysis
Bibliographic record
Abstract
The study provides valuable insights for the potential of coconut shell as a renewable energy source for bio-oil production using parabolic solar pyrolysis The primary objectives of this study were to assess the efficacy of parabolic solar pyrolysis in bio-oil production, analyze coconut shells and biochar, conduct pyrolysis experiments, identify parameters influencing pyrolysis, carry out performance tests, and analyze the composition of bio-oil compounds.The research methods used include energy analysis, optimization of parabolic solar pyrolysis systems, performance calculations, analysis of GC/MS of bio-oil compounds, and the assessment of biochar.The proximate analysis of the coconut shells revealed that they consisted of 47.37% fixed carbon, 1.89% ash, 11.32% moisture content, and 77.8% volatile matter.The heating rate during the process was in the range of 5-150℃/min.The factors affecting the performance of parabolic solar pyrolysis include the light radiation intensity, parabolic area, receiver, and reactor material.The temperature generated by the solar pyrolysis concentrator was in the range of 300-650℃.The study found varied bio-oil, biochar, and gas yields influenced by the particle size and pyrolysis temperature.The main compounds in the bio-oil were phenol, furfural, cresol, creosol, syrigol, guaiacol, pentadecanoic acid, and carbonyl compounds.
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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 | 0.000 |
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".