Characterization of Adansonia Digitata (Baobab Wood) Bio‑Oil and Biochar Produced Using a Fixed-Bed Tubular Reactor
Bibliographic record
Abstract
Characterization of Adansonia digitat from pyrolysis in a fixed bed tubular carbon steel reactor between 400 to 700°C temperature range was investigated. The physicochemical properties of the raw biomass were determined, thereafter, the quality of the bio-oil and biochar yields for bioenergy and industrial applications were investigated using Higher Heating Value (HHV), Lower Heating Value (LHV), Fourier Transform Infrared Spectroscopy (FT-IR) and Scanning Electron Microscopy (SEM). Optimum bio-oil (52.70wt.%) and bio-char (40 wt.%) yields were obtained at a temperature of 500 0 C and 400 0 C respectively. GC-MS analysis of bio-oils showed a higher percentage of oleic acid, phenol, methanol, and ketone, fuel properties, and chemical composition of bio-oil as Carbon (70.99%), Hydrogen (13.40%), Nitrogen (0.54%), Oxygen (15.01) and sulfur (<0.06%), flash (81) and pour points (-7) bio-oil with HHV (30.75 MJ/kg) and LHV (27.83 MJ/kg), bio-char physicochemical properties show the percentage of Carbon (C), Hydrogen (H), Nitrogen (N), Oxygen (O) and sulfur (S) as the ultimate analysis results are 60.30, 24.21, 0.27, 15.20 and 0.02 respectively, while (8.70%), volatile matter (14.22%), Fixed carbon (75.47%) and ash content (1.60%) are the proximate analysis with HHV of 27.50MJ/kg, bio-char SEM result shows cloudy and cloggy formation with rapid efflorescence during pyrolysis process. The results of this analysis demonstrated that the properties of products can serve as a waste management strategy, sorbent, soil enhancer, and biofuel industry applications.
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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".