Fractionation of Oil Palm Fiber Using Green Solvent for Lignin Separation
Bibliographic record
Abstract
Oil palm processing produces more than 70-wt% of its lignocellulosic content as by-product, the bulk of which is empty fruit bunches (EFB). EFB contains cellulose, hemicellulose, and lignin which makes it a potential source of bio-based chemicals. This research explores the utilization of ethanol as a potentially green, sustainable, and low-cost organic solvent (organosolv) for EFB fractionation. Organosolv processes target extraction of lignin (delignification). Conventional delignification use an acid hydrolysis process with lignin yields of approximately 18-wt%. In this study the EFB was treated in 2 stages, (1) soaking EFB for 1 hour followed by (2) delignification using ethanol as the organic solvent under variable process conditions. Temperature (140°C, 170°C), liquid-to-solid ratio (L/S-w/w) (6:1, 15:1), and wt%-ethanol (20-wt%, 50-wt%) were varied while residence time was constant at 30 minutes (experiments were run in duplicate). Data analysis using 2k Factorial Design Method showed the significant variables were temperature, L/S-ratio, wt%-ethanol, interaction of L/S ratio and temperature, temperature and %wt-ethanol interaction, and L/S-ratio and %wt-ethanol. The optimum operating conditions (170°C, 15:1, 20wt%-ethanol) produced a lignin yield of up to 31%wt. This preliminary study shows ethanol in an organosolv process is a potential delignification option.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".