Leaching Characteristics of Inorganic Constituents from Oil Palm Residues by Water
Bibliographic record
Abstract
Oil palm residues are not currently suitable as feedstock for thermal energy generation because their high ash content can cause slagging, corrosion, and fouling. A water leaching treatment is a potential strategy to reduce the ash content in these residues. This study evaluates the effects of the duration and temperature of water leaching on two types of oil palm residues, namely, empty fruit bunches (EFBs) and palm kernel shells (PKSs). The optimum process duration for ash removal from EFBs was found to be 5 min, as the effect of convection on scrubbing was observed to remove substantial ash from the substrate during this period. A cross-flow model with estimated kinetic parameters of water leaching for EFB and PKS was developed and showed that three leaching stages of EFB achieved the greatest ash reduction from 5.47% to 2.63%. A low ash content of PKS showed no value for ash removal in any leaching process. Although there was no significance in the total ash reduction due to temperature effects, the leaching treatment was found to be most effective in reducing potassium, from 2.42% to 0.69% and 0.36% at 25 and 55 °C, respectively.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".