Experimental study on the FBC chamber for analysis of temperature and combustion efficiency using palm oil biomass fuel
Bibliographic record
Abstract
Renewable energy sources such as palm oil biomass solid waste have attracted the interest of scientists. This is because palm oil biomass is suitable for conversion to heat energy so that the dependence on conventional fossil energy can be reduced. This study aimed to analyze the combustion temperature and efficiency in fluidized-bed combustor (FBC) combustion chambers using palm oil biomass (such as palm kernel shell (PKS), empty fruit bunches (EFBs), and palm oil midrib (OPM)). In addition, the remaining combustion ash from the testing process was analyzed using Gaussian equations. In this study, experiments were conducted through combustion in the FBC space. The results of the experimentation from the combustion process with EFB fuel showed higher efficiency than those with OPM and PKS fuels. The efficiency level of the EFB , OPM, and PKS fuels was 12.77%, 11.05%, and 10.63%, respectively. The analysis results for the highest temperatures were recorded for the EFB fuels, which reached 1045 °C, while the highest temperatures recorded for the PKS and OPM fuels were 875 and 934 °C, respectively, for measurements on measurement point 1 (MP-1). For point of measurement point 2 (MP-2), the highest temperatures recorded for the PKS, OPM, and EFB fuels were 935, 866, and 633 °C. Overall, the tests performed showed that the level of efficiency was in line with the testing process.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".