Simultaneous Fine Particulate Matter Separation and CO2 Adsorption in a Cyclone Separator with a Fixed Bed Bottom Ash from a Palm Oil Mill Boiler: A Simulation Study
Bibliographic record
Abstract
At palm oil mills, a cyclone is an integrated piece of equipment in the boiler with the sole purpose of separating air and particles resulting from the shell and fiber combustion process in the boiler unit.Meanwhile, the CO2 gas emissions produced cannot be reduced simultaneously in the boiler unit.This study aims to minimize the amount of fine particulate matter resulting from the combustion process while reducing CO2 emissions.By modifying the cyclone separator, namely by placing the adsorbent from bottom ash on the cyclone vortex finder, the research was conducted using the Computational Fluid Dynamics Method.This study was carried out by varying the inlet velocity, namely 10; 15; 20; 25; and 30 m/s, and the bed height at the cyclone separator gas outlet is 0; 0.155; 0.310; and 0.460 meters.The RNG model equation k-𝜀, capable of supporting device direction simulation flow, is modified with a mass load of 0.1 kg/s and an operating temperature of 573 K to determine particle collection efficiency, CO2 adsorption percentage, and pressure drop.The results showed that at a bed height of 0.465 m and an inlet velocity of 30 m/s, the cyclone separator achieved the greatest particle collection efficiency of 92.61 percent.At a bed height of 0.465 m and an inlet velocity of 10 m/s, the maximum percentage of CO2 adsorption is 99.61 percent.Cyclone modification by using bottom ash as an adsorbent is able to reduce CO2 emissions and minimize fine particulates simultaneously.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".