The Effect of the Combustion of Rice Husk with Thai Lignite in a Fixed Bed Reactor on Combustion Characteristics and Pollutant Emissions
Bibliographic record
Abstract
As an agricultural base country, Thailand possesses potentially high quantities of agricultural residues. These can be used together with Thai lignite as co-firing processes. However, the impacts of the utilization of such fuels on the atmosphere should also be evaluated in order to investigate its effect on the environment. The aim of this study is to investigate the combustion characteristic and pollutant emissions of rice husk/lignite mixture in a laboratory scale fixed bed combustor. From the experimental results, it could be concluded that bed temperatures of rice husk/lignite mixture were affected by rice husk mass concentration and over-fired air to total air ratio. The higher the rice husk mass fraction in the fuel mixture was the lower the bed combustion temperature was, due to lower calorific value of fuel mixture. Over-fired air also played a role to control bed temperature resulting from the amount of O2 in the bed during the combustion process. In terms of gaseous emissions, CO was generally decreased as increasing over-fired air supply but not much sensitivity when rice husk fraction increased. SO2 was decreased significantly as increase in rice husk fractions due to a dilution effect of sulphur content in fuel mixture. NOx reduction was insensitive to rice husk fractions. In addition, the emission of NOx was strongly governed by over-fired air to total air ratio. The correlation between CO and Particular Matter was also observed. Mass loading of Particular Matter was minimized with CO, reflecting that Particular Matter was formed by incomplete combustion. High mass loading of entrained particles was also found when under-fired air flow rate was increased.
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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) | 0.001 | 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".