Theoretical-Experimental Investigation of CO Emission From an Oil Refinery Incinerator
Bibliographic record
Abstract
In this paper, we investigate the CO emission from an oil refinery gas incinerator both theoretically and experimentally. At the beginning of this research, our collected data from this incinerator showed that the CO contamination would be far exceeding the permissible environmental standards at the stack exhaust. Therefore, we decided to perform a combined theoretical-experimental study to find a reasonable solution to reduce the CO pollution suitably. Our theoretical study showed that a reliable solution would be to increase the incinerator operating temperature. However, we needed to collect some data from this incinerator to examine if our achieved analytical solution would work correctly. In data collection procedure, we were faced with one major difficulty due to the limits of automatic system of incinerator control, which did not let us increase the incinerator temperature readily in real work conditions. As a general remedy, our suggestion was to interfere in this automatic control system and to increase its maximum possible limit of temperature. Evidently, this needed a number of considerations, which could not be performed in a short length period. As a short length remedy, we designed a number of manual control procedures, which let us examine different temporary working conditions for the incinerator. Trying different operating condition, we eventually found a suitable one with minimum CO emission from the incinerator. Although this choice resulted in an increase in the incinerator temperature and a remedy to reduce the high CO emission, it was inversely increased the incinerator fuel consumption, which is rather a negative point. Our further data collection indicated that the excess air of primary incinerator was relatively high. Therefore, we designed an automatic system of inlet air damper to adjust the inlet air, which resulted in avoiding high excess air and consequently suitable saving in the fuel consumption. The details are provided in the rest of paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".