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Record W1981588619 · doi:10.1115/fedsm2014-22066

Theoretical-Experimental Investigation of CO Emission From an Oil Refinery Incinerator

2014· article· en· W1981588619 on OpenAlexaff
Masoud Darbandi, Bagher Abrar, Mohsen Khodadadi Yazdi, Mazdak Zeinali, G. E. Schneider

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of Waterloo
FundersSharif University of Technology
KeywordsIncinerationRefineryEnvironmental scienceWaste managementWork (physics)PollutionProcess engineeringComputer scienceEngineeringEnvironmental engineeringMechanical engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.255
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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