Oxy-polishing of gas from chemical looping combustion: Fuel-nitrogen transformation and model-aided gas purity optimization
Bibliographic record
Abstract
• Zero-dimensional oxy-polishing reactor model is made and validated with 100 kW data. • The model can extract the detailed reaction path for many gases. • Useful information for oxy-polishing optimization was got with the reactor model. • Coal-biomass mixing combustion was demonstrated with a 100 kW CLC pilot. Chemical looping combustion (CLC) is a carbon capture technology for heat, power and hydrogen production. This work focuses on fuel-nitrogen transformation in fuel reactor and its further conversion in oxy-polishing step of CLC system. A 100 kW CLC pilot equipped with an oxy-polishing chamber (called POC) was used to perform experiment study and a zero-dimensional reactor model combining elementary reaction kinetics was developed and used for oxy-polishing simulation and reaction path analyses. An ilmenite and a manganese ore called Sinaus were used as oxygen carriers, and a coal and a coal-biomass mixture are the fuels in the CLC tests. It was found that in the fuel reactor, part of the fuel-nitrogen was converted to NO and the rest remained as NH 3 which was then oxidized to NO in the POC. The concentrations of HCN and NO 2 were negligible in the fuel reactor and POC. According to the simulation, when the oxygen excess is too low it is difficult to reach 1150–1200 °C which are temperatures needed for oxidizing the unconverted fuel gases. In a reference case, a high conversion was reached when the overall oxygen ratio was above 1.03 and temperature above 925 °C. With a fuel reactor temperature of 950 °C, the oxygen demand needed could be up to around 8 %. Based on the model, optimal geometrical designs of the POC were proposed. With a gas residence time of 3 s in the POC, it was possible to decrease the content of impurities (O 2 , H 2 , CO, NO, CH 4 ) to 3.3 %.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".