Effect of Fuel Gas Composition in Chemical-Looping Combustion with Ni-Based Oxygen Carriers. 2. Fate of Light Hydrocarbons
Bibliographic record
Abstract
Chemical-looping combustion (CLC) has been suggested among the best alternatives to reduce the economic cost of CO 2 capture using fuel gas because CO 2 is inherently separated in the process. Natural gas or refinery gas can be used as gaseous fuels, and they may contain different amounts of light hydrocarbons (LHC). The purpose of this work was to investigate the effect of the presence of light hydrocarbons (C 2 H 6 and C 3 H 8 ) in the feeding gas of a CLC system using a nickel-based oxygen carrier prepared by impregnation on alumina. The reactivity of the oxygen carrier with light hydrocarbons and the combustion efficiency of the process were analyzed in a batch fluidized bed (FB) and a continuous CLC plant. The experiments in the batch FB showed that light hydrocarbons can be fully converted in a CLC process at temperatures above 1173 K. The influence of the fuel reactor temperature (1073−1153 K), solid circulation flow rate (7−14 kg/h), and gas composition was studied in a continuous CLC plant (500 W th ). Neither unburnt hydrocarbons nor carbon formation were detected at any experimental condition. Moreover, there were no agglomeration problems in any case. High energy efficiencies, close to the thermodynamic limit using Ni-based materials, were reached when the oxygen carrier-to-fuel ratio was higher than 3 and the fuel reactor temperature was 1153 K. According to the results found in this work, it was concluded that no special measures should be taken in a CLC process with respect to the presence of LHC in the fuel gas, e.g. refinery gas or crude natural gas.
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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 | 0.001 |
| 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 | 0.001 | 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".